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Crime Apps and Social Machines - Crowdsourcing Sensitive Data


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“Real life is and must be full of all kinds of social constraint – the very processes from which “society” arises. Computers help if we use them to create abstract social machines on the Web: processes in which the people do the creative work and the machine does the administration.” Professor Sir Tim Berners-Lee
So, um, what isn’t a social machine?
If almost any combination of human and computing device can be a social machine, how can we start to understand how these work, without being more specific?
How can we make predictions about success factors with such a general description?
Does a social machine have to incorporate a “machine” in the sense that we might think of a computer, or can machine be used in the wider sense, as in some sense of a Turing Machine; a series of computations?
Can social machines actually cope with the “social constraint” – the “processes from which ‘society’ arises”?
Is it possible to use crowdsourcing to “fight crime”, as Luis Von Ahn has suggested?
In order to explore some of these questions, we look at them within the context of Open Crime Data in the U.K..

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Crime Apps and Social Machines - Crowdsourcing Sensitive Data

  1. 1. Crime Apps and Social Machines -Crowdsourcing Sensitive DataMaire Byrne Kieron Thanassis Craig
  2. 2. “Real life is and must be full of all kinds of social constraint – thevery processes from which “society” arises. Computers help ifwe use them to create abstract social machines on the Web:processes in which the people do the creative work and themachine does the administration.” Sir Tim Berners-Lee• So, um, what isn’t a social machine?• If almost any combination of human and computing device can be a socialmachine, how can we start to understand how these work, without beingmore specific?• How can we make predictions about success factors with such a generaldescription?• Does a social machine have to incorporate a “machine” in the sense thatwe might think of a computer, or can machine be used in the wider sense,as in some sense of a Turing Machine; a series of computations?• Can social machines actually cope with the “social constraint” – the“processes from which ‘society’ arises”?• Is it possible to use crowdsourcing to “fight crime”, as Luis Von Ahn hassuggested?• In order to explore some of these questions, we look at them within thecontext of Open Crime Data in the U.K..
  3. 3. • I was looking at crime data from the HomeOffice and found some problems with it.• It seemed that one solution would be tocrowdsource some of the data.• This made me start investigating socialmachines...• And even start thinking about defining them.
  4. 4. Why is trying to define SocialMachines like herding cats?• Berners-Lee referred to the social constraintthat these things might overcome.• But while its possible to build and observethem, specification and nice, hard, predictivescience, are a little more elusive.
  5. 5. The Mystery of the DisappearingCrime Data• The UK Home Office produces crime data, or rather,distributes crime data.• Part of the UK Governments Transparency Program.• Public knowledge of crime - comes from crime data.• Creates desire for action and drives change.• Thus the government is held accountable viatransparency.
  6. 6. • But there are a few issues:• How do we measure crime?• We need to know what crime is, before wecan measure it.• The question of knowledge of crime.• The question of recording crime.• The question of collating the crime data.• The question of producing crime data in atimely fashion.
  7. 7. Which crimes are reported?• Certain types of crime are reported to the police because of insurance.• Police may feel that dominant problems in a neighbourhood are car crimeand burglary.• Sexual assault, domestic violence.• Stalking can be hard to quantify.• When does desire for knowledge of a loved one’s movements becomeprivacy-threatening surveillance?• Victim realisation.• Negative consequences for victims if they report these crimes, not onlyfrom their attacker, but psychologically, morally and socially.• It is hard to quantify and act on these sorts of crime, given normal policereporting mechanisms which are geared around the notion of crime asevent (digital), not a process (analogue).
  8. 8. How are crimes recorded andrepresented on• Each of the 43 police forces has its own reporting procedures andpractices.• The Information Commissioner’s Office (I.C.O.) is risk averse withregard to privacy and the current data protection paradigm• Police data is anonymised and aggregated with little victimconsultation since geolocation is privacy threatening.• Data often only arrives at after a period of 4-7 weeks.• The data indicates trends, but is not up-to-date or accurate.• It cant track crimes.• Descriptive but not predictive of crime.
  9. 9. The Dark Figure• Victim surveys - the British Crime Survey, (B.C.S.) “dark figure” ofunrecorded crime.• Only 15% of sexual assault victims report to the police• Of reported crimes, the conviction rate was around 30%.• 5% of females have been victims of a serious sexual offence sincethey were 16, 20% have been a victim of some sexual offence sincethey were 16• 2.5% of females and 0.4% of males said that they had been a victimof a sexual offence in the previous 12 months• In fact, according to victim surveys, the official data is ALL WRONG!• But policy is built around ‘fear of crime’, a subjective measure, andwhich does not align with official police data.• Do we need to find other ways of creating this data?
  10. 10. Crowdsourced crime data?••••••• (Actually not the last one – it’s a Reductio Ad Absurdum)• Tip lines• Crimestoppers
  11. 11. The Gendankenexperiment• Crowdsourced data• Allows victims control over the process of disclosure• System is analogue, rather than the digital “either-it-is-a-crime-or-it-isn’t” of Police open data.• Might have predictive properties and could even beused to help prevent crime.• Expands older, verified, government open data.• Creates a “grey figure” from more up-to-date lessverified and less formalised data.• Enables trust in the reporting system.
  12. 12. Trust• Trust is a key concept with reporting some crimes.• It is recognised that technical architectures can shaperealities.• A new architecture that re-shapes knowledge andexperience of crime?• Feeds contextualised knowledge about crime with anunderstanding of how current recording systems shape ourknowledge of crime.• And of course, such a social machine changes the dynamicof the current transparency regime where KPIs andperformance data are produced by those who are beingheld to account with the resulting sometimes tragicconsequences.
  13. 13. However - Privacy• How differently might such a machine be used in Europe and Asia?• Privacy– vastly different as we traverse the globe, which such anapp could easily do.• Legal treatments of data that would make a huge social impact ifsomehow incorrectly deployed.• If we have certain expectations of privacy in the U.K. we trust thatour data will not be exposed in a way that reveals our identity.• We must consider not just “the cyber-infrastructure of high-speedsupercomputers and their networked interconnections, but theeven more powerful human interactions enabled by theseunderlying systems.”• Reporting architecture could potentially be horrifically abusive, ifidentities were leaked, lost or let slip.
  14. 14. Trust, privacy,legality and ethics• How such an app stretches existing social understandings andnorms when it’s global.• Do we create global systems that impose global standards orsystems that are flexible enough to allow for local interpretations?• Ushahidi not just lifeblood for solving crime.• Could potentially spill the lifeblood of those using the system.Mexican Drug War:• Anonymity does not depend only on encryption• Criminal organisations, law enforcement, and citizens are notindependent entities.• Apprehensions may not lead to convictions.• Boston bombing – point made that crowdsourced intelligence-gathering might work, but crowdsourced crime-solving doesn’t.
  15. 15. Social Machines & incentives• So there are some problems, and some benefits to such a machine.• We saw reasons or incentives for not reporting: Self-identification,self-blame, guilt , shame, fear of the perpetrator, fear of not beingbelieved, fear of being accused of playing a role in the crime, lack oftrust in the criminal justice system.• In the case of a crowdsourced crime-reporting system theseproblems or incentives for not reporting are overcome.• The crowdsourced reporting system helps in creating a machinethat sources such sensitive data.• Focus on what drives people to use the machine?• What incentives are there?• A user asks for help in some way.
  16. 16. Anonymous Web• Victim discloses as little or as much of what has happened as they choose- turns digital reporting to an analogue process.• But incentive becomes complex here.• To understand the mental state of a victim of domestic abuse is a complexprocess.• As stated above, one of the problems with reporting on domestic abuse isrecognition on the part of a victim that a crime has taken place.• “Knowledge of crime” ebbs and flows in the mind of the victim.• It is this knowledge that maps into knowledge that is to be captured andrepresented by the machine.• When we talk about goals and incentives, we appear to be talking of amental state, goal or intention.• That got me wondering, “How do we map these analogue states ofknowledge of crime from a crime victim into a definition orcharacterisation of a social machine?”• How they fit with the two approaches to characterisation that I looked at?
  17. 17. The top-down approach tospecification• “Computer mediated social interaction” from Robertson andGiunchiglia: “Programming the Social Computer”• Social frameworks provided by humans are so pervasive, given theubiquity of personal devices and sensors.• We must change the way we think about computation andprogramming. A social computation is one for“executable specification exists but the successful implementationof this specification depends upon computer mediated socialinteraction between the human actors in its implementation”.• Considerations of understandings of incentive structures alignedwith the relevant populations allow us to consider knowledgerepresentation and formal specifications in new ways.
  18. 18. Have we captured the “social”?• Evolved machines are underpinned with oftenperverse, unintended human interactions.• The to-and-fro of a victim unsure whether ornot they are a victim.• Is formal specification efficient in trying toisolate predictors for success where the"social" is involved?
  19. 19. The bottom-up, empirical approach• Examples of what are generally agreed to be socialmachines and see what they have in common in termsof their inputs, outputs and computational processes,for example.• Agreed examples of social machines.• Local moral judgements were creeping intospecifications. The ‘bad’ echo-chamber, the ‘bad’spammers, the ‘good’ researchers.• Organisations of person and machine are usedaltruistically or selfishly, by "good" or "bad" people andspeak of “goals” and “intentions”.• Moral vocabulary = seems unscientific.
  20. 20. Genetic variation• Social machines have some elements of non-randomgenetic variation advantageous to characteristics thatenhance survival and reproductive success.• Each user varies in terms of their intentions/ goals as theyuse the machine, and build into it.• By definition, if the machine continues to survive, then thevariation in the minds of its users as they use it or build intoit has led (truistically) to the machine’s survival.• “Selection does not have a long-term goal. It starts anewwith each generation, selecting those characteristics thatare advantageous within the environment at that particulartime.”
  21. 21. Genetic variation,epistemologicalwrangling• Makes looking for characteristics that specify the social in social machines hard.• Depends on the ecological circumstances of their users of whose evolving andmutating intentions we also cannot speak authoritatively.1. Neuroscience casts doubt on whether we can relate intention to behaviour at all.2. Victims of domestic violence do not experience crime as a single, digital, fixed-state event. Their knowledge of their experience of the crime evolves andmutates.3. Devices are more ubiquitous and pervasive. Interactions more intuitive, lessgoal-driven and less conscious. Makes analysing intentions very hard.• Only include devices that people interact with deliberately?• Perhaps mapping intentions as a form of knowledge representation into systemspecifications... ..An act of epistemological wrangling.
  22. 22. Turing Machine?• Is the social machine distinct?• Perhaps not ontologically or epistemologically viable to refer toindividuals’ goals on a large scale, as something that feeds specification.• Outward behaviour.• Intentions may be useful in describing the work of these machines butmay not help define characteristics that enable us to predict whichmachines may go viral.• Look at the overall behaviour of the machine itself as somethingontologically distinct from the inner states of its users.• The machine’s goal can perhaps be specified as something that isemergent; defined via network characteristics of users’ behaviour en-masse.• Network Science aligns itself easily with large–scale phenomena, accountsfor “genetic” variation and can analyse behaviour of those using socialmachines.• Goals mapped out as emergent exogenous behaviours defined vianetwork characteristics.
  23. 23. Some factors• Understanding of network characteristics.• Efficiency.• Omnivorous use of data, sometimes at scale, sometimeshugely aggregated.• Aligning incentives between the social and the machine -depends to some extent on understanding the element ofintention as defined above.• Strong and weak – as in AI?• So is it possible to balance a meaningful discussion ofincentive against the behavioural network scienceapproach advocated above?
  24. 24. Small-scale empirical experiments?• Explore issues around using Network Science in order tomake predictions about success factors?• Interviews and discourse-based methods to understandmore the feelings, and “goals” of victims using such asystem that creates knowledge of crime to offset currentopen crime data or victim survey data?• Could such a system show that crowdsourced data can feeddiscussions about accountability without becoming miredin statistics which can often found to be meaningless oreven dangerous?
  25. 25. Web Science• Social machines mediated by Philosophy,Computer science, Network Science, Psychology,Criminology , Behavioural economics , Sociology.• Policy formation mediated by technologicalstrategists.Can we create new architectures shaping the spaces of crime and crime reporting?Build up society’s knowledge of crime and feed decision-making on crime policy.Discussions on crowdsourcing accountability data to offset statistics generated bythose under scrutiny.Consider the impacts of what such technologies can do?Is there hubris in attempting to define large-scale human phenomena?Goals and intentions in users.In reducing these phenomena to nodes and edges and make predictions aboutsuccess?How can we do justice? Both to crime victims and people who try to define socialmachines? Have social machines really helped with social constraint ?