Anita Gurumurthy and Amrita Vasudevan Mar 26, 2018 11:14:35 IST
After days of prodding by the media, Mark Zuckerberg offered a mea culpa, apologising for the “breach of trust between Facebook and the people who share their data with us and expect us to protect it”. The news that Facebook shared user data with a number of organisations including Cambridge Analytica seems to reflect the paradox of surveillance society — that our data is never safe, but share it we must.
So once again, in a Snowdenish moment, we are hit by the revelation that Cambridge Analytica conducted behavioural modeling and psychographic profiling (creating personality profiles by gauging motives, interests, attitudes, beliefs, values, etc) based on data it collected, to successfully (allegedly) target Americans prior to the recent presidential election.
Facebook collects all kinds of social data about its users, like their relationship status, place of work, colleagues, last time they visited their parents, songs they like listening to, as well as other kinds of information such as device data, websites visited from the platform, etc. This may be information that is shared by the user or what their friends may share about them on the platform.
Data that is collected is used to draw up the profile of users — a detailed picture of the persona that emerges by piecing together known activity and aptitude and generating predictions about possible proclivities and predispositions. The mechanics of big data thus recreate the sum total of user traits and attributes — without necessarily verifying them per user.
What follows then is the clustering of users into hyper segments with similar attributes for micro-targeting ads. You may merely be ‘liking’ an article on the last male white Rhino, but Facebook will use it to predict with a fair amount of accuracy your political affiliation and sexual orientation, using algorithmic modelling to nudge you to buy something you are most likely to. Hyper-segmentation based on social media profiling can also be used to create a consumer base for political messaging, as has been suggested in the case of Cambridge Analytica.
Many digital corporations, including Uber, Twitter and Microsoft sell their data to third parties who build apps and provide services on top of it. With machine learning, the targeting of individuals assumes new dimensions; it becomes possible to do nano-targeting, zoom in precisely on one individual.
A fintech startup in India recently rejected an applicant because they could uncover that she had actually filed for a loan on behalf of her live-in partner who was unemployed. The boyfriend’s loan request had been rejected earlier. The start-up’s machine learning algorithm had used GPS and social media data — both of which the duo had given permissions for while downloading the app — to make the connection that they were in a relationship.
That big data can be put to use in ways that reinforce ‘social and cultural segregation and exclusion’ is fairly well accepted now. It is this slippery slope from micro-targeting to the social allocation by algorithms of opportunities and privileges that poses serious concerns. A ProPublica investigation from 2016 collected more than 52,000 unique attributes that Facebook used to classify users into micro target-able groups. It then went on to buy Facebook advertisement for housing that demonstrated how it was possible to exclude African-Americans, Hispanics, and Asian-Americans. As Frank Pasquale notes, constitutionally inculcated rights and morality are slowly being undone “by the use of automated processes to assess risk and allocate opportunity”.
The public sphere
This brings us to the question of what the social role of digital intelligence means for the future of democracy. Elections play a vital role in a robust democracy. We seek to safeguard their free and fair nature through regulations that impose restrictions on exit polls or call out parties for unduly influencing voters through the distribution of freebies. Wouldn’t then, a nudging of voters through intimate knowledge of their behaviour be a threat to this socio-political hygiene we seek to maintain?
Can we allow the replacement of the will of the people by a market democracy in which the masses can be gamed? How should the Election Commission take due cognisance of and address such mass-scale manipulation?
Beyond electoral fairness, there are severe repercussions for the sanctity of the public sphere in the rapidly unfolding role of algorithms. When people know that online behaviour is monitored, they carefully moderate how they interact online, a phenomenon referred to as social cooling.
The Cambridge Analytica episode bears a close resemblance to the Snowden disclosure of the unholy nexus of the state, private corporation, and uninhibited surveillance. India has already succeeded in building a ‘cradle to grave’ Panspectron by seeding citizens’ unique biometric identifier across databases. Aadhaar has allowed for an informationisation of life where “...the human body is reduced to a set of numbers that can be stored, retrieved and reconstituted across terminals, screens and interfaces”.
With the biometric, the body can never disassociate with its data and may be recalled, whenever convenient sans ‘the individual’. To add psychographic data akin to a ‘behavioural’ biometric to this mix is to give a ‘God’s eye view’ of society to the state, one that the state is bound to abuse to determine the human condition. For instance, China’s profoundly disturbing, Sesame Credit uses citizen data including data from everyday transactions, biometric data, etc, to dole out instant karma.
From the other end, corporations who already collect behavioural data are keen on accessing Aadhaar data, for this will allow them to trade data around a unique data point to attain, much like the state, a 360-degree view of their customers.
Facebook has faced criticism in the past for experimenting with users’ emotions, using unethical manipulation of information to influence the moods of users. The plausibility of nano-surveillance raises fundamental philosophical questions about society and human agency, calling attention to the urgent task of reining in the data capitalists.
While digital corporations claim to audit how third parties may use the data they have shared, monitoring is lax. India does have rules on data sharing; however, these pertain to a predefined list of data types. Traditionally, data protection legislation has focused on ‘personally identifiable information’(PII), but with technological advances — a la big data analysis and artificial intelligence, what is or is not PII is contested. The law, therefore, needs to be re-imagined to suit contemporary techniques of data analysis.
Besides the fact of unencumbered data sharing, that Facebook was able to collect such vast amounts and varied kinds of data is itself unsettling. To prevent the frightening prospect of future society being reduced to an aggregate of manipulated data points, it may well be necessary to determine that certain kinds of data will not be collected and certain types of data processing will not be done. Restrictions on collection and use can be sector specific, based on well debated social norms and constitutionally driven, much like how the Delhi High Court held on the grounds of the Right to Health that excluding genetic disorders from insurance policies is illegal.
It is time we moved from individual-centred notions of privacy where the ‘user’ is constantly asked to barter the right to privacy for entitlements, credits, and conveniences. Nandan Nilekani's exhortation that citizens be empowered to monetise the plentiful data they generate and get easier credit, better healthcare, better skills and welfare benefits is a recipe for a disempowered society left to the whims of neo-liberal market democracy. The social value of privacy needs to be spotlighted for it urges us to look not only at the individual right over data, but the social benefits that we derive from its recognition.
So far as societies are products of behavioural modelling, Zuckerberg’s apology does not really count.
The authors are with IT for Change, an NGO that works at the intersection of digital technologies and social justice
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