A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning

Eura Nofshin, Esther Brown, Brian Lim, Weiwei Pan, Finale Doshi-Velez

Keywords: Interpretability, Sim2Real, Human Proxy, Human-AI Decision Making, Explanation Properties

TL;DR: In this paper, we introduce XAIsim2real, a generalizable, cost-effective method for identifying task-relevant explanation properties in silico, which can guide the design of more expensive user studies.

Abstract: Existing user studies suggest that different tasks may require explanations with different properties. However, user studies are expensive. In this paper, we introduce XAIsim2real, a generalizable, cost-effective method for identifying task-relevant explanation properties in silico, which can guide the design of more expensive user studies. We use XAIsim2real to identify relevant proxies for three example tasks and validate our simulation with real user studies.

Submission Number: 96