This essay considers the potential to use information technology to generate personalized disclosures to consumers concerning privacy policies, financial services, and consumer products more generally, as well as potential limits of the notice-and-consent model for managing privacy choices.
Christoph Busch, 86 University of Chicago Law Review 309
This article analyzes the technical nuances and legal implications of using synthetic data, which uses machine learning techniques to modify raw data, as an alternative to anonymization or differential privacy to protect privacy interests while facilitating research.
Steven M. Bellovin, Preetam K. Dutta, & Nathan Reitinger, 22 Stanford Technological Law Review 1
This paper surveys several existing federal laws bearing on management of financial data as well as recent state activity before analyzing potential elements for comprehensive federal legislation.
Marshall Lux & Matthew Shakelford, Harvard Mossavar-Rahmani Center for Business & Government Working Paper 135
This article charts how FinTech companies are deploying new technology pilot programs to aid in COVID recovery. The article references several companies deploying big data analysis capabilities and notes uses of distributed ledgers for settling trades.
Peter Feltman, Roll Call
This brief from the World Economic Forum evaluates how the use of personal data – and business models for monetizing that data – might evolve as firms respond to the pandemic. For example, machine learning algorithms previously deployed to analyze consumer tastes could be deployed to track viral detection.
Murat Sönmez
This report analyzes mortgage default using information taken from the JPMorgan Chase Institute housing finance research to evaluate the relationship between liquidity, equity, income level, and payment burden and default. Across all four groups, the report finds that liquidity may be more predictive for determining the likelihood of mortgage default particularly among borrowers with little post-closing liquidity and little liquidity but high equity. Overall, the report determines that alternative underwriting standards incorporating a minimum amount of post-closing liquidity may be a more effective way to prevent mortgage default compared to using DTI thresholds at origination.
Diana Farrell, Kanav Bhagat, and Chen Zhao
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)
