no code implementations • 1 Apr 2024 • Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu
However, it is challenging to compare predictive performance against an existing decision-making policy that is generally under-specified and dependent on unobservable factors.
no code implementations • 30 Aug 2023 • Anna Kawakami, Luke Guerdan, Yanghuidi Cheng, Matthew Lee, Scott Carter, Nikos Arechiga, Kate Glazko, Haiyi Zhu, Kenneth Holstein
A growing body of research has explored how to support humans in making better use of AI-based decision support, including via training and onboarding.
1 code implementation • 22 Feb 2023 • Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu
We also develop a method for estimating treatment-dependent measurement error parameters when these are unknown in advance.
no code implementations • 13 Feb 2023 • Luke Guerdan, Amanda Coston, Zhiwei Steven Wu, Kenneth Holstein
In this paper, we identify five sources of target variable bias that can impact the validity of proxy labels in human-AI decision-making tasks.
no code implementations • 14 Jan 2022 • Luke Guerdan, Hatice Gunes
We show that decentralized learning is a viable alternative to centralized learning in a proof-of-concept Socially-Aware Navigation domain, and demonstrate how Elastic Transfer improves several of the proposed criteria.
no code implementations • 16 Jun 2021 • Luke Guerdan, Alex Raymond, Hatice Gunes
As machine learning approaches are increasingly used to augment human decision-making, eXplainable Artificial Intelligence (XAI) research has explored methods for communicating system behavior to humans.