Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI release_6kzy4k4znzffjkunc52n3lhlzu

by Kiana Alikhademi, Brianna Richardson, Emma Drobina, Juan E. Gilbert

Released as a article .

2021  

Abstract

Many ML models are opaque to humans, producing decisions too complex for humans to easily understand. In response, explainable artificial intelligence (XAI) tools that analyze the inner workings of a model have been created. Despite these tools' strength in translating model behavior, critiques have raised concerns about the impact of XAI tools as a tool for `fairwashing` by misleading users into trusting biased or incorrect models. In this paper, we created a framework for evaluating explainable AI tools with respect to their capabilities for detecting and addressing issues of bias and fairness as well as their capacity to communicate these results to their users clearly. We found that despite their capabilities in simplifying and explaining model behavior, many prominent XAI tools lack features that could be critical in detecting bias. Developers can use our framework to suggest modifications needed in their toolkits to reduce issues likes fairwashing.
In text/plain format

Archived Files and Locations

application/pdf  541.8 kB
file_xskdnhk6uzb7xgo6xub53jwmyu
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2021-06-14
Version   v1
Language   en ?
arXiv  2106.07483v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: cb76f82d-9149-478b-8d88-73acf78e77a1
API URL: JSON