Sandra Wachter and Brent Mittelstadt
Oxford Internet Institute
Originally published October 11, 2018
Numerous applications of ‘Big Data analytics’ drawing potentially troubling inferences about individuals and groups have emerged in recent years. Major internet platforms are behind many of the highest profile examples: Facebook may be able to infer protected attributes such as sexual orientation, race, as well as political opinions and imminent suicide attempts, while third parties have used Facebook data to decide on the eligibility for loans and infer political stances on abortion. Susceptibility to depression can similarly be inferred via usage data from Facebook and Twitter. Google has attempted to predict flu outbreaks as well as other diseases and their outcomes. Microsoft can likewise predict Parkinson’s disease and Alzheimer’s disease from search engine interactions. Other recent invasive applications include prediction of pregnancy by Target, assessment of users’ satisfaction based on mouse tracking, and China’s far reaching Social Credit Scoring system.
Inferences in the form of assumptions or predictions about future behaviour are often privacy-invasive, sometimes counterintuitive and, in any case, cannot be verified at the time of decision-making. While we are often unable to predict, understand or refute these inferences, they nonetheless impact on our private lives, identity, reputation, and self-determination.
These facts suggest that the greatest risks of Big Data analytics do not stem solely from how input data (name, age, email address) is used. Rather, it is the inferences that are drawn about us from the collected data, which determine how we, as data subjects, are being viewed and evaluated by third parties, that pose the greatest risk. It follows that protections designed to provide oversight and control over how data is collected and processed are not enough; rather, individuals require meaningful protection against not only the inputs, but the outputs of data processing.
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