📄 How Decentralized is the Governance of Blockchain-based Finance
4 chunks · Format: pdf
Priorities Extracted from This Source
#1
Multi-stakeholder governance for AI algorithms and data
#2
Facilitating public dialogue on human-algorithm interaction
#3
Addressing AI opacity, asymmetric information, and algorithmic bias
#4
Promoting human-centered AI and human control
#5
Creating a DAO (GHAIADAO) as a governance mechanism
#6
Building an ontology/knowledge base for DAO design and safe implementation
#7
Designing democratic and resilient DAO voting/governance structures
#8
Using ORCID-based identity to support governance participation
#9
Establishing supporting infrastructure for deliberation and document repositories
#10
Developing economic, legal, and technical foundations before implementation
#11
DAO governance and decentralization
#12
Human-algorithm interaction governance
#13
AI safety, alignment, and existential risk
#14
Fairness, accountability, transparency, and explainability in AI
#15
Internet governance and digital infrastructure governance
#16
Decentralized science and peer review reform
#17
Privacy and digital identity
#18
Societal and labor-market impacts of AI
#19
AI regulation and governance
#20
Algorithmic transparency and interpretability
#21
Fairness, bias, and discrimination in algorithmic systems
#22
Privacy and protection against profiling
#23
Safety, auditing, and accountability for autonomous/AI systems
#24
Governance of algorithmic markets, trading, and decentralized finance
#25
Human-algorithm interaction and human oversight
#26
algorithmic fairness and bias mitigation
#27
human-centered and trustworthy AI
#28
independent oversight, accountability, and ethical governance
#29
internet and platform governance
#30
responsible governance of autonomous systems and robotics
#31
well-being and societal impact assessment of AI
#32
human-algorithm collaboration and interaction design
Document Content
Full text from all 4 processed chunks:
Chunk 0
GOVERNANCE OF A DAO FOR FACILITATING DIALOGUE ON
HUMAN-ALGORITHM INTERACTION AND THE IMPACT OF
EMERGING TECHNOLOGIES ON SOCIETY
APREPRINT
JuliãoBraga∗ FranciscoRegateiro†
CentrodeMatemática,ComputaçãoeCognição InstitutoSuperiorTécnico
UniversidadeFederaldoABC UniversidadedeLisboa
SantoAndré,SP,BR Lisboa,PT
juliao.braga@ufabc.edu.br francisco.regateiro@tecnico.ulisboa.pt
ItanaStiubiener‡ JulianaCristinaBraga§
CentrodeMatemática,ComputaçãoeCognição CentrodeMatemática,ComputaçãoeCognição
UniversidadeFederaldoABC UniversidadeFederaldoABC
SantoAndré,SP,BR SantoAndré,SP,BR
itana.stiubiener@ufabc.edu.br juliana.braga@ufabc.edu.br
June24,2023
ABSTRACT
Human-algorithm interaction is a crucial issue for humanity in light of the impacts of the recent
releaseofChatGPT3and4,amongothers. Theseadvancedchatbotsprovokedaworldwidedebatein
March/2023,whenamanifestosignedbyseveralstakeholderswaspublishedandwidelydiscussedin
themediaandacademia. Thisworkassumesthathuman-algorithminteractionsareinfluencedbya
contextofdiverseinterestsandperspectives,whichaddshighcomplexitytotheproblem. Therefore,
this work proposes a solution to enable the effective participation of stakeholders from different
domainsandsocietyinaconstructivedialogue,usingdigitalplatformsasamedium. Inspiredbythe
successfulgovernanceoftheInternetinfrastructureecosystem,theproposalinvolvesthecreationof
anAutonomousDecentralizedOrganization(DAO)implementedintheblockchainenvironmentof
theEthereumnetwork. However,beforeimplementingtheDAO,itisnecessarytobuildaknowledge
base,thatis,anontology,whichguidesitsdevelopmentinasafeandadequateway. Apreliminary
versionofthisknowledgebasewasmanuallybuiltusingProtégéwithover4,000axioms.
Keywords internet infrastructure · web3 · ontology · machine learning · blockchain · decentralized computing ·
decentralizedscience
1 Introduction
Thispaperpresentsthefirstresultsofaproposaloutlinedinapreviousarticle. Theproposalinvolvedbrainstormingthe
creationofanenvironmentforteachers,researchers,thinkers,andotherstakeholderstogovernartificialintelligence
(AI)algorithmsanddata[1].
Themainmotivationforproposingthecreationofthisenvironmentwasthecomplexityoftheproblem. Regulatingor
establishingrulesforAIalgorithmdevelopersisahugetaskthatrequiresadebateamongadiversegroupofstakeholders.
∗http://lattes.cnpq.br/7092085044582071
†https://fenix.tecnico.ulisboa.pt/homepage/ist13522
‡http://lattes.cnpq.br/4008970012663480
§http://lattes.cnpq.br/7111526592323456
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
Thisisbecauseitinvolvesethical,social,human,technical,privateandpublicpolicyissues,amongmanyotherareasof
humanknowledge. Moreover,itisalongdebatethatrequiresproperorganizationtoensurethepersistenceofthefacts
producedbyamultitudeofstakeholders,whorepresenthumanityasawhole.
Aproblemofsimilarcomplexitycanbeconsidered: theprotocolsthatensuretheproperfunctioninganddynamic
natureoftheInternetovertime(past,present,andfuture). Thisexamplecomesfromtheecosystemsurroundingthe
InternetEngineeringTaskForce(IETF).Thousandsofstakeholdersmeetinpersonthreetimesayearandcontinueto
workpersistentlythroughemailworkinggroupsfortherestoftheyear. Itisalargeandeffectiveorganizationthat
ensuresthefunctioningoftheInternetasweknowittoday.
TheInternet,withitsundeniableimportancetohumanity,justifiestheexistenceofitsownecosystem[2].Governanceof
AIalgorithmsisamorecomplexissuethantheInternet. RecordsshowthatgovernanceofAIalgorithmsisindeedmore
complexthantheInternetbecauseitinvolvesimplicationsthatarenotonlyoffensivebutalsodeadlytohumanbeings.
Ingeneral,regardlessoftheapplication,thesesystemsareconsideredablackbox,resultinginasymmetricinformation
betweentheirdevelopersandtheirconsumers[3]. Oneofthesaddestexampleshighlightingtheconsequenceofthis
asymmetryisthedesignoftheMCAS5systemontheBoeing737MAX,whichledtotwoaccidentswith346deathsin
October2018(LionAir)andMarch2019(EthiopianAirlines). Whentheangleofattacksensorfailed,thebuilt-in
algorithmsforcedtheplanetoloweritsnose,resistingrepeatedattemptsbyconfusedpilotstoturnthenoseup. Ben
Shneiderman,inhisbookHuman-CenteredAI[4],commentsonthetwoBoeing737MAXcrashesandconsidersthat
thefutureoftheseAIalgorithmsishuman-centered. Theyshouldprimarilybecomesupertoolsthatamplifyhuman
abilitiesandempowerpeopleinremarkablewayswhileensuringhumancontrol. BennamedthesealgorithmsHCAI,
anacronymforthetitleofhisbook.
Therearecountlessotherapplications,bothusingAIandnot,thatbehavedisproportionately. Adetaileddescriptionof
theso-calledalgorithmicbiasescanbefoundinSafiyaNoble’sbook,AlgorithmsofOppression,andinothersources
[5][6][7].
Asymmetricinformation,biases,andotherpertinentissuesareconcerningdevelopers,researchers,andotherinterested
partiesastheytrytofigureoutwhatismissing[8].Perspectivesassociatedwithethics[9][10][11][12][13],regulations
[14][15][16][17][18],governance[19][20][21][22][23][3][24]andmanyotherissues[25][26][27][28][29][30]
[31][32][33]areontheagendaofallstakeholdersinsearchofappropriatealternatives. Forexample,theseissuesare
widelydiscussedinShneiderman’sbookHuman-CenteredAI[4]. Thereisextensiveliteratureonthetopicpresentedin
theunpublishedworkthatgaverisetotheapproachesinthisarticle[1].
IfwehadasstrongamotivationastheInternetdoes,withitsimmenseglobalreach,theIETFmodelwithitsbroad
stakeholderparticipation(asseeninFigure1)couldbeasolutionthatwouldcertainlyaddresstheissuesinvolving
algorithmanddatagovernance.
TherearerecentsignsinAIactivitiesthatsuggestagrowingunderstandingofthetechnologybytheworld’spopulation.
ThisisduetotheimmensesuccessofChatGPT,atoolthatispopularizingAIandexpandingitsprominence. Thishas
occurredatanunimaginablespeeddespiteChatGPT’sownlimitationsinnaturallanguageunderstanding,itsrelianceon
trainingdata,andthepossibilityofbiases. InadditiontoChatGPTandothergenerativeAIs,thereisthefactthatthey
canpropagatebiases. GenerativeAIsareinfluencedbytheirtrainingdata,whichcanleadtobiasedordiscriminatory
responses. Thedangerincreaseswiththepossibilityofthetrainingdatabeinginfluencedduringinteractionwithits
users,i.e.,groupswithulteriormotivescaninfluencethetrainingdata. TherefinementofgenerativeAIsisleadingtoan
expansionoftheircapabilities,includingtheuseofmultimodalresourcessuchasChatGPT-46andDALL-E27. Infact,
thecommunityinvolvedbelievesthataftersuchrecentreleases,weareinforasignificantshiftinAI.BillGates,for
example,reactsthroughaseven-chapterdocumententitled“TheAgeofAIhasbegun”[35]. InChapter7,heconcludes
with“TheAgeofAIisfilledwithopportunitiesandresponsibilities.”OtherconcernscomefromneuroscientistMiguel
Nicolelis. Inhismostrecentbook,“Thetruecreatorofeverything: Howthehumanbrainsculptedtheuniverseaswe
knowit”[36],heexposesinthefinaltwochapterstheseriousrisksthathumanitywillfaceinthecomingyearsasa
resultofourincreasinginteractionanddependenceondigitalsystems. Thisestablishesatruesymbiosisthatcandeeply
affectthebrainthroughthephenomenonofneuralplasticity. Basically,almostcontinuouscoexistencewithcomputers
canaffectthewaythebrainworksand,inthelimit,turnusintomeredigitalzombies. Moreover,onecannotforgetthe
storiesthataretoldagainandagainaboutfamousandrecentgenerativeAIs[37].
ThisworkproposesthecreationofaDAOcalledGHAIADAO8 asanoriginalgovernancemechanism. Todevelop
this proposal, the authors created a knowledge base about DAOs, which is available, including its updates, in a
5AcronymforManoeuvringCharacteristicsAugmentationSystem
6https://openai.com/product/gpt-4
7https://openai.com/product/dall-e-2
8https://ghaia.pt
2
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
Figure1: InternetGovernanceEcosystem. Source: [34]
public environment of the Open Science Framework (OSF) [38]. Additionally, this work proposes the creation of
theGHAIADAOtosupportanenvironmentfordebateandregistrationofstakeholderopinionsonissuesinvolving
theregulationofAIalgorithmsanddata,aswellasissuesinvolvinghuman-algorithminteractions. Theseissuesare
widelydiscussedintheCátedraOscarSalaChair9atIEA-USP10,whoseholderisProf. Dr. VirgílioAlmeida(ORCID:
0000-0001-6452-036111).
InadditiontothisIntroductionsection,thispaperdiscussesDAOsandtheirvarietyofgovernanceinSection2. In
Section2.1theknowledgebasebuiltonProtégé,afreeandopen-sourceontologyeditorandframeworkforbuilding
intelligentsystems[39],ispresented. Thissectionalsodisplaysalternativesforusingthisknowledgebase,inparticular,
guidanceontheuseoftheSPARQLlanguageasatoolforontologysearches[40,41]. InSection5,theproposalforthe
creationoftheGHAIADAOispresented,whichincludesanoriginalgovernancemechanism. Section8addressesthe
conclusionsofthisstageoftheworkandrecommendsfutureactivitiestofollow. Finally,thebibliographyisprovided.
2 DAOs
ADecentralizedAutonomousOrganization(DAO)isaformoforganizationbasedonblockchaintechnologythatis
generallygovernedbyitsmembers,whoholdtokens[42]. Tokens,atypeofcryptocurrency(amongothermeanings),
canbeacquiredorreceivedinsomeformbyanyperson. Astheownerofthesetokens,thepersongainstherightto
voteonmattersdirectlyrelatedtothegovernanceoftheDAO.ThegovernancerulesofDAOsarecharacterizedthrough
computerprogramsknownassmartcontracts,whichareexecutedandvalidatedwithintheblockchainoftheEthereum
networkthrougharesourcecalledtheEthereumVirtualMachine(EVM).Thefeaturesofsmartcontracts,suchasa
distributedblockchaindatabase,causetherulesoftheorganizationtobeenforcedbytheverycodethatdefinesthe
DAO,thusmakingitself-governed.
Therefore,DAOsaredifferentfromtraditionalorganizationsbecausetheyareself-governingandfunctionautonomously
inadecentralizedmannerwithouttheneedforintermediaries. Incontrast,traditionalorganizationsaresubjecttorights
andresponsibilitiesdefinedbythelegalsystemoftheenvironmentinwhichtheyoperate.
9https://bit.ly/cos-usp
10http://www.iea.usp.br/
11https://orcid.org/0000-0001-6452-0361
3
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
2.1 GettingtoknowimplementedDAOsindetailthroughontologies
There are many types and functions performed by DAOs. As of April 2023, approximately 150 DAOs have been
implementedontheEthereumnetwork. Duetothediversityofimplementations,itwasdecidedtocreateaknowledge
base(orontology),referredtoasKB,tounderstandandmaintainpermanentinformationaboutalltheDAOs. Tocreate
thisknowledgebase,theProtégésoftwarewasused[39]. Protégé,developedbyStanfordUniversity12,isafreeand
open-sourceontologyeditorandknowledgemanagementsystem.
Twoontologieswerecreatedusingdifferenttechniques. Bothareavailableinthepublicenvironmentoftheprojecton
theOpenScienceFramework(OSF)[38]. Weseparatedfromthesetwo,theonethatbestrepresentedtheexpected
knowledge. Itwasnameddecom.ttl,andisdetailedinFigure2.
Figure2: Featuresofthedecom.owlontology,with4,004axioms
The.ttlextension,namedTurtle,isasyntaxforexpressingthesetofaxiomsrepresentedinOWLinatextfileandis
appropriatefordisplayontheWeb[43].
Todeveloptheontology,itsscopeinthedomainofDAOswasfirstidentified. Thefigures3,4and5characterizethe
maintermsoftheontologyproposed.
Figure3: Acronymsthatwillappearinthedevelopmentoftheproposedontology.
TheontologyresultingfromthesestudiesisavailableontheGHAIADAOwebsite13.
3 HowtoSearchtheKnowledgeBase
OncetheKBiscompletewithasubstantialnumberofaxioms,themainhumaninterestturnstosearchingtheKB.
Onetoolforthisisthewell-knownSPARQLProtocolandRDFQueryLanguage(SPARQL)[44][45][46]. Protégé
12https://protege.stanford.edu/
13https://ghaia.pt/kb/decom.ttl
4
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
Figure4: OntologytobeimplementedinProtégé.
Figure5: ComplementtotheOntologyinthefigureabove,identifyingthevarioustoolsforcreatingDAOs.
provides facilities for using SPARQL, as does Apache Jena14. In addition to these two examples, DBpedia15 and
Wikidata16providepublicinterfacestoSPARQL,amongmanyothers. InallthetoolsusedexceptProtégé,theURLs
https://ghaia.pt/kb/decom.owlorhttps://ghaia.pt/kb/decom.ttlareusedastheentrypoint.
InProtégé,SPARQLactsontheloadedontology.
14https://jena.apache.org/tutorials/sparql.html
15http://virtuoso.openlinksw.com/dataspace/doc/dav/wiki/Main/VOSSPARQL
16https://w.wiki/rL
5
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
Protégéoffersotherresourcestosearchtheontologyproducedthroughit. Forexample,theOntoGraphprovidesimages
suchastheoneshowninFigure6.
Figure6: GraphicalviewofknowledgebaseDAOs
4 AnAIAlgorithmsandtheirbiases
TheCatedraOscarSalafocusedon“Algorithms,artificialintelligence(AI),robotsandmachinesoperatedbyalgorithms
thatincreasinglymediateoursocial,cultural,economicandpoliticalinteractions”initsHuman-AlgorithmInteractions
Project.
FortheCatedra17,"therearethreebasicmotivationsfortheproposedfocusoftheCatedraOscarSalain2022/23:
1. Therearemanydifferenttypesofalgorithmsinoperationthatplayanincreasingroleinsocietyandinthe
dailyactivitiesofcitizens.
2. Thecomplexityofalgorithmsandsystemsintegratingmultiplealgorithmsisincreasingrapidly. Newmodels
andmassiveamountsofdatamakethesealgorithmsandsystemsopaque,makingitverydifficulttounderstand
theirbehavior.
3. Understandingthesocial,political,andeconomicimpacts,whetherpositiveornegative,isaresearchchallenge.
Prof. Virgilioanticipatedaglobalconcernoveropaquealgorithms,mostlythoseofAI.Thishasproventobeanissue
of high priority for humanity, culminating very recently with a manifesto produced by the Future of Life Institute
regardingconcernsoverChatGPT(ChatGenerativePre-trainedTransformer)[47]. Themanifestoproposesasix-month
moratoriumontrainingitandothersimilaralgorithms,especiallythoseusinglargelanguagemodels[48].
Books,scientificpapers,newspaperarticles,andmanyotherformsofexpressionhavelaidouttheiropinions,concerns,
andrecommendationstocountertheunregulatedinvasionofalgorithmswithbiasesofallkinds. Therehavebeen
seriousoffensesandevencriminaloffenses.However,thetextsbeforethemanifestodescribedinthepreviousparagraph
wereintense.
Inadditiontotheabovedocuments,manyothers,especiallyrecentoneswiththeirreferences,addressthesameissue
[49][50][51][52][53][54][55][56][57][58][59][60][61].
TheproposaloftheCatedraOscarSalaforactivitiesfortheyear2022/2023showeditsimmenseacademicdiversity. In
theopinionoftheauthors,itisasubjectofquitehighcomplexitydeservingspecialattentionandcontinuousdebatewith
theparticipationofalargenumberofstakeholders. Anadequateformofgovernance,copyingtheproposalimplemented
intheIETFbytheInternetSociety(ISOC),issuggested.
17https://bit.ly/cosvirgilioalmeida
6
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
Therefore,basedonISOC’sconcernsabouttheecosystemthatgovernstheInternet,theauthorsproposethecreationof
aDAOnamedGHAIADAO,whichispresentedinthefollowingsections.
5 GHAIADAOConstitution
OneofthemainconcernsofaDAOisitsgovernance,whichmustbeefficientanddemocratic[62][63]. ThefirstDAOs
setouttoadmitthattheirgovernancewasdonebytheirmembers,whoheldthetokens. ADAOmemberwouldhave
anumberofvotesequivalenttothenumberoftokenstheyowned. Astimewenton,itbecameapparentthatthose
interestedincontrollingthevotesandinducingthegovernancetoworkthewaytheywantedcouldeasilydothisby
buyingenoughtokens. Thisbegantohappen,andsuchparticipantswerecalledwhales.
Inanattempttoimprovethegovernanceprocess,avotingschemewasusedinwhicheachparticipantwouldhaveone
vote. Thewhaleswereunrelentingandcreatedphantomparticipantsorusedproxiestoincreasetheirparticipation
power. Anewschemewasadopted,theso-calledquadraticscheme,ensuringthatvotingwouldbedecentralized[64].
Inthisproposal,votingcontinuespermember,buteachmemberreceivesanumberofvotesequaltotwicethenumber
oftheirtokens,whichcanadditionallybeusedinvoting. SupposethatmemberAhas5tokensandmemberBhas
10tokens. Then,Awillhave10votesthattheycanuse5ascreditstovoteononeproposaland5creditstovoteon
anotherproposal. MemberBwillhave20votesandcanuse10creditsforoneproposaland10creditsforanother
proposal. Thememberwhohasmoretokenscanspendtheirvotesonaproposal,butiftheothermemberhasmore
memberssupportingtheirproposal,theycanwinthevote. Quadraticvotingallowsusersto“pay”foradditionalvotes
onagivenproposaltomorestronglyexpresstheirsupportforcertainissues. Thisresultsinvotingoutcomesaligned
withthehighestwillingnesstoparticipate(orpay),ratherthanjusttheoutcomepreferredbythemajority,regardlessof
theintensityofindividualpreferences. ThisquestionaboutdemocracyexercisedinaDAOleadstothefundamental
dilemmasofsocietieswiththeirparadoxesandbehavioroftheindividualswhoconstitutethem,particularlyinthose
wholiveinsocietiesinwhichdemocraticinstitutionsfunction[65].
OtherDAOs,adoptingoneoftheabovecriteria,establishaGovernanceCouncilthatwilllookaftertheirgovernance
forapreviouslyagreedperiod.
6 TheGHAIADAO
DeSci(DecentralizedScience)isarecentmovementthataimstousenewtechnologies,suchasblockchainortheWeb3
environment18,toaddresssomeoftheproblematicpointsofscientificresearch,silos,andbottlenecks. Itisanopen
andglobalalternativetothemodernscientificsystemthatfacesmanychallenges. Itextendstheideaofopenscience,
allowingscientiststoraisefunds,shareexperimentaldata,andgetideas. Oneofthemostinterestingexamplesisthatof
aDeScitotailorpeerreview[66][67][66][68][69][70].
TheGHAIADAOisaDeSciandwilluseORCIDIDforitsgovernance,whichisdescribedinthefollowingsection.
6.1 ORCID
ORCIDstandsforOpenResearcherandContributorIDandisaglobal,non-profit,fee-supportedorganizationofits
memberorganizations. TheyformacommunitybuiltandgovernedbyaBoardofDirectorsrepresentativeofmembers
withbroadstakeholderrepresentation[71].
TheORCIDIDisaunique,persistent,freeidentifierforindividualstousewhileengaginginresearch,scholarship,and
innovationactivities. ORCIDoffersasetofApplicationProgrammingInterfaces(APIs).
AsofearlyApril2023,thestatisticsofORCID19indicatedsomethinglikeninemillionfourhundredandtenresearchers
enrolled,spreadacross56countries. Brazilwasthethirdcountrywiththemostregistrants(361,900),aftertheUnited
States(794,493)andChina(412,925).
6.2 TheGovernanceoftheGHAIADAO
Whenimplemented,theGHAIADAOwillbeaDeScithatprovidesamulti-disciplinarydiscussionenvironmentamong
stakeholdersintheissuessurroundingalgorithmichumaninteraction. Itwillfollow, inpart, theIETFandIRTF20
(InternetResearchTaskForce)discussionmodel. ThefollowingruleswillmodeltheGHAIADAO:
18Notaveryacceptablegenericnamegiventotheblockchain
19https://info.orcid.org/orcid-statistics/
20https://irtf.org
7
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
1. Itwillhavetwotypesoftokens: ORandNOR,bothinitiallywithavalueofone(1)USD.
2. Itsmemberswillbeoftwotypes: thosewhohaveanORCIDandthosewithoutanORCID.
3. StakeholderswithanORCIDwillreceive1ORfreeofcharge. TheORisequivalentto1NOR,whichonthe
implementationdateoftheGAIADAOwillbeequivalenttoone(1)USD.StakeholderswithoutanORCID
needtopurchaseNORsatmarketvaluetobecomemembers.
4. OneORisequivalenttooneNOR.ORsarenottradable,butNORsarefreelytradable.
5. TheholderofanORwillbeentitledtoreceiveaNORtwenty-one(21)monthsafterhavingreceivedtheOR
withoutlosingtherighttovote.
6. TheGHAIADAOtreasurymusthavecollateralequivalenttothevalueofthenumberofNORsdistributedin
ORs. Inotherwords,youcannotdistributeanORwithouthavingtheequivalentcollateralintheGHAIADAO
treasury.
7. ParticipantsholdinganORcanvoteandcanbevotedforatwenty-one(21)memberboard,whichwillhandle
thegovernanceoftheGHAIADAO.
8. ParticipantswhodonotholdanOR,thatis,donothaveanORCID,donothavevotingrightsbutcanbevoted
for.
9. ParticipantswithanORalsovotefortheControlCouncil,consistingofseven(7)memberswhosepurposeis
toensurethattherearenoexcessesonthepartoftheGovernanceCouncil. SeeFigure7.
10. Outsidetheblockchain,theGovernanceBoard,throughTechnicalSupport,willmaintainemaillistsequivalent
totheIETF/IRTFworkinggroups(WGs21)andothersimilaritiestomakeeffectivedebatearoundHuman-
AlgorithmInteractions.
11. The GHAIA DAO Internet environment should host a repository of documents equivalent to IETF RFCs
(RequestforComments)thatwillbedevelopedbyitsmembers.
12. Otherrulesrelatedtothesocialandethicalbehaviorofbothparticipantsshouldbedefined.
13. Technical and Operational Support is composed of technical, administrative and other personnel who are
adequatelyremunerated.
Figure7displaystheproposedgovernancefortheGHAIADAO.
This figure abstracts from implementation details on the Ethereum network and the Internet resources outside the
blockchainthatarenecessarytomeetthegoalsoftheGHAIADAO.
7 RelatedLiterature
Table1referencestheliteratureusedtounderstandthemechanismofalgorithmanddatagovernanceandallowsfora
comparisonoftherecommendedproposals. Mostofthesereferenceswereoriginallycollectedintheproposalprepared
asarequirementofthepreliminaryphaseoftheOscarSalaChairandnotpublished[1].
Thereferencesareclassifiedintosevencategoriesandarenotexhaustiveinthelistpresentedinthisproposal:
(a). Internet: TheseincludereferencesthataddressthetopicofInternetgovernance.
(b). Algorithms: ThesearereferencesthatdisplayAIalgorithmsinvariousapplicationareas.
(c). DAO:ReferencesthataddressDAOsandtherespectivetechniquesonwhichtheyarebuilt(blockchainand
cryptocurrencies).
(d). Economics: Referencesthataddressissuesrelatedtotheeconomicsofalgorithmsandtheirenvironments.
(e). Others: A set of references that describe the involvement of AI algorithms in subjects such as Bots,
Discrimination,SoftwareEngineering,AI,Games,Robotics,andSecurity.
(f). RLiterature: Literaturereviewpapers,includingsystematicreviews.
(g). Social: Papersthatreferencethesocial,ethical,andphilosophicalaspectsofalgorithms.
21WorkGroups
8
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
Figure7: GHAIADAO’sproposedgovernancestructure
8 Conclusionsandfuturework
ThereismuchworktobedonetosetuptheGHAIADAO.Thecurrentunavailabilityofresourceshaspreventedthe
implementationoftheGHAIADAO,butitisconsideredamomentaryhindrance. TheKBofDAOsprovedtobean
appropriatesolutionforlearningaboutDAOswhilesimplifyingtheirpresentation.
Inthefuture,thefollowingtaskswereconsideredmandatory:
i. ORCIDshouldbeinformedoftheintentionstousetheORCIDIDtoidentifyfuturevotingmembersofthe
GHAIADAO.
ii. The economic model of the GHAIA DAO’s operating structure must be formally constructed prior to its
establishment.Thismodel,amongmanyotheroutcomes,mustestimatethesafecollateralforitsinitiation.The
baseparameterforthisformulationatthebeginningofApril2023isthe9,410,000researchersregisteredin
ORCID,plusthecostsinvolvedinmaintainingTechnicalandOperationalSupport.Itishopedthatstakeholders
intheprojectcandeveloppapersinthisdirection.
iii. AftertheeconomicmodelhasbeendefinedandalltheoperatingrulesfortheDAOhavebeenestablished,one
ormoresmartcontractswillbedevelopedtoensuretheself-governanceoftheGHAIADAO.
iv. DAOsarenotyetregulatedinmanycountries, whichcancreatelegaluncertainty. Interestedpartieswith
expertiseinlaw,particularlyinternationallaw,shouldstudythisissue.
v. TheontologycreatedusingProtégéandstoredindecom.ttlmustbeevaluated,checked,andcomparedwith
othersimilarontologiestoensureitsvalidity. However,themanualprocessofbuildinganontologycanbe
time-consumingandexhausting,andmaynotalwaysproduceaccurateresults. Assuch,itisrecommendedto
usesemi-automatictechniquesthatinvolveacombinationofmanualinputandautomatedprocessestodevelop
andupdatetheontology. Thesetechniques,whichareconstantlybeingimproved,generallyinvolvetheuseof
deeplearningandtextcapturefromtheweb[462,463,464,465].
vi. AtextdetailingtheuseofSPARQLoverthetwobasesshouldbedevelopedintutorialformtospreadthework
developedandusefulfortheinterestedcommunity.
vii. AcompanionpaperpresentingthegraphicsproducedextensivelybyProtégéisavailableattheOSFofthe
project22.
22https://bit.ly/daoKBinGraphics
9
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
Table1: Primaryandsecondarystudies. bysubject
# References Classification
1. [72],[73],[74],[75],[76],[77],[78],[79],[80],[81],[82],[83],[84],[85],[86] Internet
2. [87],[88],[89],[90],[91],[92],[93],[94],[95],[96],[97],[98],[99],[100],[101],[102]
3. [103],[104],[105],[106],[107],[108],[109],[110],[111],[112],[113],[114],[115]
4. [116],[117],[118],[119],[120],[121],[122],[123],[124],[125].[126],[127],[128]
5. [129],[130],[131],[132],[133],[134],[135],[136],[137],[138],[139],[140],[141]
6. [142],[143],[144],[145],[146],[147],[148],[149],[150],[151][152],[153],[154] Algorithms
7. [155],[156],[157],[158],[159],[160],[161],[162],[163],[164][165],[166],[167]
8. [168],[169],[170],[171],[172],[173],[174],[175],[176],[177],[178],[179],[180]
9. [181],[182],[183],[184],[185],[186],[187],[188],[189],[190],[191],[192],[193]
10. [194],[195],[196],[197],[198],[199],[200],[201],[202],[203],[204],[205],[206]
11. [207],[208],[209],[210],[211],[212],[213],[214],[215],[216],[217],[218],[219]
12. [220],[221],[222],[223],[224],[225],[226],[227],[228],[229]
13. [230],[231],[232],[233],[234],[235],[236],[237],[238],[239],[240],[241],[242]
DAO
14. [243],[244],[245],[246],[247]
15. [248],[249],[250],[251],[252],[253],[254],[255],[256],[257],[258],[259],[260]
16. [261],[262],[263],[264],[265],[266],[267],[268],[269],[270],[271],[272],[273] Economics
17. [274],[275],[276],[277],[278],[279]
18. [280],[281],[282],[283],[284],[285],[286],[287],[288],[289],[290],[291],[292]
19. [293],[294],[295],[296],[297],[298],[299],[300],[301],[302],[303],[304],[305]
20. [306],[307],[308],[309],[310],[311],[312],[313],[314],[315],[316],[317],[318] Others
21. [319],[320],[321],[322],[323],[324],[325],[326],[327],[328],[329],[330],[331]
22. [332],[333],[334],[335],[336],[337],[338],[339]
23. [340],[341],[342],[343],[344],[345],[346],[347],[348],[349],[350],[351],[352]
24. [353],[354],[355],[356],[357],[358],[359],[360],[361],[362],[363],[364],[365]
25. [366],[367],[368],[369],[370],[371],[372],[373],[374],[375],[376],[377],[378]
26. [379],[380],[381],[382],[383],[384],[385],[386],[387],[388],[389],[390],[391]
Governance
27. [392],[393],[394],[395],[396],[397],[398],[399],[400],[401],[402],[403],[404]
28. [405],[406],[407],[408],[409],[410],[411],[412],[413],[414],[415],[416],[417]
29. [418],[419],[420],[421],[422],[423],[424],[425],[426],[427],[428],[429],[430]
30. [431],[432],[433]
31. [434],[435],[436],[437] RLiterature
32. [438],[439],[440],[441],[442],[443],[444],[445],[446],[447],[448],[449],[450]
Social
33. [451],[452],[453],[454],[455],[456],[457],[458],[459][460],[461]
viii. Forthesuccessoftheproject,itisappropriatetohaveanunlimitedpresenceofstakeholdersfromthemost
variedandimmenseareasofknowledge,includingsocietyingeneral.
ix. ItisexpectedthattheDAOwillhostinterestedpartiesinextendinghuman-algorithminteractionstothecontext
ofalldigitalplatforms.
References
[1] JuliaoBraga,FranciscoRegateiro,ItanaStiubiener,andJulianaCBraga. AproposaltoimproveresearchinAI
algorithmanddatagovernance,Sep2022. Portugueseversion: https://osf.io/xcpsd.
[2] WolfgangKleinwachterandVirgilioAFAlmeida. Theinternetgovernanceecosystemandtherainforest. IEEE
InternetComputing,19(2):64–67,2015.
[3] Urs Gasser and Virgilio A.F. Almeida. A Layered Model for AI Governance. IEEE Internet Computing,
21(6):58–62,2017.
[4] BenShneiderman. Human-CenteredAI. OxfordUniversityPress,2022.
[5] Safya.NobleandFelipeDamorim. AlgoritmosdaOpressão: Comoosmecanismosdebuscareforçamoracismo.
EditoraRuadoSabão,RiodeJaneiro,1edition,2022.
[6] CathyO’Neil,editor. AlgoritmosdeDestruiçãoemMassa. EditoraRuadoSabão,SantoAndrá,SP,2020.
[7] Magaly Prado. Fake news e inteligência artificial: O poder dos algoritmos na guerra da desinformação,
volume1. AlmedinaBrasil,2022.
[8] MMitchellWaldrop. Whatarethelimitsofdeeplearning? ProceedingsoftheNationalAcademyofSciences,
116(4):1074–1077,2019.
10
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[9] KatinaMichael,DianaBowman,MegLetaJones,andRamonaPringle. Robotsandsocio-ethicalimplications
[guesteditorial]. IEEETechnologyandSocietyMagazine,37(1):19–21,2018.
[10] AndreaCensi,KonstantinSlutsky,TichakornWongpiromsarn,DmitryYershov,ScottPendleton,JamesFu,and
EmilioFrazzoli.Liability,ethics,andculture-awarebehaviorspecificationusingrulebooks.In2019International
ConferenceonRoboticsandAutomation(ICRA),pages8536–8542.IEEE,2019.
[11] MichaelKearnsandAaronRoth. Theethicalalgorithm: Thescienceofsociallyawarealgorithmdesign. Oxford
UniversityPress,2019.
[12] FlávioS.CorrêaSilvaandNinaS.T.Hirata. InteligênciaÉtica. ComputaçãoBrasil,7:15–18,2022.
[13] PaolaCantarini. Porumaética. RevistaJuridica,4(71):892–912,2022.
[14] ChristianrMölleandArnaudAmouroux,editors. GoverningtheInternet: FreedomandRegulationintheOSCE
Region. OrganizationforSecurityandCo-operationinEurope(OSCE),2007.
[15] EduardoBismarck. PL21/2020,2020.
[16] EuropeCommission. CoordinatedPlanonArtificialIntelligence2021Review. Technicalreport, European
Commission,Brussels,2021.
[17] MargretheVestagerandThierryBreton. UmaEuropaPreparadaparaaEraDigital: Comissãopropõenovas
regraseaçõesparapromoveraexcelênciaeaconfiançanainteligênciaartificial. Technicalreport,Comissão
Euroeia,2021.
[18] SociedadeBrasileiradeComputação. Éticaeregulaçãonainteligênciaartificial. Technicalreport,Sociedade
BrasileiradeComputação,72022.
[19] SolonBarocas,SophieHood,andMalteZiewitz. GoverningAlgorithms: AProvocationPiece. SSRNElectronic
Journal,pages1–12,2013.
[20] FlorianSaurwein,NataschaJust,andMichaelLatzer. Governanceofalgorithms: Optionsandlimitations. Info,
17(6):35–49,2015.
[21] Danilo Doneda and Virgilio A.F. Almeida. What Is Algorithm Governance? IEEE Internet Computing,
20(4):60–63,2016.
[22] LucasD.Introna. Algorithms,Governance,andGovernmentality: OnGoverningAcademicWriting. Science
TechnologyandHumanValues,41(1):17–49,2016.
[23] Martin Ebers and Marta Cantero Gamito, editors. Algorithmic Governance and Governance of Algorithms:
LegalandEthicalChallenges. Springer,2021.
[24] FernandaBruno. Rastrosdigitaissobaperspectivadateoriaator-rede. RevistaFamecos,19(3):681–704,2012.
[25] AdamD.I.Kramer,JamieE.Guillory,andJeffreyT.Hancock.Experimentalevidenceofmassive-scaleemotional
contagionthroughsocialnetworks. InProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesof
America,volume111,pages8788–8790,2014.
[26] DavidLazer. Theriseofthesocialalgorithm. Science,348(6239):1090–1091,2015.
[27] Matthew O Jackson. The human network: How your social position determines your power, beliefs, and
behaviors. PantheonBooks,2019.
[28] EytanBakshy,SolomonMessing,andLadaAAdamic. Exposuretoideologicallydiversenewsandopinionon
facebook. Science,348(6239):1130–1132,2015.
[29] Jean-FrançoisBonnefon,AzimShariff,andIyadRahwan. Thesocialdilemmaofautonomousvehicles. Science,
352(6293):1573–1576,2016.
[30] DavidMJLazer,MatthewABaum,YochaiBenkler,AdamJBerinsky,KellyMGreenhill,FilippoMenczer,
MiriamJMetzger, BrendanNyhan, GordonPennycook, DavidRothschild, etal. Thescienceoffakenews.
Science,359(6380):1094–1096,2018.
[31] Mengyi Wei and Zhixuan Zhou. Ai ethics issues in real world: Evidence from ai incident database. arXiv
preprintarXiv:2206.07635,2022.
[32] European Data Protection Supervisor (EDPS). Towards a new digital ethics. Technical Report September,
EuropeanOrganization,2015.
[33] AnneMagalydePaulaCanuto. ÉticanoUsodeDadosBiométricos: HisteriaouumaPreocupaçãoCoerente?
ComputaçãoBrasil,7:36–39,2022.
11
Chunk 1
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[34] JuliaoBraga,JefersonCamposNobre,LisandroZambenedettiGranville,andMarceloSantos. ComoProtocolos
InovadoressãoCriadoseAdotadosemEscalaMundial: UmavisãosobreoInternetEngineeringTaskForce
(IETF)eaInfraestruturadaInternet. InTaisySilvaWeberandClaudiaAparecidaMartins,editors,Jornadasde
AtualizacãoemInformática2020,page45.SociedadeBrasileiradeComputacão,Cuiabá,MTBrazil,2020.
Availablein: https://doi.org/10.5753/sbc.5728.3.2.
[35] BillGates. TheAgeofAIhasbegun,2023. Lastaccessed23march2023.
[36] MiguelNicolelis. OVerdadeiroCriadordeTudo: ComooCerebroHumanoMoldouoUniversoTalComoo
Conhecemos. ELSINORE,Brasil,2023.
[37] BrunoGarattoni. GPT-4tentaassumirocontroledeoutrocomputador–edigitar“comoescapar”noGoogle.
SuperInteressante,Publicadoem23mar2023,15h08,2023. Lastaccessed24march2023.
[38] JuliaoBraga,FranciscoRegateiro,andItanaStiubiener. Human-algorithm: Governance,Sep2022. Acessedin
03/09/2022.
[39] MarkAMusen. Theprotégéproject: alookbackandalookforward. AImatters,1(4):4–12,2015.
[40] Steve Harris and Andy Seaborne. SPARQL 1.1 Query Language. W3c working draft, W3C, march 2023.
https://www.w3.org/TR/sparql11-query/.
[41] W3C. SPARQL,March2023. [Online;accessed24-March-2023].
[42] CarlosSantanaandLauraAlbareda. Blockchainandtheemergenceofdecentralizedautonomousorganizations
(daos): Anintegrativemodelandresearchagenda. TechnologicalForecastingandSocialChange,182:1–15,
2022.
[43] Caroline Burle, Bernadette Farias Loscio, and Newton Calegari. Data on the web best practices. W3C
recommendation,W3C,January2017. https://www.w3.org/TR/2017/REC-dwbp-20170131/.
[44] AndySeaborneandStevenHarris. SPARQL1.1querylanguage. W3Crecommendation,W3C,March2013.
https://www.w3.org/TR/2013/REC-sparql11-query-20130321/.
[45] BobDuCharme. LearningSPARQL:queryingandupdatingwithSPARQL1.1. "O’ReillyMedia,Inc.",2013.
[46] JedrzejPotoniec. Learningsparqlqueriesfromexpectedresults. ComputingandInformatics,38(3):679–700,
2019.
[47] OpenAI. Gpt-4technicalreport. arXiv,2023.
[48] FutureofLifeInstitute. Pausegiantaiexperiments: Anopenletter,2023.
[49] EmilyMBender,TimnitGebru,AngelinaMcMillan-Major,andShmargaretShmitchell. OntheDangersof
StochasticParrots: CanLanguageModelsBeTooBig? InProceedingsofthe2021ACMconferenceonfairness,
accountability,andtransparency,pages610–623,2021.
[50] EddGent. Acryptocurrencyforthemassesorauniversalid?: Worldcoinaimstoscanalltheworld’seyeballs.
IEEESpectrum,60(1):42–57,2023.
[51] NickBostrom. Superintelligence: Paths,Dangers,Strategies. OxfordUniversityPress,2016.
[52] BenjaminSBucknallandShiriDori-Hacohen. Currentandnear-termaiasapotentialexistentialriskfactor. In
Proceedingsofthe2022AAAI/ACMConferenceonAI,Ethics,andSociety,pages119–129,July2022.
[53] JosephCarlsmith. Ispower-seekingaianexistentialrisk? arXivpreprintarXiv:2206.13353,2022.
[54] BrianChristian. TheAlignmentProblem: MachineLearningandHumanValues. Norton&Company,2020.
[55] MichaelCohen,MarcusHutter,andMOsborne. Advancedartificialagentsinterveneintheprovisionofreward.
AIMagazine,43(3):282–293,2022.
[56] TEloundouetal. Gptsaregpts: Anearlylookatthelabormarketimpactpotentialoflargelanguagemodels.
arXivpreprintarXiv:2303.10130,2023.
[57] DanHendrycksandMantasMazeika. X-riskanalysisforairesearch. arXivpreprintarXiv:2206.05862,2022.
[58] RichardNgo. Thealignmentproblemfromadeeplearningperspective. arXivpreprintarXiv:2209.00626,2022.
[59] StuartRussell. Humancompatible: Artificialintelligenceandtheproblemofcontrol. Penguin,2019.
[60] MaxTegmark. Life3.0: BeingHumanintheAgeofArtificialIntelligence. Knopf,2017.
[61] LukasWeidingeretal. Ethicalandsocialrisksofharmfromlanguagemodels,2021.
[62] GaliaKondovaandRenatoBarba. Governanceofdecentralizedautonomousorganizations. JournalofModern
AccountingandAuditing,15(8):406–411,2019.
12
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[63] Usman W Chohan. The decentralized autonomous organization and governance issues. Available at SSRN
3082055,pages1–16,2017.
[64] StevenPLalleyandEGlenWeyl. Quadraticvoting: Howmechanismdesigncanradicalizedemocracy. InAEA
PapersandProceedings,volume108,pages33–37,2018.
[65] Paulo Trigo Pereira. Prisioneiro, o amante e as sereias: instituições econômicas, políticas e democracia.
Almedina,2008.
[66] BiancaTrovòandNazzarenoMassari. Ants-review: Aprivacy-orientedprotocolforincentivizedopenpeer
reviewsonethereum. InLectureNotesinComputerScience(includingsubseriesLectureNotesinArtificial
IntelligenceandLectureNotesinBioinformatics),volume12480LNCS,2021.
[67] SarahHamburg. Calltojointhedecentralizedsciencemovement,2021.
[68] RichardWalkerandPascalRochadaSilva. Emergingtrendsinpeerreview-asurvey. FrontiersinNeuroscience,
9,2015.
[69] RichardSmith. Peerreview: Aflawedprocessattheheartofscienceandjournals. JournaloftheRoyalSociety
ofMedicine,99,2006.
[70] RobertE.Gropp,ScottGlisson,StephenGallo,andLisaThompson. Peerreview: Asystemunderstress. In
BioScience,volume67,2017.
[71] ORCID. OpenResearcherandContributorID,2023. https://info.orcid.org/what-is-orcid/.
[72] TomStandage. TheVictorianInternet: Theremarkablestoryofthetelegraphandthenineteenthcentury’sonline
pioneers. PhoenixLondon,1998.
[73] JanetAbbate. InventingtheInternet. MITPress,1999.
[74] Michele Banko and Eric Brill. Scaling to very very large corpora for natural language disambiguation. In
Proceedingsofthe39thannualmeetingoftheAssociationforComputationalLinguistics,pages26–33,2001.
[75] LeeA.BygraveandJonBing. InternetGovernance: InfrastructureandInstitutions. OxfordUniversityPress,
NewYork,2009.
[76] WolfgangKleinwachter.TheHistoryofInternetGovernance.InGoverningtheInternet:FreedomandRegulation
intheOSCERegion,pages41–65.OSCERegion,Vienna,2007.
[77] ChristianMoller. GoverningtheDomainNameSystem: AnIntroductiontoInternetInfrastructure. InGoverning
theInternet: FreedomandRegulationintheOSCERegion,pages29–39.OSCE,Vienna,2007.
[78] DiegoRafaelCanabarroandFlavioRech. AGovernançadaInternet: Definição,DesafiosePerspectivas. In9o
ENCONTRODAABCP,page17,2014.
[79] DiegoRafaelCanabarro. Governançaglobaldainternet: tecnologia,poderedesenvolvimento. Doutoralthesis,
FederalUniversityofRioGrandedoSul,2014.
[80] JovanKurbalija. Anintroductiontointernetgovernance. DiploFoundation,2016.
[81] AlexandreArnsGonzales. QuemGovernaaGovernançadaInternet? UmaanálisedopapeldaInternetsobre
osrumosdositema-mundo. Dissertaçãodemestrado,UniversidadeFederaldoRioGrandedoSul,2016.
[82] FabrícioPasquotBertiniPolidoandLucasCostaDosAnjos,editors. MarcoCivilEGovernançaDaInternet:
DiálogosEntreODomésticoEOGlobal. FaculdadedeDireitodaUFMG,2016.
[83] FabrícioBertiniPasquotPolido,LucasCosdadosAnjos,andLuízaCoutoChavesBrandão,editors. Tecnologias
eConectividade: DireitoePolíticasnaGovernançadasRedes. IRIS,2017.
[84] Lucas Andrade, Juliao Braga, Stefany Pereira, Rafael Roque, and Marcelo Santos. In-Person and
Remote Participation Review at IETF. In Proceeding of CSBC 2018 - V Workshop pre IETF,
page 11, Natal, RN Brazil, July 2018. To be published. Available at: http://braga.net.br/papers/In-
Person%20and%20Remote%20Participation%20Review%20at%20IETF.pdf.
[85] AlexandrePachecodaSilva,AnaPaulaCamelo,DiegoR.Canabarro,andFlavioRechWagner,editors.Estrutura
efuncionamentodainternet: aspectostécnicos,políticoseregulatórios. FGV,2021.
[86] ElizabethMachadoVeloso. LegislaçãosobreInternetnoBrasil. Technicalreport,CamaradosDeputados,2009.
[87] WarrenSMcCullochandWalterPitts. Alogicalcalculusoftheideasimmanentinnervousactivity. Thebulletin
ofmathematicalbiophysics,5(4):115–133,1943.
[88] JürgenSchmidhuber. Deeplearninginneuralnetworks: Anoverview. NeuralNetworks,61:85–117,2015.
[89] JanetAbbate*. L’histoiredel’InternetauprismedesSTS. Letempsdesmédias,18(1):170–180,2012.
13
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[90] MartinAdam,MichaelWessel,andAlexanderBenlian. Ai-basedchatbotsincustomerserviceandtheireffects
onusercompliance. ElectronicMarkets,31(2):427–445,2021.
[91] NadavAharony,WeiPan,CoryIp,InasKhayal,andAlexPentland. Socialfmri: Investigatingandshapingsocial
mechanismsintherealworld. Pervasiveandmobilecomputing,7(6):643–659,2011.
[92] Katti Faceli, Ana Carolina Lorena, João Gama, and André Carlos Ponce de Leon Ferreira de Carvalho.
Inteligênciaartificial: umaabordagemdeaprendizadodemáquina. LTC,RiodeJanerio,RJ,Brasil,2011.
[93] SaarAlonBarkatandMadalinaBusuioc. Human-aiinteractionsinpublicsectordecision-making: Automation
biasandselectiveadherencetoalgorithmicadvice. AcceptedManuscript,2022.
[94] SaleemaAmershi,DanWeld,MihaelaVorvoreanu,AdamFourney,BesmiraNushi,PennyCollisson,JinaSuh,
ShamsiIqbal,PaulNBennett,KoriInkpen,etal. Guidelinesforhuman-aiinteraction. InProceedingsofthe
2019chiconferenceonhumanfactorsincomputingsystems,pages1–13,2019.
[95] SaleemaAmershi,AndrewBegel,ChristianBird,RobertDeLine,HaraldGall,EceKamar,NachiappanNagappan,
BesmiraNushi,andThomasZimmermann. Softwareengineeringformachinelearning: Acasestudy. In2019
IEEE/ACM41stInternationalConferenceonSoftwareEngineering: SoftwareEngineeringinPractice(ICSE-
SEIP),pages291–300.IEEE,2019.
[96] DarioAmodei,ChrisOlah,JacobSteinhardt,PaulChristiano,JohnSchulman,andDanMané.Concreteproblems
inaisafety. arXivpreprintarXiv:1606.06565,2016.
[97] JuliaAngwin,JeffLarson,SuryaMattu,andLaurenKirchner. MachineBias,2016.
[98] GeorgeA.AkerlofandRachelE.Kranton. IdentityEconomics: HowOurIdentitiesShapeOurWork,Wages,
andWell-Being. PrincetonUniversityPress,Princeton,1edition,2010.
[99] KennethCArnold,KrystaChauncey,andKrzysztofZGajos. Predictivetextencouragespredictablewriting. In
Proceedingsofthe25thInternationalConferenceonIntelligentUserInterfaces,pages128–138,2020.
[100] EdmondAwad,SohanDsouza,RichardKim,JonathanSchulz,JosephHenrich,AzimShariff,Jean-François
Bonnefon,andIyadRahwan. Themoralmachineexperiment. Nature,563(7729):59–64,2018.
[101] LisanneBainbridge. Ironiesofautomation. InAnalysis,designandevaluationofman–machinesystems,pages
129–135.Elsevier,1983.
[102] ReubenBinns,MaxVanKleek,MichaelVeale,UlrikLyngs,JunZhao,andNigelShadbolt. ’it’sreducinga
humanbeingtoapercentage’perceptionsofjusticeinalgorithmicdecisions. InProceedingsofthe2018Chi
conferenceonhumanfactorsincomputingsystems,pages1–14,2018.
[103] TolgaBolukbasi,Kai-WeiChang,JamesYZou,VenkateshSaligrama,andAdamTKalai. Manistocomputer
programmer as woman is to homemaker? debiasing word embeddings. Advances in neural information
processingsystems,29,2016.
[104] JoshBongard,VictorZykov,andHodLipson. Resilientmachinesthroughcontinuousself-modeling. Science,
314(5802):1118–1121,2006.
[105] TawfiqAmmari,JofishKaye,JaniceYTsai,andFrankBentley. Music,search,andiot: Howpeople(really)use
voiceassistants. ACMTrans.Comput.Hum.Interact.,26(3):17–1,2019.
[106] NBolstrom. Superintelligence.Paths,dangers,strategies. OxfordUniversityPress,UnitedKingdom,2014.
[107] JeffreyMBradshaw,RobertRHoffman,DavidDWoods,andMatthewJohnson. Thesevendeadlymythsof
"autonomoussystems". IEEEIntelligentSystems,28(3):54–61,2013.
[108] ElizabethBroadbent.Interactionswithrobots:Thetruthswerevealaboutourselves.Annualreviewofpsychology,
68(1):627–652,2017.
[109] ConnorBrooksandDanielSzafir. Visualizationofintendedassistanceforacceptanceofsharedcontrol. In2020
IEEE/RSJInternationalConferenceonIntelligentRobotsandSystems(IROS),pages11425–11430.IEEE,2020.
[110] ConnorBrooksandDanielSzafir. Balancedinformationgatheringandgoal-orientedactionsinsharedautonomy.
In201914thACM/IEEEInternationalConferenceonHuman-RobotInteraction(HRI),pages85–94.IEEE,
2019.
[111] Erik Brynjolfsson and Tom Mitchell. What can machine learning do? Workforce implications. Science,
358(6370):1530–1534,2017.
[112] JoyBuolamwiniandTimnitGebru. Gendershades: Intersectionalaccuracydisparitiesincommercialgender
classification. InConferenceonfairness,accountabilityandtransparency,pages77–91.PMLR,2018.
14
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[113] AylinCaliskan,JoannaJBryson,andArvindNarayanan.Semanticsderivedautomaticallyfromlanguagecorpora
containhuman-likebiases. Science,356(6334):183–186,2017.
[114] RafaelACalvo,DorianPeters,KarinaVold,andRichardMRyan. Supportinghumanautonomyinaisystems:
Aframeworkforethicalenquiry. InEthicsofDigitalWell-Being,pages31–54.Springer,2020.
[115] NeilA.H.Campbell. TheEvolutionofFlightDataAnalysis. InProceedingsofAustralianSocietyofAirSafety
Investigators,pages1–22,2003.
[116] JulianoCappi. Internet,BigDataediscursodeódio: reflexõessobreasdinâmicasdeinteraçãonoTwittereos
novosambientesdedebatepolítico. Doctoralthesis,PontifíciaUniversidadeCatólicadeSãoPaulo,2017.
[117] FelixCarros,JohannaMeurer,DianaLöffler,DavidUnbehaun,SarahMatthies,IngaKoch,RainerWieching,
DaveRandall,MarcHassenzahl,andVolkerWulf. Exploringhuman-robotinteractionwiththeelderly: results
fromaten-weekcasestudyinacarehome. InProceedingsofthe2020CHIConferenceonHumanFactorsin
ComputingSystems,pages1–12,2020.
[118] MilesBrundage, ShaharAvin, JasmineWang, HaydnBelfield, etal. TowardTrustworthyAIDevelopment:
MechanismsforSupportingVerifiableClaims. arXivpreprint,2020.
[119] RadhikaChemuturi,FarshidAmirabdollahian,andKerstinDautenhahn. Adaptivetrainingalgorithmforrobot-
assistedupper-armrehabilitation,applicabletoindividualisedandtherapeutichuman-robotinteraction. Journal
ofNeuroEngineeringandRehabilitation,10(1),2013.
[120] BoChen,ChunshengHua,BoDai,YuqingHe,andJiandaHan. Onlinecontrolprogrammingalgorithmfor
human–robotinteractionsystemwithanovelreal-timehumangesturerecognitionmethod. InternationalJournal
ofAdvancedRoboticSystems,16(4):1–18,2019.
[121] LuCheng,KushRVarshney,andHuanLiu. Sociallyresponsibleaialgorithms: Issues,purposes,andchallenges.
JournalofArtificialIntelligenceResearch,71:1137–1181,2021.
[122] AlexandraChouldechova,DianaBenavides-Prado,OleksandrFialko,andRhemaVaithianathan. Acasestudyof
algorithm-assisteddecisionmakinginchildmaltreatmenthotlinescreeningdecisions. InConferenceonFairness,
AccountabilityandTransparency,pages134–148.PMLR,2018.
[123] PaulF.Christiano,JanLeike,TomB.Brown,MiljanMartic,ShaneLegg,andDarioAmodei.Deepreinforcement
learningfromhumanpreferences. InAdvancesinNeuralInformationProcessingSystems,pages4300–4308,
2017.
[124] JacobWCrandall,MayadaOudah,FatimahIshowo-Oloko,SheriefAbdallah,Jean-FrançoisBonnefon,Manuel
Cebrian, Azim Shariff, Michael A Goodrich, Iyad Rahwan, et al. Cooperating with machines. Nature
communications,9(1):1–12,2018.
textbfThisstudyexaminesalgorithmiccooperationwithhumansandprovidesanexampleofmethodsthatcanbe
usedtostudythebehaviourofhuman–machinehybridsystems.
[125] AntoineCully, JeffClune, DaneshTarapore, andJean-BaptisteMouret. Robotsthatcanadaptlikeanimals.
Nature,521(7553):503–507,2015.
[126] NileshDalvi,PedroDomingos,SumitSanghai,andDeepakVerma. Adversarialclassification. InProceedingsof
thetenthACMSIGKDDinternationalconferenceonKnowledgediscoveryanddatamining,pages99–108,2004.
[127] Rajarshi Das, James E Hanson, Jeffrey O Kephart, and Gerald Tesauro. Agent-human interactions in the
continuous double auction. In International Joint Conference on Artificial Intelligence, volume 17, pages
1169–1178.LawrenceErlbaumAssociatesLtd,2001.
[128] JesseDavisandMarkGoadrich. Therelationshipbetweenprecision-recallandroccurves. InProceedingsofthe
23rdinternationalconferenceonMachinelearning,pages233–240,2006.
[129] BerkeleyJDietvorst, JosephPSimmons, andCadeMassey. Algorithmaversion: peopleerroneouslyavoid
algorithmsafterseeingthemerr. JournalofExperimentalPsychology: General,144(1):114,2015.
[130] DouglasCEngelbartandWilliamKEnglish. Aresearchcenterforaugmentinghumanintellect. InProceedings
oftheDecember9-11,1968,falljointcomputerconference,partI,pages395–410,1968.
[131] JuliaDresselandHanyFarid. Theaccuracy,fairness,andlimitsofpredictingrecidivism. Scienceadvances,
4(1):1–5,2018.
[132] DouglasCEngelbart. Augmentinghumanintellect: Aconceptualframework. MenloPark,CA,page21,1962.
ReprintedinPacker,R.amdKprdam.L;.eds;(2–1).Multimedia: FromWagnertoVirtualReality.NewYork: W.
W.Norton,64-90.
[133] DanielleEnsign,SorelleAFriedler,ScottNeville,CarlosScheidegger,andSureshVenkatasubramanian.Runaway
feedbackloopsinpredictivepolicing. InProceedingsofMachineLearningResearch,pages1–12,2018.
15
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[134] ZivEpstein,BlakeleyHPayne,JudyHanwenShen,AbhimanyuDubey,BjarkeFelbo,MatthewGroh,Nick
Obradovich,ManuelCebrian,andIyadRahwan.Closingtheaiknowledgegap.arXivpreprintarXiv:1803.07233,
2018.
[135] Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian.
Certifyingandremovingdisparateimpact. InProceedingsofthe21thACMSIGKDDInternationalConference
onKnowledgeDiscoveryandDataMining,pages259–268,2015.
[136] TarletonGillespie. TheRelevanceofAlgorithms. InMediatechnologies: Essaysoncommunication,materiality,
andsociety,pages167–194.TheMITPress,2014.
[137] EnricGalceran,AlexanderGCunningham,RyanMEustice,andEdwinOlson. Multipolicydecision-makingfor
autonomousdrivingviachangepoint-basedbehaviorprediction: Theoryandexperiment. AutonomousRobots,
41:1367–1382,2017.
[138] TimnitGebru,JamieMorgenstern,BrianaVecchione,JenniferWortmanVaughan,HannaWallach,HalDaumé
Iii,andKateCrawford. Datasheetsfordatasets. CommunicationsoftheACM,64:86–92,2021.
[139] AlessandroGiusti,JérômeGuzzi,DanCCires¸an,Fang-LinHe,JuanPRodríguez,FlavioFontana,Matthias
Faessler,ChristianForster,JürgenSchmidhuber,GianniDiCaro,etal. Amachinelearningapproachtovisual
perceptionofforesttrailsformobilerobots. IEEERoboticsandAutomationLetters,1:661–667,2015.
[140] VernLGlaser. EnchantedalgorithmsTheQuantificationofOrganizationalDecision-Making. Universityof
SouthernCalifornia,2014.
[141] KurtGrayandDanielMWegner. Feelingrobotsandhumanzombies: Mindperceptionandtheuncannyvalley.
Cognition,125(1):125–130,2012.
[142] VictoriaGroomandCliffordNass. Canrobotsbeteammates?: Benchmarksinhuman–robotteams. Interaction
studies,8(3):483–500,2007.
[143] JaronHarambam, DimitriosBountouridis, MykolaMakhortykh, andJorisVanHoboken. Designingforthe
betterbytakingusersintoaccount: Aqualitativeevaluationofusercontrolmechanismsin(news)recommender
systems. InProceedingsofthe13thACMConferenceonRecommenderSystems,pages69–77,2019.
[144] SophieFreeman,MartinGibbs,andBjørnNansen. ‘don’tmesswithmyalgorithm’: Exploringtherelationship
betweenlistenersandautomatedcurationandrecommendationonmusicstreamingservices. FirstMonday,
2022.
[145] JeffreyHeer. Agencyplusautomation: Designingartificialintelligenceintointeractivesystems. Proceedingsof
theNationalAcademyofSciences,116:1844–1850,2019.
[146] BramHendriks,BerntMeerbeek,StellaBoess,SteffenPauws,andMariekeSonneveld. Robotvacuumcleaner
personalityandbehavior. InternationalJournalofSocialRobotics,3:187–195,2011.
[147] MartinHilbert,SaifuddinAhmed,JaehoCho,BillyLiu,andJonathanLuu. CommunicatingwithAlgorithms: A
TransferEntropyAnalysisofEmotions-basedEscapesfromOnlineEchoChambers. CommunicationMethods
andMeasures,12(4):260–275,2018.
[148] GunterJHitsch,AliHortaçsu,andDanAriely. Matchingandsortinginonlinedating. AmericanEconomic
Review,100(1):130–63,2010.
[149] ShaneeHonig,AlonBartal,YisraelParmet,andTalOron-Gilad. Usingonlinecustomerreviewstoclassify,
predict,andlearnaboutdomesticrobotfailures. arXivpreprintarXiv:2201.03287,2022.
[150] ShaneeHonigandTalOron-Gilad. Understandingandresolvingfailuresinhuman-robotinteraction: Literature
reviewandmodeldevelopment. Frontiersinpsychology,9:861,2018.
[151] XiyangHu,YanHuang,BeibeiLi,andTianLu. UncoveringtheSourceofEvaluationBiasinMicro-Lending. In
ICIS2021Proceedings,volume1.AssociationforComputingMachinery,2021.
[152] Yin-FuHuangandYi-HaoLi. Sentimenttranslationmodelforexpressingpositivesentimentalstatements. In
2019InternationalConferenceonMachineLearningandDataEngineering(iCMLDE),pages79–84.IEEE,
2019.
[153] LillianHung,MarioGregorio,JimMann,ChristineWallsworth,NeilHorne,AnnetteBerndt,CindyLiu,Evan
Woldum,AndyAu-Yeung,andHabibChaudhury. Exploringtheperceptionsofpeoplewithdementiaaboutthe
socialrobotparoinahospitalsetting. Dementia,20:485–504,2021.
[154] NicholasRJennings,LucMoreau,DavidNicholson,SarvapaliRamchurn,StephenRoberts,TomRodden,and
AlexRogers. Human-agentcollectives. CommunicationsoftheACM,57:80–88,2014.
16
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[155] NeilJohnson,GuannanZhao,EricHunsader,HongQi,NicholasJohnson,JingMeng,andBrianTivnan. Abrupt
riseofnewmachineecologybeyondhumanresponsetime. Scientificreports,3(1):1–7,2013.
[156] Ece Kamar, Severin Hacker, and Eric Horvitz. Combining human and machine intelligence in large-scale
crowdsourcing. InAAMAS,volume12,pages467–474,2012.
[157] SooyeonJeong,CynthiaBreazeal,DeirdreLogan,andPeterWeinstock. Huggable: theimpactofembodiment
on promoting socio-emotional interactions for young pediatric inpatients. In Proceedings of the 2018 CHI
ConferenceonHumanFactorsinComputingSystems,pages1–13,2018.
[158] WilliamBKannelandDanielLMcGee. Diabetesandcardiovasculardisease: theframinghamstudy. Jama,
241(19):2035–2038,1979.
[159] Serge Kernbach, Ronald Thenius, Olga Kernbach, and Thomas Schmickl. Re-embodiment of honeybee
aggregationbehaviorinanartificialmicro-roboticsystem. AdaptiveBehavior,17:237–259,2009.
[160] PeterHKahn,NathanGFreier,TakayukiKanda,HiroshiIshiguro,JolinaHRuckert,RachelLSeverson,and
ShaunKKane. Designpatternsforsocialityinhuman-robotinteraction. InProceedingsofthe3rdACM/IEEE
internationalconferenceonHumanrobotinteraction,pages97–104,2008.
[161] HiroakiKitano,MinoruAsada,YasuoKuniyoshi,ItsukiNoda,andEiichiOsawa. Robocup: Therobotworld
cupinitiative. InProceedingsofthefirstinternationalconferenceonAutonomousagents,pages340–347,1997.
[162] JonKleinberg,HimabinduLakkaraju,JureLeskovec,JensLudwig,andSendhilMullainathan. HumanDecisions
andMachinePredictions. TheQuarterlyJournalofEconomics,133(1):237–293,082017.
[163] JacquelineMKoryWestlund,SooyeonJeong,HaeWPark,SamuelRonfard,AradhanaAdhikari,PaulLHarris,
DavidDeSteno,andCynthiaLBreazeal. Flatvs.expressivestorytelling: Youngchildren’slearningandretention
ofasocialrobot’snarrative. Frontiersinhumanneuroscience,11:295,2017.
[164] JohannesKunkel, ClaudiaSchwenger, andJürgenZiegler. NewsViz: DepictingandControllingPreference
ProfilesUsingInteractiveTreemapsinNewsRecommenderSystems. UMAP2020-Proceedingsofthe28th
ACMConferenceonUserModeling,AdaptationandPersonalization,pages126–135,2020.
[165] AngelikiLazaridou,AlexanderPeysakhovich,andMarcoBaroni. Multi-agentcooperationandtheemergenceof
(natural)language. In5thInternationalConferenceonLearningRepresentations,ICLR2017-ConferenceTrack
Proceedings,pages1–11,2017.
[166] Tsung-YiLin,MichaelMaire,SergeBelongie,JamesHays,PietroPerona,DevaRamanan,PiotrDollár,and
CLawrenceZitnick. Microsoftcoco: Commonobjectsincontext. InEuropeanconferenceoncomputervision,
pages740–755.Springer,2014.
[167] MichaelL.Littman,IfeomaAjunwa,GuyBerger,CraigBoutilier,MorganCurrie,FinaleDoshi-Velez,Gillian
Hadfiel, Michael C. Horowitz, Charles Isbell, Hiroaki Kitano, Karen Levy, Terah Lyons, Melanie Mitchell,
JulieShah,StevenSloman,ShannonVallor,andTobyWalsh. GatheringStrength,GatheringStorms: TheOne
HundredYearStudyonArtificialIntelligence(AI100)2021StudyPanelReport. StanfordUniversity,Stanford,
CA,pages1–82,2021.
[168] GustavoLópez,LuisQuesada,andLuisAGuerrero. Alexavs.sirivs.cortanavs.googleassistant: acomparison
ofspeech-basednaturaluserinterfaces. InInternationalconferenceonappliedhumanfactorsandergonomics,
pages241–250.Springer,2017.
[169] Tamara Lorenz, Astrid Weiss, and Sandra Hirche. Synchrony and reciprocity: Key mechanisms for social
companionrobotsintherapyandcare. InternationalJournalofSocialRobotics,8(1):125–143,2016.
[170] IreneLopatovska,KatrinaRink,IanKnight,KieranRaines,KevinCosenza,HarrietWilliams,PerachyaSorsche,
DavidHirsch,QiLi,andAdriannaMartinez. Talktome: Exploringuserinteractionswiththeamazonalexa.
JournalofLibrarianshipandInformationScience,51(4):984–997,2019.
[171] John Markoff. Machines of loving grace: The quest for common ground between humans and robots.
HarperCollinsPublishers,2016.
[172] SeanMcGregor. PreventingRepeatedRealWorldAIFailuresbyCatalogingIncidents:TheAIIncidentDatabase.
35thAAAIConferenceonArtificialIntelligence,AAAI2021,17B:15458–15463,2021.
[173] MichelleNMeyer. Twocheersforcorporateexperimentation: Thea/billusionandthevirtuesofdata-driven
innovation. Colo.Tech.LJ,13:273,2015.
[174] Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena
Spitzer,InioluwaDeborahRaji,andTimnitGebru. Modelcardsformodelreporting. InProceedingsofthe
conferenceonfairness,accountability,andtransparency,pages220–229,2019.
17
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[175] AlexMitrevski,SantoshThoduka,ArgentinaOrtegaSáinz,MaximilianSchöbel,PatrickNagel,PaulGPlöger,
andErwinPrassler. Deployingrobotsineverydayenvironments: Towardsdependableandpracticalrobotic
systems. arXivpreprintarXiv:2206.12719,2022.
[176] BrentMittelstadt, ChrisRussell, andSandraWachter. Explainingexplanationsinai. InProceedingsofthe
conferenceonfairness,accountability,andtransparency,pages279–288,2019.
[177] AlexandraSMueller,IanJReagan,andJessicaBCicchino. Addressingdriverdisengagementandpropersystem
use: Humanfactorsrecommendationsforlevel2drivingautomationdesign. JournalofCognitiveEngineering
andDecisionMaking,15(1):3–27,2021.
[178] SimoneNatale. TobelieveinSiri: AcriticalanalysisofAIvoiceassistants. TechnicalReportMarch,University
ofBremen,2020.
[179] TienTNguyen,Pik-MaiHui,FMaxwellHarper,LorenTerveen,andJosephAKonstan. Exploringthefilter
bubble: theeffectofusingrecommendersystemsoncontentdiversity. InProceedingsofthe23rdinternational
conferenceonWorldwideweb,pages677–686,2014.
[180] AmitKumarPandeyandRodolpheGelin. Amass-producedsociablehumanoidrobot: Pepper: Thefirstmachine
ofitskind. IEEERobotics&AutomationMagazine,25(3):40–48,2018.
[181] HaeWonPark,RinatRosenberg-Kima,MaorRosenberg,GorenGordon,andCynthiaBreazeal. Growinggrowth
mindsetwithasocialrobotpeer.InProceedingsofthe2017ACM/IEEEinternationalconferenceonhuman-robot
interaction,pages137–145,2017.
[182] RikPeeters. Theagencyofalgorithms: Understandinghuman-algorithminteractioninadministrativedecision-
making. InformationPolity,25(4):507–522,2020.
[183] OlaPettersson. Executionmonitoringinrobotics: Asurvey. RoboticsandAutonomousSystems,53(2):73–88,
2005.
[184] YuriyNevmyvaka,YiFeng,andMichaelKearns. Reinforcementlearningforoptimizedtradeexecution. In
Proceedingsofthe23rdinternationalconferenceonMachinelearning,pages673–680,2006.
[185] AntonioPérez,MIsabelGarcía,ManuelNieto,JoséLPedraza,SantiagoRodríguez,andJuanZamorano. Argos:
Anadvancedin-vehicledatarecorderonamassivelysensorizedvehicleforcardriverbehaviorexperimentation.
IEEETransactionsonIntelligentTransportationSystems,11(2):463–473,2010.
[186] MarcoTulioRibeiro,SameerSingh,andCarlosGuestrin. "WhyShouldITrustYou?"ExplainingthePredictions
ofAnyClassifier. NAACL-HLT2016-2016ConferenceoftheNorthAmericanChapteroftheAssociationfor
ComputationalLinguistics: HumanLanguageTechnologies,ProceedingsoftheDemonstrationsSession,pages
97–101,2016.
[187] Angel Rivas-Casado, Rafael Martinez-Tomás, and Antonio Fernández-Caballero. Multi-agent system for
knowledge-basedeventrecognitionandcomposition. ExpertSystems,28(5):488–501,2011.
[188] FlorianRosenbergandSchahramDustdar. Designandimplementationofaservice-orientedbusinessrulesbroker.
InSeventhIEEEInternationalConferenceonE-CommerceTechnologyWorkshops,pages55–63.IEEE,2005.
[189] MichaelRubenstein,AlejandroCornejo,andRadhikaNagpal. Programmableself-assemblyinathousand-robot
swarm. Science,345(6198):795–799,2014.
[190] OlgaRussakovsky,JiaDeng,HaoSu,JonathanKrause,SanjeevSatheesh,SeanMa,ZhihengHuang,Andrej
Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge.
InternationalJournalofComputerVision,115(3):211–252,2015.
[191] Elliot Salisbury, Ece Kamar, and Meredith Ringel Morris. Toward scalable social alt text: Conversational
crowdsourcingasatoolforrefiningvision-to-languagetechnologyfortheblind. InFifthAAAIConferenceon
HumanComputationandCrowdsourcing,2017.
[192] Matthew J Salganik, Peter Sheridan Dodds, and Duncan J Watts. Experimental study of inequality and
unpredictabilityinanartificialculturalmarket. science,311(5762):854–856,2006.
[193] FilippoSantonideSioandJeroenVandenHoven. Meaningfulhumancontroloverautonomoussystems: A
philosophicalaccount. FrontiersinRoboticsandAI,page15,2018.
[194] BenShneiderman. Thelimitsofspeechrecognition. CommunicationsoftheACM,43(9):63–65,2000.
[195] MehdiShanbedi,SaeedZeinaliHeris,AhmadAmiri,SadeghAdyani,MohsenAlizadeh,andMajidBaniadam.
Optimizationofthethermalefficiencyofatwo-phaseclosedthermosyphonusingactivelearningonthehuman
algorithminteraction. NumericalHeatTransfer;PartA:Applications,66(8):947–962,2014.
18
Chunk 2
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[196] BenSheehan,HyunSeungJin,andUdoGottlieb. Customerservicechatbots: Anthropomorphismandadoption.
JournalofBusinessResearch,115:14–24,2020.
[197] DongheeShin. Howdousersinteractwithalgorithmrecommendersystems? Theinteractionofusers,algorithms,
andperformance. ComputersinHumanBehavior,109(May2019):106344,2020.
[198] GeoffreyEHinton,SimonOsindero,andYee-WhyeTeh. Afastlearningalgorithmfordeepbeliefnets. Neural
computation,18(7):1527–1554,2006.
[199] Hirokazu Shirado and Nicholas A Christakis. Locally noisy autonomous agents improve global human
coordinationinnetworkexperiments. Nature,545(7654):370–374,2017.
[200] DavidSilver,JulianSchrittwieser,KarenSimonyan,IoannisAntonoglou,AjaHuang,ArthurGuez,Thomas
Hubert,LucasBaker,MatthewLai,AdrianBolton,etal. Masteringthegameofgowithouthumanknowledge.
Nature,550(7676):354–359,2017.
[201] DavidSilver,AjaHuang,ChrisJMaddison,ArthurGuez,LaurentSifre,GeorgeVanDenDriessche,Julian
Schrittwieser,IoannisAntonoglou,VedaPanneershelvam,MarcLanctot,etal. Masteringthegameofgowith
deepneuralnetworksandtreesearch. nature,529(7587):484–489,2016.
[202] WilkoSchwarting,JavierAlonso-Mora,LiamPauli,SertacKaraman,andDanielaRus. Parallelautonomyin
automatedvehicles: Safemotiongenerationwithminimalintervention. In2017IEEEInternationalConference
onRoboticsandAutomation(ICRA),pages1928–1935.IEEE,2017.
[203] MichelTaïx,DavidFlavigné,andEtienneFerré. Humaninteractionwithmotionplanningalgorithm. Journalof
IntelligentandRoboticSystems: TheoryandApplications,67(3-4):285–306,2012.
[204] AndreasTheodorou,RobertHWortham,andJoannaJBryson. Designingandimplementingtransparencyfor
realtimeinspectionofautonomousrobots. ConnectionScience,29(3):230–241,2017.
[205] AndreaLThomazandCynthiaBreazeal. Teachablerobots: Understandinghumanteachingbehaviortobuild
moreeffectiverobotlearners. ArtificialIntelligence,172(6-7):716–737,2008.
[206] Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, and Andrew H Beck. Deep learning for
identifyingmetastaticbreastcancer. arXivpreprintarXiv:1606.05718,2016.
[207] DavidWatson. Therhetoricandrealityofanthropomorphisminartificialintelligence. MindsandMachines,
29(3):417–440,2019.
[208] AndreasWagner. Robustnessandevolvabilityinlivingsystems. Princetonuniversitypress,2013.
[209] JacquelineMKoryWestlund,HaeWonPark,RandiWilliams,andCynthiaBreazeal.Measuringyoungchildren’s
long-termrelationshipswithsocialrobots. InProceedingsofthe17thACMconferenceoninteractiondesignand
children,pages207–218,2018.
[210] DavidHWolpert. Thelackofaprioridistinctionsbetweenlearningalgorithms. Neuralcomputation,8(7):1341–
1390,1996.
[211] SimonHaykin. Neuralnetworks: acomprehensivefoundation. PrenticeHallPTR,1998.
[212] AlonHalevy,PeterNorvig,andFernandoPereira. Theunreasonableeffectivenessofdata. IEEEIntelligent
Systems,24(2):8–12,2009.
[213] MartinErwig. Onceuponanalgorithm: Howstoriesexplaincomputing. MITPress,2017.
[214] SangseokYouandLionelRobert. Emotionalattachment,performance,andviabilityinteamscollaboratingwith
embodiedphysicalaction(epa)robots. You,S.andRobert,LP(2018).EmotionalAttachment,Performance,and
ViabilityinTeamsCollaboratingwithEmbodiedPhysicalAction(EPA)Robots,JournaloftheAssociationfor
InformationSystems,19(5):377–407,2017.
[215] Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. Learning fair representations. In
Internationalconferenceonmachinelearning,pages325–333.PMLR,2013.
[216] Xiaohua Zeng, Abraham O Fapojuwo, and Robert J Davies. Design and performance evaluation of voice
activatedwirelesshomedevices. IEEETransactionsonConsumerElectronics,52(3):983–989,2006.
[217] JieMZhang,MarkHarman,LeiMa,andYangLiu. Machinelearningtesting: Survey,landscapesandhorizons.
IEEETransactionsonSoftwareEngineering,2020.
[218] EricHorvitz. Principlesofmixed-initiativeuserinterfaces. InProceedingsoftheSIGCHIconferenceonHuman
FactorsinComputingSystems,pages159–166,1999.
[219] VinodNairandGeoffreyEHinton. Rectifiedlinearunitsimproverestrictedboltzmannmachines. InProceedings
ofthe27thinternationalconferenceonmachinelearning(ICML-10),pages807–814,2010.
19
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[220] RichardODuda,PeterEHart,andDavidGStork. Patternclassification. JohnWiley&Sons,2012.
[221] SébastienBubecketal. Convexoptimization: Algorithmsandcomplexity. FoundationsandTrends®inMachine
Learning,8(3-4):231–357,2015.
[222] AurélienGéron. Hands-OnMachineLearningwithScikit-LearningwithKeras&TensorFlow. O’ReillyMedia,
Inc.,Canada,2edition,2019.
[223] FatimaNAl-Aswadi,HuahYongChan,andKengHoonGan. Automaticontologyconstructionfromtext: a
reviewfromshallowtodeeplearningtrend. ArtificialIntelligenceReview,53:3901–3928,2020.
[224] JohnWenskovitch,MichelleZhou,ChristopherCollins,RemcoChang,MichelleDowling,AlexEndert,andKai
Xu. Puttingthe“i”ininteraction: Interactiveinterfacespersonalizedtoindividuals. IEEEComputerGraphics
andApplications,40(3):73–82,2020.
[225] PercyLiang,RishiBommasani,TonyLee,DimitrisTsipras,DilaraSoylu,MichihiroYasunaga,YianZhang,
DeepakNarayanan,YuhuaiWu,AnanyaKumar,etal. Holisticevaluationoflanguagemodels. arXivpreprint
arXiv:2211.09110,2022.
[226] LalitMohanSanagavarapu,VivekIyer,andRaghuReddy. Adeeplearningapproachforontologyenrichment
fromunstructuredtext. arXivpreprintarXiv:2112.08554,2021.
[227] WaqasHaiderBangyal,NajeebUrRehman,AsmaNawaz,KashifNisar,AgAsriAgIbrahim,RabiaShakir,
andDandaBRawat. Constructingdomainontologyforalzheimerdiseaseusingdeeplearningbasedapproach.
Electronics,11(12):1890,2022.
[228] NithinBuduma,NikhilBuduma,andJoePapa. Fundamentalsofdeeplearning. "O’ReillyMedia,Inc.",2022.
[229] JensAlbrecht,SidharthRamachandran,andChristianWinkler. BlueprintsforTextAnalyticsUsingPython.
O’Reilly,2021.
[230] StefanoAngieri,AlbertoGarcía-Martínez,BingyangLiu,ZhiweiYan,ChuangWang,andMarceloBagnulo. An
experimentindistributedinternetaddressmanagementusingblockchains. arXivpreprintarXiv:1807.10528,
2018.
[231] Juliao Braga, Joao Nuno Silva, Patricia Takako Endo, Jessica Ribas, and Nizam Omar. Blockchain to
improve security, knowledge and collaboration inter-agent communication over restrict domains of the
internetinfrastructure,withhumaninteraction. BrazilianJournalofDevelopment,5(7):9013–9029,july2019.
DOI:10.34117/bjdv5n7-103,ISSN2525-8761.
[232] IgorMCoelhoandVitorNCoelho. Neocompilereco: experimentaçaodeconsensoemblockchainecontratos
inteligentes. InAnaisdoVIWorkshopdotestbedFIBRE,pages57–67.SBC,2021.
[233] LaraBonemerRochaFloriani. Smartcontractsnoscontratosempresariais: umestudosobrepossibilidadee
viabilidadeeconômicadesuautilização. EditoraDialética,2021.
[234] AlanE.Kazdin. Thetokeneconomy: AReviewandEvaluation. PlenumPress,2012.
[235] AntonyLewis. TheBasicsofBitcoinsandBlockchains: AnIntroductiontoCryptocurrenciesandtheTechnology
thatPowersThem. Group,MangoPublishing,CoralGables,FL,1edition,2018.
[236] AlexMurray,DennieKim,andJordanCombs. Thepromiseofadecentralizedinternet: Whatisweb3.0and
howcanfirmsprepare? BusinessHorizons,65:565–570,2022.
[237] Ankita Saxena. Workforce Diversity: A Key to Improve Productivity. Procedia Economics and Finance,
11:76–85,2014.
[238] StevenD.Travers. DistributedAutonomousOrganization: ABlockchainOrganizationalArchetype. Strategic
ManagementSociety37thAnnualConference,2017.
[239] Shermin Voshmgir. Economia dos Tokens: Como a Web3 está reinventando a internet e a relação entre os
agenteseconômicos. TokenKitchen,2edition,2020.
[240] AriesWanlinWang. CryptoEconomy: HowBlockchain,CryptocurrencyandToken-EconomyareDisruptingthe
FinancialWorld. SkyhorsePublishing,2018.
[241] Guy R Vishnia and Gareth W Peters. Auditchain: A trading audit platform over blockchain. Frontiers in
Blockchain,3:9,2020.
[242] DenysVirovetsandSergiyObushnyi. Decentralizedautonomousorganizationsasthenewformofeconomic
cooperationindigitalworld. TheUSVAnnalsofEconomicsandPublicAdministration,20(2(32)):41–52,2021.
[243] Wikipédia. Decentralizedautonomousorganization,2021. [Online;accessed12-junho-2023].
20
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[244] YanChenandCristianoBellavitis. Blockchaindisruptionanddecentralizedfinance: Theriseofdecentralized
businessmodels. JournalofBusinessVenturingInsights,13:e00151,2020.
[245] CristianoBellavitis,ChristianFisch,andPaulPMomtaz. Theriseofdecentralizedautonomousorganizations
(daos): afirstempiricalglimpse. VentureCapital,pages1–17,2022.
[246] GailWeinstein,StevenLofchie,JasonSchwartz,FrankFried,ShriverHarris,andJacobsonLLP. APrimeron
DAOs,2022.
[247] BrynlyLlyr,AidenSlavin,andKevinWerbach. DecentralizedAutonomousOrganizationToolkit. Technical
report,WorldEconomicForum,2023.
[248] FredrikYggeandHansAkkermans. Decentralizedmarketsversuscentralcontrol: Acomparativestudy. Journal
ofartificialintelligenceresearch,11:301–333,1999.
[249] W Brian Arthur. Complexity and the economy. In Handbook of Research on Complexity. Edward Elgar
Publishing,2009.
[250] NicolasBouleau. Onexcessivemathematization,symptoms,diagnosisandphilosophicalbasesforrealworld
knowledge. RealWorldEconomics,57:90–105,2011.
[251] Kyle Croman, Christian Decker B, Ittay Eyal, Adem Efe Gencer, Ari Juels, Ahmed Kosba, Andrew Miller,
PrateekSaxena,DawnSong,andRogerWattenhofer. OnScalingDecentralizedBlockchains(APositionPaper).
LectureNotesinComputerScience,9604:106–125,2016.
[252] EricBudish,PeterCramton,andJohnShim. Thehigh-frequencytradingarmsrace: Frequentbatchauctionsasa
marketdesignresponse. TheQuarterlyJournalofEconomics,130(4):1547–1621,2015.
[253] JohnCartlidge,MarcoDeLuca,CharlotteSzostek,andDaveCliff. Toofasttoofurious: fasterfinancial-market
tradingagentscangivelessefficientmarkets. InICAART-2012: 4thInternationalConferenceonAgentsand
ArtificialIntelligence,pages126–135.SciTePress,2012.
[254] Le Chen and Christo Wilson. Observing algorithmic marketplaces in-the-wild. ACM SIGecom Exchanges,
15(2):34–39,2017.
[255] LeChen,AlanMislove,andChristoWilson.Anempiricalanalysisofalgorithmicpricingonamazonmarketplace.
InProceedingsofthe25thinternationalconferenceonWorldWideWeb,pages1339–1349,2016.
[256] JDoyneFarmerandSpyrosSkouras. Anecologicalperspectiveonthefutureofcomputertrading. Quantitative
Finance,13:325–346,2013.
[257] EmilioFerrara,OnurVarol,ClaytonDavis,FilippoMenczer,andAlessandroFlammini. Theriseofsocialbots.
CommunicationsoftheACM,59:96–104,2016.
[258] Michael Kearns, Alex Kulesza, and Yuriy Nevmyvaka. Empirical limitations on high-frequency trading
profitability. TheJournalofTrading,5:50–62,2010.
[259] AndreiAKirilenkoandAndrewWLo.Moore’slawversusmurphy’slaw:Algorithmictradinganditsdiscontents.
JournalofEconomicPerspectives,27(2):51–72,2013.
[260] JonKleinberg,SendhilMullainathan,andManishRaghavan. Inherenttrade-offsinthefairdeterminationofrisk
scores. LeibnizInternationalProceedingsinInformatics,LIPIcs,67:1–23,2017.
[261] JonKleinbergandSigalOren. Mechanismsfor(mis)allocatingscientificcredit. InProceedingsoftheforty-third
annualACMsymposiumonTheoryofcomputing,pages529–538,2011.
[262] Farshad Kooti, Mihajlo Grbovic, Luca Maria Aiello, Nemanja Djuric, Vladan Radosavljevic, and Kristina
Lerman. Analyzinguber’sride-sharingeconomy. InProceedingsofthe26thInternationalConferenceonWorld
WideWebCompanion,pages574–582,2017.
[263] JaronLanier. Youarenotagadget. Vintage,2010.
[264] MichaelLatzer,KatharinaHollnbuchner,NataschaJust,andFlorianSaurwein. Theeconomicsofalgorithmic
selectionontheinternet. InHandbookontheEconomicsoftheInternet,pages395–425.UniversityofZurich,
2014.
[265] AlbertJMenkveld. Theeconomicsofhigh-frequencytrading. AnnualReviewofFinancialEconomics,8:1–24,
2016.
[266] MiriamNaigembe. Banklendingpolicy,creditscoringandthesurvivalofLoans: AcasestudyofbanksXandY.
PhDthesis,MakerereUniversity,2010.
[267] TommyLundgren. Amicroeconomicmodelofcorporatesocialresponsibility. Metroeconomica,62(1):69–95,
2011.
21
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[268] DavidCParkesandMichaelPWellman. Economicreasoningandartificialintelligence. Science,349(6245):267–
272,2015.
[269] FrankPasquale. TheBlackBoxSociety: TheSecretAlgorithmsthatControlMoneyandInformation. Harvard
UniversityPress,2015.
[270] KasperRoszbach. Banklendingpolicy,creditscoring,andthesurvivalofloans. ReviewofEconomicsand
Statistics,86(4):946–958,2004.
[271] JonathanJJMSeddonandWendyLCurrie. Amodelforunpackingbigdataanalyticsinhigh-frequencytrading.
JournalofBusinessResearch,70:300–307,2017.
[272] Robert J Shiller. Narrative economics: How stories go viral and drive major economic events. Princeton
UniversityPress,2020.
[273] Chih-FongTsaiandJhen-WeiWu. Usingneuralnetworkensemblesforbankruptcypredictionandcreditscoring.
Expertsystemswithapplications,34(4):2639–2649,2008.
[274] MichaelP.Wellman,PeterR.Wurman,KevinO’Malley,RoshanBangera,DanielReeves,WilliamEWalsh,
etal. Designingthemarketgameforatradingagentcompetition. IEEEInternetComputing,5(2):43–51,2001.
[275] BernardDafflon.Theassignmentoffunctionstodecentralizedgovernment:fromtheorytopractice.InHandbook
ofmultilevelfinance,pages163–199.EdwardElgarPublishing,2015.
[276] Shuai Wang, Wenwen Ding, Juanjuan Li, Yong Yuan, Liwei Ouyang, and Fei-Yue Wang. Decentralized
autonomousorganizations: Concept, model, andapplications. IEEETransactionsonComputationalSocial
Systems,6(5):870–878,2019.
[277] AlexandreAronne,AurelianoBressan,andHaroldoGuimarãesBrasil. MensuraçãoeGerenciamentodeRiscos
Corporativos: AplicaçõesdeCashFlowatRiskeRealOptions. SaintPaul,2021.
[278] RogérioSilvaNacif. OperaçõesEficientes,EmpresasRentáveis: MelhorandoosResultadosFinanceirospor
MeiodaGestãodeOperações. Aquila,2021.
[279] WenwenDing,JuanjuanLi,RuiQin,RobertKozma,andFei-YueWang. Anewarchitectureandmechanismfor
decentralizedsciencemetamarkets. IEEETransactionsonSystems,Man,andCybernetics: Systems,2023.
[280] CarloAppugliese,PacoNathan,andWilliamSRoberts. AgileAI:APracticalGuidetoBuildingAIApplications
andTeams. O’Reilly,2020.
[281] KennethAppel, WolfgangHaken, andJohnKoch. Everyplanarmapisfourcolorable.partii: Reducibility.
IllinoisJournalofMathematics,21(3):491–567,1977.
[282] KennethAppelandWolfgangHaken. Everyplanarmapisfourcolorable. BulletinoftheAmericanmathematical
Society,82(5):711–712,1976.
[283] PerBak,KanChen,andMichaelCreutz. Self-organizedcriticalityinthegameoflife. Nature,342(6251):780–
782,1989.
[284] MarcGBellemare,YavarNaddaf,JoelVeness,andMichaelBowling. Thearcadelearningenvironment: An
evaluationplatformforgeneralagents. JournalofArtificialIntelligenceResearch,47:253–279,2013.
[285] RogerBemelmans,GertJanGelderblom,PieterJonker,andLucDeWitte. Sociallyassistiverobotsinelderly
care: asystematicreviewintoeffectsandeffectiveness. JournaloftheAmericanMedicalDirectorsAssociation,
13(2):114–120,2012.
[286] AndrewBerdahl,ColinJTorney,ChristosCIoannou,JolyonJFaria,andIainDCouzin. Emergentsensingof
complexenvironmentsbymobileanimalgroups. Science,339(6119):574–576,2013.
[287] AnaBerdasco,GustavoLópez,IgnacioDiaz,LuisQuesada,andLuisAGuerrero. Userexperiencecomparison
ofintelligentpersonalassistants: Alexa,googleassistant,siriandcortana. MultidisciplinaryDigitalPublishing
InstituteProceedings,31(1):51,2019.
[288] AlessandroBessiandEmilioFerrara. Socialbotsdistortthe2016uspresidentialelectiononlinediscussion.
Firstmonday,21(11-7),2016.
[289] MichaelBowling,NeilBurch,MichaelJohanson,andOskariTammelin. Heads-uplimithold’empokerissolved.
CommunicationsoftheACM,60(11):81–88,2017.
[290] MurrayCampbell,AJosephHoaneJr,andFeng-hsiungHsu. Deepblue. Artificialintelligence,134(1-2):57–83,
2002.
[291] JuanMiguelCarrascosa,JakubMikians,RubenCuevas,VijayErramilli,andNikolaosLaoutaris. Ialwaysfeel
like somebody’s watching me: measuring online behavioural advertising. In Proceedings of the 11th ACM
ConferenceonEmergingNetworkingExperimentsandTechnologies,pages1–13,2015.
22
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[292] MauriceChiodoandTobyClifton. Theimportanceofethicsinmathematics. EuropeanMathematicalSociety
Magazine,114:34–37,2019.
[293] BACEN. LIFTChallenge,2022. Acessedin03/09/2022.
[294] WasifaChowdhury. Employingneuralhierarchicalmodelwithpointergeneratornetworksforabstractivetext
summarization. PhDthesis,SimonFrazerUniversity: SchoolofComputingScience,2019.
[295] BattistaBiggio,IginoCorona,DavideMaiorca,BlaineNelson,NedimŠrndic´,PavelLaskov,GiorgioGiacinto,
andFabioRoli. Evasionattacksagainstmachinelearningattesttime. InJointEuropeanconferenceonmachine
learningandknowledgediscoveryindatabases,pages387–402.Springer,2013.
[296] JackClarkandRayPerrault. IntroductiontotheAIindexreport2022. Technicalreport,StanfordUniversity,
2022.
[297] AmitDatta,MichaelCarlTschantz,andAnupamDatta. Automatedexperimentsonadprivacysettings: Atale
ofopacity,choice,anddiscrimination. arXivpreprintarXiv:1408.6491,2014.
[298] YueDeng,FengBao,YouyongKong,ZhiquanRen,andQionghaiDai. Deepdirectreinforcementlearning
forfinancialsignalrepresentationandtrading. IEEETransactionsonNeuralNetworksandLearningSystems,
28(3):653–664,2016.
[299] PedroDomingos. Themasteralgorithm: Howthequestfortheultimatelearningmachinewillremakeourworld.
BasicBooks,2015.
[300] FinaleDoshi-VelezandBeenKim. Towardsarigorousscienceofinterpretablemachinelearning. arXivpreprint
arXiv:1702.08608,2017.
[301] SebastianElbaumandJohnCMunson. Softwareblackbox: analternativemechanismforfailureanalysis. In
Proceedings11thInternationalSymposiumonSoftwareReliabilityEngineering.ISSRE2000,pages365–376.
IEEE,2000.
[302] GiulianoDaEmpoli. Osengenheirosdocaos. Vestígio,BeloHorizonte,1edition,2019.
[303] AmirGlobersonandSamRoweis. Nightmareattesttime: robustlearningbyfeaturedeletion. InProceedingsof
the23rdinternationalconferenceonMachinelearning,pages353–360,2006.
[304] Dennis R Grossi. Aviation Recorder Overview. In International Symposium On Transportation Recorders,
page12,2006.
[305] JoseHernandez-Orallo. Beyondtheturingtest. JournalofLogic,LanguageandInformation,9(4):447–466,
2000.
[306] John Kay and Mervyn King. Radical uncertainty: Decision-making beyond the numbers. WW Norton &
Company,2020.
[307] HimabinduLakkaraju, EceKamar, RichCaruana, andEricHorvitz. Identifyingunknownunknownsinthe
openworld: Representationsandpoliciesforguidedexploration. InThirty-firstaaaiconferenceonartificial
intelligence,2017.
[308] Tian-ShyugLeeandI-FeiChen. Atwo-stagehybridcreditscoringmodelusingartificialneuralnetworksand
multivariateadaptiveregressionsplines. ExpertSystemswithapplications,28(4):743–752,2005.
[309] Joel Z Leibo, Cyprien de Masson d’Autume, Daniel Zoran, David Amos, Charles Beattie, Keith Anderson,
AntonioGarcíaCastañeda,ManuelSanchez,SimonGreen,AudrunasGruslys,etal. Psychlab: apsychology
laboratoryfordeepreinforcementlearningagents. arXivpreprintarXiv:1801.08116,2018.
[310] NancyG.Leveson. EngineeringaSaferWorld: SystemsThinkingAppliedtoSafety. TheMITPress,2011.
[311] CDianneMartin. Themythoftheawesomethinkingmachine. CommunicationsoftheACM,36(4):120–133,
1993.
[312] Simone Natale et al. Deceitful media: Artificial intelligence and social life after the Turing test. Oxford
UniversityPress,USA,2021.
[313] OlfaNasraouiandPatrickShafto. Human-AlgorithmInteractionBiasesintheBigDataCycle: AMarkovChain
IteratedLearningFramework. arXiv,2016.
[314] RandolphMNesse. Tinbergen’sfourquestions,organized: aresponsetobatesonandlaland. TrendsinEcology
&Evolution,28(12):681–82,2013.
[315] KishorePapineni,SalimRoukos,ToddWard,andWei-JingZhu. Bleu: amethodforautomaticevaluationof
machinetranslation. InProceedingsofthe40thannualmeetingoftheAssociationforComputationalLinguistics,
pages311–318,2002.
23
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[316] JudeaPearlandDanaMackenzie. TheBookofWhy: TheNewScienceofCauseandEffect. BasicBooks,New
York,firstedition,2018.
[317] DavidL.PooleandAlanK.Mackworth. ArtificialIntelligence:foundationsofcomputationalagents. Cambridge
UniversityPress,secondedition,2017.
[318] DavidLPooleandAlanKMackworth. ArtificialIntelligence: foundationsofcomputationalagents. Cambridge
UniversityPress,2010.
[319] StuartRusselandPeterNorvig. ArtificialIntelligence. PrenticeHall,NewYork,3edition,2010.
[320] JonathanSchaeffer,NeilBurch,YngviBjornsson,AkihiroKishimoto,MartinMuller,RobertLake,PaulLu,and
SteveSutphen. Checkersissolved. science,317(5844):1518–1522,2007.
[321] GregSiegel. Forensicmedia: Reconstructingaccidentsinacceleratedmodernity. DukeUniversityPress,2014.
[322] RobertSkidelsky. Informationretrievalandhypertext. YaleUniversityPress,2020.
[323] DanielSmilkov,NikhilThorat,BeenKim,FernandaViégas,andMartinWattenberg. Smoothgrad: removing
noisebyaddingnoise. arXivpreprintarXiv:1706.03825,2017.
[324] MeghaSrivastava,HodaHeidari,andAndreasKrause. Mathematicalnotionsvs.humanperceptionoffairness:
Adescriptiveapproachtofairnessformachinelearning. InProceedingsofthe25thACMSIGKDDinternational
conferenceonknowledgediscovery&datamining,pages2459–2468,2019.
[325] VenkatramananSSubrahmanian,AmosAzaria,SkylarDurst,VadimKagan,AramGalstyan,KristinaLerman,
LinhongZhu, EmilioFerrara, AlessandroFlammini, andFilippoMenczer. Thedarpatwitterbotchallenge.
Computer,49(6):38–46,2016.
[326] ChristianSzegedy,WojciechZaremba,IlyaSutskever,JoanBruna,DumitruErhan,IanGoodfellow,andRob
Fergus. Intriguingpropertiesofneuralnetworks. 2ndInternationalConferenceonLearningRepresentations,
ICLR2014-ConferenceTrackProceedings,pages1–10,2014.
[327] FlorianTramèr,AlexeyKurakin,NicolasPapernot,IanGoodfellow,DanBoneh,andPatrickMcDaniel.Ensemble
adversarialtraining: Attacksanddefenses. In6thInternationalConferenceonLearningRepresentations,ICLR
2018-ConferenceTrackProceedings,pages1–22,2018.
[328] MilenaTsvetkova,TahaYasseri,EricT.Meyer,J.BrianPickering,VegardEngen,PaulWalland,MarikaLüders,
AsbjørnFølstad,andGeorgeBravos. UnderstandingHuman-MachineNetworks. ACMComputingSurveys,
50(1):1–35,2018.
[329] MilenaTsvetkova,RuthGarcía-Gavilanes,LucianoFloridi,andTahaYasseri. Evengoodbotsfight: Thecaseof
wikipedia. PloSone,12(2):e0171774,2017.
[330] AlanMTuring. Computingmachineryandintelligence. Mind,LIX(236):433–460,1950.
[331] KoenVanDeSande,TheoGevers,andCeesSnoek. Evaluatingcolordescriptorsforobjectandscenerecognition.
IEEETransactionsonPatternAnalysisandMachineIntelligence,32(9):1582–1596,2009.
[332] CarissaVéliz. Privacyispower. MelvilleHouse,2021.
[333] XingyuXing,WeiMeng,DanDoozan,AlexCSnoeren,NickFeamster,andWenkeLee. Takethispersonally:
Pollutionattacksonpersonalizedservices. In22ndUSENIXSecuritySymposium(USENIXSecurity13),pages
671–686,2013.
[334] YuYaoandEllaAtkins. Thesmartblackbox: Avalue-drivenhigh-bandwidthautomotiveeventdatarecorder.
IEEETransactionsonIntelligentTransportationSystems,22(3):1484–1496,2020.
[335] Quan-shiZhangandSong-ChunZhu. Visualinterpretabilityfordeeplearning:asurvey. FrontiersofInformation
Technology&ElectronicEngineering,19(1):27–39,2018.
[336] ZhimingZhou,HanCai,ShuRong,YuxuanSong,KanRen,WeinanZhang,YongYu,andJunWang. Activation
maximizationgenerativeadversarialnets. arXivpreprintarXiv:1703.02000,2017.
[337] SusmitJha,TuhinSahai,VasumathiRaman,AlessandroPinto,andMichaelFrancis. Explainingaidecisions
usingefficientmethodsforlearningsparsebooleanformulae.JournalofAutomatedReasoning,63(4):1055–1075,
2019.
[338] Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen,
GrushaPrasad,AmanpreetSingh,PratikRingshia,etal. Dynabench: Rethinkingbenchmarkinginnlp. arXiv
preprintarXiv:2104.14337,2021.
[339] PeterMarcDeisenroth,A.AldoFaisal,andChengSoomOng. MATHEMATICSFORMACHINELEARNING.
CambridgeUniversityPress,1edition,2020.
24
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[340] PatríciaGomesRêgodeAlmeida. RegulaçãodaInteligênciaArtificial: AçãoColetivaqueRequerGovernança.
ComputaçãoBrasil,7:23–26,2022.
[341] MargaretBoden,JoannaBryson,DarwinCaldwell,KerstinDautenhahn,LilianEdwards,SarahKember,Paul
Newman, Vivienne Parry, Geoff Pegman, Tom Rodden, Tom Sorrell, Mick Wallis, Blay Whitby, and Alan
Winfield. Principlesofrobotics: regulatingrobotsintherealworld. ConnectionScience,29(2):124–129,2017.
[342] KevinBonsorandNathanChandler. Howblackboxeswork. HowStuffWorks,June,13,2001.
[343] FernandaBragancaandRenataBraga. OsDesafiosdaRegulamentaçãoJurídicadaInteligênciaArtificcialno
Brasil. ComputaçãoBrasil,7:19–22,2022.
[344] LindellBromham,RussellDinnage,andXiaHua. Interdisciplinaryresearchhasconsistentlylowerfunding
success. Nature,534(7609):684–687,2016.
[345] JoannaJBryson,MihailisEDiamantis,andThomasDGrant. Of,for,andbythepeople: thelegallacunaof
syntheticpersons. ArtificialIntelligenceandLaw,25(3):273–291,2017.
[346] RogerClarke. Regulatoryalternativesforai. ComputerLaw&SecurityReview,35(4):398–409,2019.
[347] EuropeCommission. CoordinatedPlanonArtificialIntelligence2021Review. Technicalreport, European
Commission,Brussels,2021.
[348] SamCorbett-Davies,EmmaPierson,AviFeller,SharadGoel,andAzizHuq. Algorithmicdecisionmakingand
thecostoffairness. InProceedingsofthe23rdacmsigkddinternationalconferenceonknowledgediscoveryand
datamining,pages797–806,2017.
[349] IainDCouzin, ChristosCIoannou, GüvenDemirel, ThiloGross, ColinJTorney, AndrewHartnett, Larissa
Conradt,SimonALevin,andNaomiELeonard. Uninformedindividualspromotedemocraticconsensusin
animalgroups. science,334(6062):1578–1580,2011.
[350] KateCrawford,MeredithWhittaker,MadeleineClareElish,SolonBarocas,AaronPlasek,andKadijaFerryman.
Theainowreport.TheSocialandEconomicImplicationsofArtificialIntelligenceTechnologiesintheNear-Term,
2016.
[351] KennethCukier,ViktorMayer-Schönberger,andFrancisdeVéricourt. Framers: Humanadvantageinanageof
technologyandturmoil. Penguin,2022.
[352] HarrisonEdwardsandAmosStorkey. Censoringrepresentationswithanadversary. 4thInternationalConference
onLearningRepresentations,ICLR2016-ConferenceTrackProceedings,pages1–14,2016.
[353] EPI. Algorithmictransparency: Endsecretprofiling. Technicalreport,ElectronicPrivacyInformationCenter,
2015.
[354] VirginiaEubanks. Automatinginequality: Howhigh-techtoolsprofile,police,andpunishthepoor. St.Martin’s
Press,2018.
[355] Gregory Falco, Ben Shneiderman, Julia Badger, Ryan Carrier, Anton Dahbura, David Danks, Martin Eling,
AlwynGoodloe,JerryGupta,ChristopherHart,etal. Governingaisafetythroughindependentaudits. Nature
MachineIntelligence,3(7):566–571,2021.
[356] HanmingFangandAndreaMoro. Theoriesofstatisticaldiscriminationandaffirmativeaction: Asurvey. In
HandbookofSocialEconomics,volume1,pages133–200.ElsevierB.V.,2011.
[357] Jessica Fjeld, Nele Achten, Hannah Hilligoss, Adam Nagy, and Madhulika Srikumar. Principled artificial
intelligence: Mappingconsensusinethicalandrights-basedapproachestoprinciplesforai. BerkmanKlein
CenterResearchPublication,2020-1,2020.
[358] AnaFrazão. DiscriminaçãoAlgoritmica: arelaçãoentrehomensemáquinas. ColunaJota,junho2021. Trabalho
divididoemtrezepartes.
[359] FutureofLiveInstitute. AutonomousWeapons: AnOpenLetterfromAIandRoboticsReserarchers. https:
//futureoflife.org/2016/02/09/open-letter-autonomous-weapons-ai-robotics/
?cn-reloaded=1&cn-reloaded=1,July2018.
[360] IEEE. EthicallyAlignedDesign: Version2-ForPublicDiscussion. IEEEStandards,pages1–263,2017.
[361] CEI. Ourvision. Technicalreport,CouncilonExtendedIntelligence,2022.
[362] LucasD.IntronaandHelenNissenbaum.Shapingtheweb:Whythepoliticsofsearchenginesmatters.Computer
Ethics,pages157–173,2017.
[363] Joosr. AJoosrGuideto...WeaponsofMathDestructionbyCathyO’Neil: HowBigDataIncreasesInequality
andThreatensDemocracy. Broadwaybooks,2016.
25
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[364] DanielKahneman,OlivierSibony,andCRSunstein. Noise. HarperCollinsUK,2022.
[365] DanielKahneman,AMRosenfield,LGandhi,andTBlaser. Noise: Howtoovercomethehigh. HavardBusiness
Review,2016. Availableinhttps://hbr.org/2016/10/noise.
[366] DanielKahneman. Thinking,fastandslow. Macmillan,2011.
[367] Pratyusha Kalluri et al. Don’t ask if artificial intelligence is good or fair, ask how it shifts power. Nature,
583:169–169,2020.
[368] KrishnaM.Kavi. BeyondtheBlackBox. IEESpectrum,47(8):46—-51,2021.
[369] PranavKhadpe,RanjayKrishna,LiFei-Fei,JeffreyTHancock,andMichaelSBernstein. Conceptualmetaphors
impact perceptions of human-ai collaboration. Proceedings of the ACM on Human-Computer Interaction,
4(CSCW2):1–26,2020.
[370] MervynKingandJohnKay. RadicalUncertainty: Decision-makingforanunknowablefuture. HachetteUK,
2020.
[371] NicoleCKrämer,AstridvonderPütten,andSabrinaEimler. Human-agentandhuman-robotinteractiontheory:
Similaritiestoanddifferencesfromhuman-humaninteraction. InHuman-computerinteraction: Theagency
perspective,pages215–240.Springer,2012.
[372] Samantha Krening and Karen M Feigh. Interaction Algorithm Effect on Human Experience. ACMTrans.
Human-RobotInteract,7(2):22,2018.
[373] ArminKrishnan. Killerrobots: legalityandethicalityofautonomousweapons. Routledge,2016.
[374] DavidLazer,RyanKennedy,GaryKing,andAlessandroVespignani. Theparableofgoogleflu: trapsinbigdata
analysis. Science,343(6176):1203–1205,2014.
[375] HeidiLedford. Teamscience. Nature,525(7569):308–311,2015.
[376] JosephCRLicklider. Man-computersymbiosis. IREtransactionsonhumanfactorsinelectronics,pages4–11,
1960.
[377] ClaudiaBauzerMedeiros. Dados,Algoritmos,MáquinasePessoas. ComputaçãoBrasil,7:11–14,2022.
[378] DavidAMindell. Ourrobots,ourselves: Roboticsandthemythsofautonomy. Viking,2015.
[379] SendhilMullainathan. Biasedalgorithmsareeasiertofixthanbiasedpeople. TheNewYorkTimes,2019.
[380] NSCAI.NationalSecurityCommissiononArtificialIntelligence-InterimReport.NationalSecurityCommission
onArtificialIntelligenceReport,pages1–101,2019.
[381] Cathy O’neil. Weapons of math destruction: How big data increases inequality and threatens democracy.
Broadwaybooks,2016.
[382] ZiadObermeyer,BrianPowers,ChristineVogeli,andSendhilMullainathan.Dissectingracialbiasinanalgorithm
usedtomanagethehealthofpopulations. Science,366(6464):447–453,2019.
[383] CristinaGodoyBernardodeOliveira,JoãoPauloCândiaVeiga,andFabioG.Cozman. RegulaçãodaInteligência
Artificial: QualoModeloAdotar. ComputaçãoBrasil,7:28–31,2022.
[384] RajaParasuraman,ThomasBSheridan,andChristopherDWickens. Amodelfortypesandlevelsofhuman
interactionwithautomation. IEEETransactionsonsystems,man,andcybernetics-PartA:SystemsandHumans,
30(3):286–297,2000.
[385] FrankPasquale. Newlawsofrobotics: defendinghumanexpertiseintheageofAI. BelknapPress,2020.
[386] KomalPatel. TestingtheLimitsoftheFirstAmendment:HowaCFAAProhibitiononOnlineAntidiscrimination
TestingInfringesonProtectedSpeechActivity. SSRNElectronicJournal,pages1–46,2017.
[387] WaltLPerry.Predictivepolicing:Theroleofcrimeforecastinginlawenforcementoperations.RandCorporation,
2013.
[388] SundarPichai. AIatGoogle: ourprinciples. 2018-07-07,page1,2018.
[389] TonyJPrescottandJulieMRobillard. Arefriendselectric? thebenefitsandrisksofhuman-robotrelationships.
Iscience,24(1):101993,2021.
[390] IyadRahwan,ManuelCebrian,NickObradovich,JoshBongard,Jean-FrançoisBonnefon,CynthiaBreazeal,
JacobWCrandall,NicholasAChristakis,IainDCouzin,MatthewOJackson,etal. Machinebehaviour. Nature,
568(7753):477–486,2019.
[391] ByronReevesandCliffordNass. Howpeopletreatcomputers,television,andnewmedialikerealpeopleand
places,1996.
26
Chunk 3
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[392] TahiraReidandJamesGibert. Inclusioninhuman–machineinteractions. Science,375(6577):149–150,2022.
[393] LionelPRobert. TheGrowingProblemofHumanizingRobots. InternationalRobotics&AutomationJournal,
3(1):1–2,2017.
[394] MargaretERoberts. Censored. InCensored.PrincetonUniversityPress,2018.
[395] WTeedRockwell. Algorithmsandstories. HumanAffairs,23(4):633–644,2013.
[396] HeatherMRoff. Thestrategicrobotproblem: Lethalautonomousweaponsinwar. JournalofMilitaryEthics,
13(3):211–227,2014.
[397] JárioSantos,IgBittencourt,MarceloReis,GeiserChalco,andSeijiIsotani. Twobillionregisteredstudents
affectedbystereotypededucationalenvironments: ananalysisofgender-basedcolorbias. HUMANITIESAND
SOCIALSCIENCESCOMMUNICATIONS|,9(249):1—-16,2022.
[398] DanielSchiff,AladdinAyesh,LauraMusikanski,andJohnCHavens. Ieee7010: Anewstandardforassessing
thewell-beingimplicationsofartificialintelligence. In2020IEEEinternationalconferenceonsystems,man,
andcybernetics(SMC),pages2746–2753.IEEE,2020.
[399] HelenSharp,YvonneRogers,andJenniferPreece. InteractionDesign: BeyondHuman-ComputerInteraction.
JohnWiley&SonsInc,fifthedition,2019.
[400] BenShneiderman. Designlessonsfromai’stwograndgoals: Humanemulationandusefulapplications. IEEE
TransactionsonTechnologyandSociety,1(2):73–82,2020.
[401] BenShneiderman. Human-centeredartificialintelligence: Reliable,safe&trustworthy. InternationalJournalof
Human–ComputerInteraction,36(6):495–504,2020.
[402] Ben Shneiderman. The dangers of faulty, biased, or malicious algorithms requires independent oversight.
ProceedingsoftheNationalAcademyofSciences,113(48):13538–13540,2016.
[403] Ben Shneiderman, Catherine Plaisant, Maxine S Cohen, Steven Jacobs, Niklas Elmqvist, and Nicholas
Diakopoulos. Designing the user interface: strategies for effective human-computer interaction. Pearson,
2016.
[404] BenShneiderman. Humanresponsibilityforautonomousagents. IEEEintelligentsystems,22(2):60–61,2007.
[405] BenShneidermanandPattieMaes. Directmanipulationvs.interfaceagents. interactions,4(6):42–61,1997.
[406] BenShneiderman. Thefutureofinteractivesystemsandtheemergenceofdirectmanipulation. Behaviour&
InformationTechnology,1(3):237–256,1982.
[407] BenShneiderman. Directmanipulation: Astepbeyondprogramminglanguages. InProceedingsoftheJoint
ConferenceonEasierandMoreProductiveUseofComputerSystems.(Part-II):HumanInterfaceandtheUser
Interface-Volume1981,page143,1981.
[408] MarloSouza. TecnologiasdaLinguagem,ÉticaemIAeRegulamentação. ComputaçãoBrasil,7:32–35,2022.
[409] PeterStone,RodneyBrooks,ErikBrynjolfsson,RyanCalo,OrenEtzioni,GregHager,JuliaHirschberg,Shivaram
Kalyanakrishnan,EceKamar,SaritKraus,KevinLeyton-Brown,DavidParkes,WilliamPress,AnnaLee(Anno)
Saxenian,JulieShah,MilindTambe,andAstroTeller. ArtificialIntelligenceandLifein2030: theonehundred
yearstudyonartificialintelligence. Technicalreport,StanfordUniversity,September2016.
[410] MeganKStrait,CynthiaAguillon,VirginiaContreras,andNoemiGarcia. Thepublic’sperceptionofhumanlike
robots: Onlinesocialcommentaryreflectsanappearance-baseduncannyvalley,ageneralfearofa“technology
takeover”, and the unabashed sexualization of female-gendered robots. In 2017 26th IEEE International
SymposiumonRobotandHumanInteractiveCommunication(RO-MAN),pages1418–1423.IEEE,2017.
[411] Barry Strauch. Ironies of automation: Still unresolved after all these years. IEEE Transactions on Human-
MachineSystems,48(5):419–433,2017.
[412] WenlongSun,OlfaNasraoui,andPatrickShafto. Evolutionandimpactofbiasinhumanandmachinelearning
algorithminteraction,volume15. PublicLibraryofScienceSanFrancisco,CAUSA,2020.
[413] LatanyaSweeney. Discriminationinonlineaddelivery. CommunicationsoftheACM,56(5):44–54,2013.
[414] NikoTinbergen. Onaimsandmethodsofethology. AnimalBiology,55(4):297–321,2005.
[415] UfukTopcu,NadyaBliss,NancyCooke,MissyCummings,AshleyLlorens,HowardShrobe,andLenoreZuck.
Assuredautonomy: Pathtowardlivingwithautonomoussystemswecantrust. arXivpreprintarXiv:2010.14443,
2020.
[416] ZeynepTufekci. Youtube,thegreatradicalizer. TheNewYorkTimes,10(3):2018,2018.
27
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[417] ZeynepTufekci. Engineeringthepublic: Bigdata,surveillanceandcomputationalpolitics. FirstMonday,2014.
[418] UN. ReportoftheWorkingGrouponInternetGovernance. Technicalreport,UnitedNations,2005.
[419] UniversityofMontreal.MontréalDeclarationforaResponsibleDevelopmentofArtificialIntelligence.Technical
report,UniversityofMontreal,2018.
[420] SoroushVosoughi,DebRoy,andSinanAral. Thespreadoftrueandfalsenewsonline. science,359(6380):1146–
1151,2018.
[421] PaulVoosen. Theaidetectives,2017.
[422] SaraWachter-Boettcher. Technicallywrong: Sexistapps,biasedalgorithms,andotherthreatsoftoxictech. WW
Norton&Company,2017.
[423] ChathurikaSWickramasinghe,DanielLMarino,JavierGrandio,andMilosManic. Trustworthyaidevelopment
guidelinesforhumansysteminteraction. In202013thInternationalConferenceonHumanSystemInteraction
(HSI),pages130–136.IEEE,2020.
[424] Christine T. Wolf and Jeanette L. Blomberg. Evaluating the promise of human-algorithm collaborations in
everydayworkpractices. ProceedingsoftheACMonHuman-ComputerInteraction,3(CSCW):1–23,2019.
[425] David D Woods, James Tittle, Magnus Feil, and Axel Roesler. Envisioning human-robot coordination in
futureoperations. IEEETransactionsonSystems,Man,andCybernetics,PartC(ApplicationsandReviews),
34(2):210–218,2004.
[426] WeiXu. Towardhuman-centeredai: aperspectivefromhuman-computerinteraction. interactions,26(4):42–46,
2019.
[427] SergioAmadeuSilveira. Governodosalgoritmos. RevistadePolíticasPúblicas,21(1):267–281,2017.
[428] CanYavuz. MachineBiasArtificialIntelligenceandDiscrimination. PhDthesis,LundUniversity,2019.
[429] ShunyuanZhang,NitinMehta,ParamVirSingh,andKannanSrinivasan. Cananaialgorithmmitigateracial
economicinequality? ananalysisinthecontextofairbnb. AnAnalysisintheContextofAirbnb(January21,
2021).RotmanSchoolofManagementWorkingPaper,2021.
[430] IgnasKalpokas. AlgorithmicGovernance,volume9. PalgraveMacmillan,2019.
[431] MeredithBroussard. Artificialunintelligence: Howcomputersmisunderstandtheworld. MITPress,2018.
[432] CésarAHidalgo,DianaOrghian,JordiAlboCanals,FilipaDeAlmeida,andNataliaMartín. Howhumansjudge
machines. MITPress,2021.
[433] U.S.DepartmentofEducation,OfficeofEducationalTechnology,. ArtificialIntelligenceandFutureofTeaching
andLearning: InsightsandRecommendations. Technicalreport,U.S.DepartmentofEducation,2023.
[434] AlexisLambert,NahalNorouzi,GerdBruder,andGregoryWelch.Asystematicreviewoftenyearsofresearchon
humaninteractionwithsocialrobots. InternationalJournalofHuman–ComputerInteraction,36(19):1804–1817,
2020.
[435] YiMou,ChangqianShi,TianyuShen,andKunXu. Asystematicreviewofthepersonalityofrobot: Mappingits
conceptualization,operationalization,contextualizationandeffects. InternationalJournalofHuman–Computer
Interaction,36(6):591–605,2020.
[436] SarahSebo,BrettStoll,BrianScassellati,andMalteFJung. Robotsingroupsandteams: aliteraturereview.
ProceedingsoftheACMonHuman-ComputerInteraction,4(CSCW2):1–36,2020.
[437] WeiyuWangandKengSiau. Artificialintelligence,machinelearning,automation,robotics,futureofworkand
futureofhumanity: Areviewandresearchagenda. JournalofDatabaseManagement(JDM),30(1):61–79,2019.
[438] LewisMumfordt. TechnicsandCivilization. UniversityofChicagoPress,Chicago,1934.
[439] JosephWeizenbaum. ComputerPowerandHumanReason: FromJudgementtoCalculation. W.H.Freeman
andCompany,1976.
[440] C Dianne Martin. Eniac: press conference that shook the world. IEEE Technology and Society Magazine,
14(4):3–10,1995.
[441] ThomasS.Kuhn. AEstruturadasRevoluçõesCientíficas. Perspectiva,SãoPaulo,1edition,1996.
[442] CigdemBAS¸FIRINCIandZuhalÇ˙IL˙ING˙IR. Anthropomorphismandadvertisingeffectiveness: Moderating
rolesofproductinvolvementandthetypeofconsumerneed. JournalofSocialandAdministrativeSciences,
2(3):108–131,2015.
28
GovernanceofaDAOforFacilitatingDialogueonHuman-AlgorithmInteractionandtheImpactofEmerging
TechnologiesonSociety APREPRINT
[443] Tarleton Gillespie, Pablo J. Boczkowski, and Kirsten A. Foot, editors. Media Technologies: Essays on
Communication,Materiality,andSociety. TheMITPress,2014.
[444] Mark Granovetter and Roland Soong. Threshold models of diffusion and collective behavior. Journal of
Mathematicalsociology,9:165–179,1983.
[445] MarkGranovetter. Thresholdmodelsofcollectivebehavior. Americanjournalofsociology,83:1420–1443,
1978.
[446] JohnABarghandKatelynYAMcKenna. Theinternetandsociallife. Annu.Rev.Psychol.,55:573–590,2004.
[447] David Lazer, Alex Pentland, Lada Adamic, Sinan Aral, Albert-László Barabási, Devon Brewer, Nicholas
Christakis,NoshirContractor,JamesFowler,MyronGutmann,etal. Computationalsocialscience. Science,
323(5915):721–723,2009.
[448] PierreLévy. L’intelligencecollective: pouruneanthropologieducyberespace. Ladécouverte,2013.
[449] AnikóHannák,ClaudiaWagner,DavidGarcia,AlanMislove,MarkusStrohmaier,andChristoWilson. Biasin
onlinefreelancemarketplaces: Evidencefromtaskrabbitandfiverr. InProceedingsofthe2017ACMconference
oncomputersupportedcooperativeworkandsocialcomputing,pages1914–1933,2017.
[450] LeilaHudson,ColinSOwens,andMattFlannes. Dronewarfare: Blowbackfromthenewamericanwayofwar.
MiddleEastPolicy,18:122–132,2011.
[451] PeterMKrafft,MichaelMacy,andAlex"Sandy"Pentland. Botsasvirtualconfederates: designandethics. In
Proceedingsofthe2017ACMConferenceonComputerSupportedCooperativeWorkandSocialComputing,
pages183–190,2017.
[452] BjarkeMønsted,PiotrSapiez˙yn´ski,EmilioFerrara,andSuneLehmann. Evidenceofcomplexcontagionof
informationinsocialmedia: Anexperimentusingtwitterbots. PloSone,12(9):e0184148,2017.
[453] YuhuaLiangandSeungcheolAustinLee. Fearofautonomousrobotsandartificialintelligence: Evidencefrom
nationalrepresentativedatawithprobabilitysampling. InternationalJournalofSocialRobotics,9(3):379–384,
2017.
[454] KathleenRichardson,MarkCoeckelbergh,KutomaWakunuma,ErikBilling,TomZiemke,PabloGomez,Bram
Vanderborght,andTonyBelpaeme. Robotenhancedtherapyforchildrenwithautism(dream): Asocialmodelof
autism. IEEETechnologyandsocietymagazine,37(1):30–39,2018.
[455] Michael P Wellman and Uday Rajan. Ethical issues for autonomous trading agents. Minds and Machines,
27(4):609–624,2017.
[456] Alan FT Winfield and Marina Jirotka. The case for an ethical black box. In Annual Conference Towards
AutonomousRoboticSystems,pages262–273.Springer,2017.
[457] AlanFTWinfieldandMarinaJirotka. Ethicalgovernanceisessentialtobuildingtrustinroboticsandartificial
intelligencesystems.PhilosophicalTransactionsoftheRoyalSocietyA:Mathematical,PhysicalandEngineering
Sciences,376(2133):20180085,2018.
[458] NéstorGarcíaCanclini. Ciudadanosreemplazadosporalgoritmos. Calas,2019.
[459] NeilMcBride. Robotenhancedtherapyforautisticchildren: Anethicalanalysis. IEEETechnologyandSociety
Magazine,39(1):51–60,2020.
[460] MarcWiedermann,EKeithSmith,JobstHeitzig,andJonathanFDonges. Anetwork-basedmicrofoundationof
granovetter’sthresholdmodelforsocialtipping. Scientificreports,10(1):1–10,2020.
[461] ColmKearns,GarySinclair,JackBlack,MarkDoidge,ThomasFletcher,DanielKilvington,KatieListon,Theo
Lynn,andPierangeloRosati. Ascopingreviewofresearchononlinehateandsport. Communication&Sport,
pages1–29,2022.
[462] Adam Farquhar, Richard Fikes, and James Rice. The ontolingua server: A tool for collaborative ontology
construction. Internationaljournalofhuman-computerstudies,46(6):707–727,1997.
[463] OrenEtzioni,MicheleBanko,StephenSoderland,andDanielSWeld. Openinformationextractionfromthe
web. CommunicationsoftheACM,51(12):68–74,2008.
[464] OrenEtzioni,AnthonyFader,JanaraChristensen,StephenSoderland,andMausamMausam. Openinformation
extraction: Thesecondgeneration. InIJCAI,volume11,pages3–10,2011.
[465] AgnieszkaKonysandZygmuntDra˛z˙ek. Ontologylearningapproachestoprovidedomain-specificknowledge
base. ProcediaComputerScience,176:3324–3334,2020.
29