Nari Johnson — accepted AI-conference papers

2022

OpenXAI: Towards a Transparent Evaluation of Model Explanations

Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri +2

NeurIPS 2022accept

While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introduce OpenXAI, a comprehensive and extensible open-source framework for evaluating and benchmarking post hoc explanation m…

Cited by 195 Source ↗PDF ↗code ↗Scholar ↗

2022

Use-Case-Grounded Simulations for Explanation Evaluation

Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar

NeurIPS 2022accept

A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use cases. However, running user studies is challenging and costly, and consequently each study typically only evaluates a l…

Cited by 22 Source ↗PDF ↗Scholar ↗