Date: Wednesday, April 2nd, 2025
9:00 am – 10:00 am Pacific Time
12:00 pm – 1:00 pm Eastern Time
Location: Weekly Seminar, Zoom
Title: Regulation along the AI Development Pipeline for Fairness, Safety and Related Goals
Abstract:
Machine learning (ML) and artificial intelligence (AI) systems are designed within a broader ecosystem involving multiple actors and interests. This talk focuses on attempts to regulate the AI development process to make these technologies fair, safe, performant, or otherwise aligned with social ends.
I will start with a discussion of one proposal for ML regulation, stemming from U.S. disparate impact doctrine, which compels plaintiffs or firms to search for a “less discriminatory alternative” (LDA), an alternative policy that meets the same business needs but exhibits lower disparate impacts across protected populations. Defining this concept for data-driven decision-making might open up a promising avenue for regulation, however, a number of technical challenges remain. I will provide a set of formal results characterizing the ‘multiplicity’ of model designs and the limits and opportunities for searching for LDAs. [based on joint work w/ Manish Raghavan, Solon Barocas]
More generally, AI is often deployed in a way that requires a general-purpose producer to adapt to a number of different domains. I will put forward a model of how regulation would operate in this sort of process. Reasoning about the interaction between regulators, general-purpose AI creators, and domain specialists suggests that even straightforward and modest regulatory measures can backfire, inadvertently undermining safety outcomes. Conversely, stronger regulations, applied strategically along the development pipeline, can boost both safety and performance outcomes. [based on joint work w/ Jon Kleinberg, Hoda Heidari]
The talk will conclude with a discussion about the role for formal models in building actionable regulatory frameworks for AI.
Bio:
Benjamin Laufer is a PhD student in the School of Computing and Information Sciences at Cornell Tech, where he is advised by Helen Nissenbaum and Jon Kleinberg, and affiliated with the AI, Policy and Practice Group and the Digital Life Initiative. He is interested in data-driven algorithmic systems and their implications for the public interest. His research uses tools and methods spanning statistics, game theory, network science, and ethics. Prior to joining Cornell, Ben worked as a data scientist at Lime, where he applied machine learning to urban mobility decisions. He graduated from Princeton University with a B.S.E. in Operations Research and Financial Engineering with minors in Urban Studies and Environmental Studies.
Ben’s research is supported by a LinkedIn Fellowship. He has also spent time at Microsoft Research with the Fairness, Accountability, Transparency and Ethics Group. He was named a “rising star” by Stanford in Management Science and Engineering.