Sociology

Business

Metric (unit)

Jurisprudence

Data mining

Internet privacy

Marketing

Political science

Data governance

Data quality

Data science

General Data Protection Regulation

Computer science

Autonomy

Big data

Data Protection Act 1998

Analytics

Columbia Business Law Review

Big Data analytics and artificial intelligence (AI) draw non-intuitive and unverifiable inferences and predictions about the behaviors, preferences, and private lives of individuals. These inferences draw on highly diverse and feature-rich data of unpredictable value, and create new opportunities for discriminatory, biased, and invasive decision-making. Data protection law is meant to protect people’s privacy, identity, reputation, and autonomy, but is currently failing to protect data subjects from the novel risks of inferential analytics. The legal status of inferences is heavily disputed in legal scholarship, and marked by inconsistencies and contradictions within and between the views of the Article 29 Working Party and the European Court of Justice (ECJ).This Article shows that individuals are granted little control or oversight over how their personal data is used to draw inferences about them. Compared to other types of personal data, inferences are effectively “economy class” personal data in the General Data Protection Regulation (GDPR). Data subjects’ rights to know about (Articles 13–15), rectify (Article 16), delete (Article 17), object to (Article 21), or port (Article 20) personal data are significantly curtailed for inferences. The GDPR also provides insufficient protection against sensitive inferences (Article 9) or remedies to challenge inferences or important decisions based on them (Article 22(3)).This situation is not accidental. In standing jurisprudence the ECJ has consistently restricted the remit of data protection law to assessing the legitimacy of input personal data undergoing processing, and to rectify, block, or erase it. Critically, the ECJ has likewise made clear that data protection law is not intended to ensure the accuracy of decisions and decision-making processes involving personal data, or to make these processes fully transparent. Current policy proposals addressing privacy protection (the ePrivacy Regulation and the EU Digital Content Directive) and Europe’s new Copyright Directive and Trade Secrets Directive also fail to close the GDPR’s accountability gaps concerning inferences.This Article argues that a new data protection right, the “right to reasonable inferences,” is needed to help close the accountability gap currently posed by “high risk inferences,” meaning inferences drawn from Big Data analytics that damage privacy or reputation, or have low verifiability in the sense of being predictive or opinion-based while being used in important decisions. This right would require ex-ante justification to be given by the data controller to establish whether an inference is reasonable. This disclosure would address (1) why certain data form a normatively acceptable basis from which to draw inferences; (2) why these inferences are relevant and normatively acceptable for the chosen processing purpose or type of automated decision; and (3) whether the data and methods used to draw the inferences are accurate and statistically reliable. The ex-ante justification is bolstered by an additional ex-post mechanism enabling unreasonable inferences to be challenged.

A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI