In the era of big data and AI, people can suffer because of how the sum of individual data is analysed and sorted into groups by algorithms. Novel forms of collective data-driven harms are appearing as a result: online housing, job and credit ads discriminating on the basis of race and gender, women disqualified from jobs on the basis of gender.
In Martin Tisné's new paper, The Data Delusion: Protecting Individual Data is Not Enough When the Harm is Collective published by Stanford University’s Cyber Policy Center and edited by their International Policy Director Marietje Schaake, he argues that privacy concerns surrounding COVID-19 brought to the surface a number of systemic mismatches between individual privacy law and the value of collective data processing.
Marietje and Martin discuss in this Q&A the need to update legal privacy standards to be more reflective of the harms incurred through collective data analysis, as opposed to individual privacy violations. Markets extract huge value from our collective data, but our laws mostly protect individual privacy. Regulation must catch up to protect the public interest.