Training Human-AI Teams

AI systems are augmenting humans' capabilities in settings such as healthcare and programming, forming human-AI teams. To enable more accurate and timely decisions, we need to optimize the performance of the human-AI team directly. In this thesis, we utilize a mathematical framing of the human-AI team and propose a set of methods that optimize the AI, the human, and the interface in which they communicate to enable better team performance. We first show how to provably train AI classifiers that complement humans and can defer the decision to humans when it is best to do so. However, in specific settings, AI cannot autonomously make decisions and thus only provides advice to humans. In that case, we build onboarding procedures that train humans to have an accurate mental model of the AI to enable appropriate reliance. Finally, we study how humans interact with large language models (LLMs) to write code. To understand current inefficiencies, we developed a taxonomy to categorize programmers' interactions with the LLM. Motivated by insight from the taxonomy, we leverage human feedback to know when to best display LLM suggestions.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Institute for Data, Systems, and Society

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Mozannar, Hussein

Advisors dc:contributor.advisor

• Sontag, David
• Satyanarayan, Arvind
• Glassman, Elena
• Horvitz, Eric

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/155508

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/155508