Accuracy, Fairness, and Interpretability of Machine Learning Criminal Recidivism Models

Ingram, Eric; Gursoy, Furkan; Kakadiaris, Ioannis A.

doi:10.1109/bdcat56447.2022.00040

Cited by 2 publications

(1 citation statement)

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“…[42] applies a similar approach, attributing a models overall unfairness to its individual features using the Shapley value, and proposing an intervention to improve fairness. [43] examines machine learning models to predict recidivism, and empirically shows tradeoffs between model accuracy, fairness, and interpretability.…”

Section: Related Workmentioning

confidence: 99%

Model Explanation Disparities as a Fairness Diagnostic

Chang¹,

Fishman²,

Neel³

2023

Preprint

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In recent years, there has been a flurry of research focusing on the fairness of machine learning models, and in particular on quantifying and eliminating bias against protected subgroups. One line of work generalizes the notion of protected subgroups beyond simple discrete classes by introducing the notion of a "rich subgroup" [1, 2], and seeks to train models that are calibrated or equalize error rates with respect to these richer subgroup classes. Largely orthogonally, local model explanation methods have been developed that given a classifier h and test point x, attribute influence for the prediction h(x) to the individual features of x. This raises a natural question: Do local model explanation methods attribute different feature importance values on average across different protected subgroups, and can we detect these disparities efficiently? If the model places high weight on a given feature in a specific protected subgroup, but not on the dataset overall (or vice versa), this could be a potential indicator of bias in the predictive model or the underlying data generating process, and is at the very least a useful diagnostic that signals the need for a domain expert to delve deeper. In this paper, we formally introduce the notion of feature importance disparity (FID) in the context of rich subgroups, design oracle-efficent algorithms to identify large FID subgroups, and conduct a thorough empirical analysis that establishes auditing for FID as an important method to investigate dataset bias. Our experiments show that across 4 datasets and 4 common feature importance methods our algorithms find (feature, subgroup) pairs that simultaneously: (i) have subgroup feature importance that is often an order of magnitude different than the importance on the dataset as a whole (ii) generalize out of sample, and (iii) yield interesting discussions about potential bias inherent in these datasets.

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Section: Related Workmentioning

confidence: 99%