Lindsay Sanneman "Transparent Value Alignment: Foundations for Human-Centered Explainable AI in Alignment"

4:15 pm
Wednesday, February 14, 2024
104A Surge Hall
Faculty Host: Dr. Rakesh Kapania

Abstract:   Aligning the objectives of artificial intelligence (AI) agents with those of humans can greatly enhance these systems’ ability to flexibly, safely, and reliably meet humans’ goals across diverse contexts from space exploration to robotic manufacturing. However, it is often difficult or impossible for humans, both expert and non-expert, to enumerate their objectives comprehensively, accurately, and in forms that are readily usable for agent planning. Value alignment is an open challenge in AI that aims to address this problem by enabling agents to infer human goals and values through interaction. Providing humans with direct and explicit feedback about this value learning process through explainable AI (XAI) can enable humans to more efficiently and effectively teach agents about their goals. In this talk, I will introduce the Transparent Value Alignment (TVA) paradigm, which captures this two-way communication and inference process, and will discuss foundations for the design and evaluation of XAI within this paradigm, including human-centered metrics for alignment, models of agent transparency, and algorithms for automatic generation of user-tailored explanations.

Bio:  Lindsay Sanneman is a postdoctoral associate in the Department of Aeronautics and Astronautics at MIT and a member of the Interactive Robotics Group and the Algorithmic Alignment Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on the development of models, metrics, and algorithms for explainable AI (XAI) and AI alignment in complex human-autonomy interaction settings. Since 2018, she has been a member of MIT’s Work of the Future task force and has visited over 50 factories worldwide alongside an interdisciplinary team of social scientists and engineers in order to study the adoption of robotics and AI in manufacturing. She has also been selected as a Future Leader in Aerospace and a Siegel Research Fellow and has presented her work in diverse venues including to the Industry Studies Association, the Federal Aviation Administration (FAA), and the UN Department of Economic and Social Affairs.