This white paper outlines some of the most important considerations for managing risk in machine learning models to create more accurate and compliant algorithms. Key recommendations include focusing on the quality of input data as well as implementing techniques to reduce and expose bias.
Andrew Burt, Immuta & Future of Privacy Forum
This essay discusses the legal requirements of pricing credit and the architecture of machine learning and intelligent algorithms to provide an overview of legislative gaps, legal solutions, and a framework for testing discrimination that evaluates algorithmic pricing rules. Using real-world mortgage data, the authors find that restricting the data characteristics within the algorithm can increase pricing gaps while having a limited impact on disparity.
Talia B. Gillis & Jann L. Spiess, University of Chicago Law Review
This paper examines concerns about big data’s disparate impact risk from the perspective of American antidiscrimination law, more specifically, through Title VII’s prohibition of discrimination in employment. The paper also calls out the legal and political difficulties of addressing and remedying this type of discrimination, in particular, the tension between the two major theories underlying antidiscrimination law: anticlassification and antisubordination.
104 California Law Review 671 (2016)
Solon Barocas, Andrew D. Selbst
This paper provides an overview of the challenges and implications for the supervision and regulation of financial services with regards to the opportunities presented by BDAI technology: the phenomena of big data (BD) being used in conjunction with artificial intelligence (AI). The paper draws from market analyses and use cases to outline potential developments seen from the industry and government perspectives, and the impact on consumers.
Germany’s Federal Financial Supervisory Authority (BaFin)
