MichaelRovatsosTheUniversityofEdinburgh

UniversityofLeeds,9thJune2016

DiversityinAcConTheSmartSocietyperspecCve

SmartSociety

�  HybridandDiversity-AwareCollecCveAdapCveSystems:“whenpeoplemeetmachinestobuildasmartersociety”

�  4-year€6.8MFETIntegratedProject,co-ordinatedbyUniversityofTrento

�  BringstogetherAI,computerscience,humanfactors,privacy,ethics

ProjectContext�  Hybridity

� VisionofpeopleandmachinescollaboraCngandcomplemenCngeachothertotacklehardsocietalproblems

�  Diversity� DiversepopulaConsofinteracCnghumansandmachineswithdifferentknowledge,skills,objecCves,andexpectaCons

�  Collec1ves� HowdowecomposeindividualinteracConstoobtaincollecCveacConandgloballycoherentsocialcomputaCons?

�  Adapta1on� HowcanweunderstandandsupportcollecCveadaptaConinacomplexsocio-technicalsystems?

Ask&Share�  Anappfordoingthingstogether�  withoutknowingthewhat/who/how

�  Combines�  human-drivencrowdsourcing�  automatedacCvityrecommendaCon

�  Currentscenariotourism,butaimforgeneralmodelofcollecCveproblemsolving

Where’sthenovelty?�  ConvenConalappsrelyon

�  StaCccontentsuppliedapriori�  Search/filtering/recommendaCon�  “Browse&pick”feel

�  LimitaCons�  Limitedscalability&flexibility� Nolearningfromhumaninput� Noexplicituser/placormgoals� NoincenCvisaCon� Notransparency

TheSmartSocietyPlacorm

What’sinthebox(es)Programmingmodel

Accountability&transparency

IncenCves Privacy-awarenessAcCvity&contextrecogniCon

SocialOrchestra1on

SocialorchestraCon

Discovery

ComposiCon

RecommendaCon

NegoCaCon

ExecuCon

Feedback

8

AdapCvesocialorchestraCon

Discovery

ComposiCon

RecommendaCon

NegoCaCon

ExecuCon

Feedback

9

The“agentprotocol”view

task execution

allocationtask

compositiontask

feedback

discoverypeer

po

FAILED AGREE(t)

ADVERTISE(C)

REQUEST(D)

REJECT(t)

FEEDBACK(t, F )

NO SOLUTION

SOLUTIONS(T )

AGREE(t)

START(t)

UPDATE(t)

TaskcomposiCon�  CombinatorialproblemofallocaCnggroupsofuserstosharedtasks,wheretaskrequestscomefromusers

�  HardconstraintsrestrictthegroupingsandtaskproperCesthatcanberealisedinprinciple

�  So>constraintsdeterminewhichcoaliConstructuresandtaskfeaturesarepreferredbysystemand/orusers

�  Examples:� Ridesharingwheredriverssharetheircarswithotherpassengersforaspecificjourney(ourvanillaexample)

� MeeCngscheduling,ciCzensciencetasks,workforceshigallocaCon,clinicalworkflowmanagement,etcetc

DiversityintaskcomposiCon�  IntradiConalmechanismdesign,globalallocaConsarecomputedgivenindividualpreferencesandglobalcriteria�  E.g.socialwelfaremaximisaCon,ParetoopCmality,strategy-proofness,etc.

� MechanismsareproposedthatprovablysaCsfytheseproperCes,soluConcanthereforebeimposedonusers

�  Diversityimpliesthatuserscannotreporttheirpreferences�  Systemnevercapturesallrelevantdecisionvariables�  SoluConscannotbecomputed/consideredexhausCvely� UClityofsoluConscannotbedeterminedbyusersapriori

Keyproblems:1.Howtocompute“opCmal”setsofsoluCons2.Howtoinfluenceusers’choices3.Howtolearnusers’preferences

FromtaskallocaContotaskrecommendaCon

UserandsystemuClity

User's utility function depends on user’s requirements and preferences

Global (system) utility function depends on social welfare and maximising task completion

MIPfirst

MIPothers

MIP*

Learning

Objective

Objective

Objective

Constraints

Constraints

Constraints

Hard feasibility constraints

MIP*

MIPfirst

CompuCngallocaCons

We want to modify users' utility artificially so that their choices lead to a feasible global solution •  Explicit Approaches:

•  intervention •  (possible) future reward

•  Implicit Approaches: •  discounts •  taxation

Influencingusers

MIPfirst

MIPothers

MIP*

MIPfirst

Objective

Constraints

Noiseless and Constant Noise Models

Logit Model (also goes into objective function)

Sponsored Solution

TaxaCon

Learningusers’preferences�  DevelopedamodelofcoarsepreferencesincombinaConwithusertypingtoallowforfastpreferenceelicitaCon

�  ShouldallowustoopCmisequerieswhenrecommendingsoluConsusingexisingac1velearningmethods�  PosequeriesthatalsomaximisetheexpectedvalueofinformaCon,andincludethisintheopCmisaConprocedure

�  Challenge:notsuitableforcollecCves,onlyforsingleusers� BasicallyopCmisaConconstraintswouldhavetoincludefullBaysianupdateofprobabilitydistribuConsonpreferences

Conclusions�  CreaCnglarge-scalecollecCveintelligencethatuCliseshumanandmachinestrengthsrequiresaddressingdiversityatdifferentlevels

� Harnessingthepowerofdiversityinvolves� RelaxingassumpConsthatotherwiseallowustoprovidehardguarantees

� Tacklingnewalgorithmicchallenges(andrepresentaConalonesIhavenotmenConed

Conclusions�  Inreturn,wegetsystemsthatbelermodelabroaderrangeofhumandecisionmaking� IncreaseslikelihoodthatpeoplewillengagewithfuturecollecCveintelligencesystems

�  IhaveomiledmanyotheraspectsofSmartSocietywherediversityalsocreatesnewchallenges� AcCvityrecogniCon,transparency,privacy,etc.

Promise

Surveillance

Manipulation

Collective intelligence

Personalisation

Humans as cheap labour

Peril

Man-machine collaboration

Promisesandperils

Whatmagicaltrickmakesusintelligent?Thetrickisthatthereisnotrick.Thepowerofintelligencestemsfromourvastdiversity,notfromanysingle,perfectprinciple.

-MarvinMinsky