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Found 3 papers, 2 papers with code
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 relation to explanation disparities remains largely unexplored.
Understanding human mobility patterns in Chicago: an analysis of taxi data using clustering techniques
This analysis will provide a sense of which neighborhoods individuals are using taxis to travel between, suggesting regions to focus new public transit development efforts.
Predicting blood pressure under circumstances of missing data: An analysis of missing data patterns and imputation methods using NHANES
Motivated by the pervasiveness of missing data in the NHANES dataset, we will conduct an analysis of imputation methods under different simulated patterns of missing data.
