SAFE "LawLab - Fintech & AI" Workshop on the paper "Predictably Unequal. The Effects of Machine Learning on Credit Markets"

27 Jun 2022 15:00 PM
27 Jun 2022 17:00 PM

The SAFE "LawLab - Fintech & AI" organizes and cordially invites you to attend a workshop on the paper
Predictably Unequal. The Effects of Machine Learning on Credit Markets
Andreas Fuster, Paul Goldsmith-Pinkham, Tarun Ramadorai, Ansgar Walther
The Journal of Finance, Vol:77, ISSN:0022-1082, Pages:5-47
to be virtually held on 27 June 2022, 3:00 p.m. - 5:00 p.m., via Zoom
Presented by: Ansgar Walther (Imperial College London)
Talia Gillis (Columbia Law School) will comment on the paper against the background of US Anti-Discrimination law
Aislinn Kelly-Lyth (Faculty of Law, University of Oxford) will comment on the paper against the background of UK Anti-Discrimination law
Katja Langenbucher (SAFE "LawLab – Fintech & AI" and Goethe University)
Loriana Pelizzon (SAFE Department "Financial Markets" and Goethe University)
Credit Scoring is an established tool to reduce information asymmetry in the context of underwriting decisions. With the advent of big data and machine learning, AI Credit Scoring has gained momentum, enhancing the scope of input data significantly. While this has led to better access for „invisible prime“ applicants, concerns of (among other things) algorithmic discrimination have been raised. Following up on such worries, the EU Commission has taken a bold step when publishing its Proposal for an AI Draft in April 2021, qualifying AI Credit Scoring as a „high risk application“ which entails a host of compliance requirements.