Data Coverage for Detecting Representation Bias in Image Datasets: A Crowdsourcing Approach

TL;DR

This paper introduces a crowdsourcing-based method to detect representation bias in image datasets by analyzing data coverage, providing theoretical guarantees, heuristics for cost reduction, and validating with extensive experiments including live human assessments.

Contribution

It develops a divide and conquer algorithm with performance guarantees for identifying coverage gaps without explicit attributes, and proposes heuristics and model-based adjustments to improve detection efficiency.

Findings

The algorithm effectively identifies coverage gaps in image datasets.

Pre-trained predictors are unreliable for bias detection.

Live experiments validate the approach's effectiveness.

Abstract

Existing machine learning models have proven to fail when it comes to their performance for minority groups, mainly due to biases in data. In particular, datasets, especially social data, are often not representative of minorities. In this paper, we consider the problem of representation bias identification on image datasets without explicit attribute values. Using the notion of data coverage for detecting a lack of representation, we develop multiple crowdsourcing approaches. Our core approach, at a high level, is a divide and conquer algorithm that applies a search space pruning strategy to efficiently identify if a dataset misses proper coverage for a given group. We provide a different theoretical analysis of our algorithm, including a tight upper bound on its performance which guarantees its near-optimality. Using this algorithm as the core, we propose multiple heuristics to reduce the coverage detection cost across different cases with multiple intersectional/non-intersectional groups. We demonstrate how the pre-trained predictors are not reliable and hence not sufficient for detecting representation bias in the data. Finally, we adjust our core algorithm to utilize existing models for predicting image group(s) to minimize the coverage identification cost. We conduct extensive experiments, including live experiments on Amazon Mechanical Turk to validate our problem and evaluate our algorithms' performance.