Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors

TL;DR

This paper investigates the sources of unfairness in unsupervised outlier detection algorithms by systematically injecting various data biases and analyzing their impact, revealing that algorithmic bias is influenced by both data properties and design choices.

Contribution

It provides a comprehensive data-centric bias injection framework to analyze fairness issues in outlier detection, highlighting the interaction between data properties and algorithmic design.

Findings

OD algorithms are susceptible to various data biases.

Bias injection affects fairness in outlier detection.

Natural data properties can also cause unfairness.

Abstract

The astonishing successes of ML have raised growing concern for the fairness of modern methods when deployed in real world settings. However, studies on fairness have mostly focused on supervised ML, while unsupervised outlier detection (OD), with numerous applications in finance, security, etc., have attracted little attention. While a few studies proposed fairness-enhanced OD algorithms, they remain agnostic to the underlying driving mechanisms or sources of unfairness. Even within the supervised ML literature, there exists debate on whether unfairness stems solely from algorithmic biases (i.e. design choices) or from the biases encoded in the data on which they are trained. To close this gap, this work aims to shed light on the possible sources of unfairness in OD by auditing detection models under different data-centric factors. By injecting various known biases into the input data -- as pertain to sample size disparity, under-representation, feature measurement noise, and group membership obfuscation -- we find that the OD algorithms under the study all exhibit fairness pitfalls, although differing in which types of data bias they are more susceptible to. Most notable of our study is to demonstrate that OD algorithm bias is not merely a data bias problem. A key realization is that the data properties that emerge from bias injection could as well be organic -- as pertain to natural group differences w.r.t. sparsity, base rate, variance, and multi-modality. Either natural or biased, such data properties can give rise to unfairness as they interact with certain algorithmic design choices.