Understanding Disparities in Post Hoc Machine Learning Explanation
Understanding Disparities in Post Hoc Machine Learning Explanation
Previous work has highlighted that existing post-hoc explanation methods exhibit disparities in explanation fidelity (across "race" and "gender" as sensitive attributes), and while a large body of work focuses on mitigating these issues at the explanation metric level, the role of the data generating process and black box model in …