Market-based view on privacy and big data
A recently published paper – “Separation, Pooling, and Predictive Privacy Harms from Big Data: Confusing Benefits for Costs”, looks at the right to privacy from the perspective of market efficiency. The dilemma is between more efficient markets based on sufficient information which indicate who are the good or bad debtors, customers, employees on the one hand. Put simply – in an effective economy bad (higher risk) types subsidize good (lower risk) types. On the other hand, is the right to be ‘left alone’. Big data analytics makes it ever more possible and affordable to predict behavior based on previous events. This inevitably leads to diverse treatment – some debtors, customers, employees are seen as more or less risk than others. This leads to market effective decisions but also causes “predictive privacy harm” or “classification harm”.
Big data can reduce information asymetries and thus improve the performance of the markets. The question is – when can Big data be harmful. The author, James C. Cooper, uses economic theories of contracts and torts to answer the question. Analytical and empirical arguments reveal that Big data and the right to privacy co-exist in a complex relationships. Big data can lead to “predictive privacy harm” but also can deliver many benefits. Examples are the labour markets, differential treatment of the poor in credit markets etc. As result of the discussion the author calls for cautious and balanced approach to the calls for Big data public policies.