 Fairness via Explanation Quality: Evaluating Disparities in the Quality
of Post hoc ExplanationsAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2022 |
 The Road to Explainability is Paved with Bias: Measuring the Fairness of
ExplanationsConference on Fairness, Accountability and Transparency (FAccT), 2022 |
 A Survey on the Explainability of Supervised Machine LearningJournal of Artificial Intelligence Research (JAIR), 2020 |
 ERASER: A Benchmark to Evaluate Rationalized NLP ModelsAnnual Meeting of the Association for Computational Linguistics (ACL), 2019 |
 Model Agnostic Supervised Local ExplanationsNeural Information Processing Systems (NeurIPS), 2018 |
 A Benchmark for Interpretability Methods in Deep Neural NetworksNeural Information Processing Systems (NeurIPS), 2018 |
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