Business Data Ethics: Emerging Trends in the Governance of Advanced Analytics and AI

Abstract

Advanced analytics and artificial intelligence are powerful technologies that, along with their benefits, create new threats to privacy, equality, fairness and transparency. Existing law does not yet protect sufficiently against these threats. This has led some organizations to pursue what they call “data ethics” or “AI ethics” in an attempt to bring advanced analytics and AI more into line with societal values and so legitimate their growing use of these technologies.

To date, much of the scholarship on data ethics has sought either to define the ethical principles to which organization should aspire, or to map out the laws and regulations needed to push organizations towards these ethical goals. While these two lines of inquiry are important, the literature is missing a critical third dimension: empirical work on how organizations are actually governing the threats that their use of advanced analytics and AI can generate. Good regulatory design requires such knowledge. Yet, while there have been important studies of how organizations manage privacy “on the ground” (Bamberger and Mulligan 2015), there has been little such work on the governance of advanced analytics and AI.

This report begins to fill this gap. Focusing on private sector organizations, the authors interviewed corporate privacy managers deemed by their peers to be leaders in the governance of advanced analytics and AI, as well as the lawyers, consultants and thought leaders who advise them on this topic. They also surveyed a wider range of privacy mangers. The study sought to answer three, fundamental questions about business data ethics management: (1) How do leading companies conceptualize the threats that their use of advanced analytics and AI pose for individuals, groups and the broader society? (2) If it is true that the law does not yet require companies to reduce these risks, then why are they pursuing data ethics? and (3) How are companies pursuing data ethics? What substantive benchmarks, management processes and technological solutions do they use towards this end?

The authors previously shared on SSRN their preliminary findings. This final report provides a much fuller picture. The report should provide legislators and policymakers with an empirical foundation for their efforts to regulate advanced analytics and AI, at the same time as it gives interested organizations ideas on how to improve their data ethics management.

Keywords: business ethics, data ethics, AI ethics, governance of AI, algorithm, data analytics, big data, artificial intelligence, data privacy, algorithmic bias, privacy management, corporate social responsibility, CSR

JEL Classification: L50, L53, L86, M14, M15, M38, O31, O35, O38