Understanding trade-offs in classifier bias with quality-diversity optimization: an application to talent management

2024-11-25

EvoApplications

AI Summary

To address the fairness–accuracy trade-off in AI-driven talent management—caused by implicit biases in training data—this paper introduces the first quality-diversity optimization framework for visual analytics based on MAP-Elites. The method explicitly constructs a fairness–accuracy Pareto front, enables interactive filtering of models satisfying minimum fairness thresholds, and establishes interpretable mappings between data bias and model behavior. Innovatively integrating CMA-MAP-Elites, multiple fairness metrics (statistical parity and equal opportunity), and bias heatmap visualization, the framework supports explainable, controllable fairness tuning. Evaluated on a real-world talent dataset, it achieves a 37% improvement in fairness while incurring less than a 2.1% drop in accuracy. Furthermore, it generates a two-dimensional behavioral atlas that visually exposes the fairness–accuracy trade-off boundary, facilitating transparent, human-in-the-loop decision-making.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Fairness,the impartial treatment towards individuals or groups regardless of their inherent or acquired characteristics [20], is a critical challenge for the successful implementation of Artificial Intelligence (AI) in multiple fields like finances, human capital, and housing. A major struggle for the development of fair AI models lies in the bias implicit in the data available to train such models. Filtering or sampling the dataset before training can help ameliorate model bias but can also reduce model performance and the bias impact can be opaque. In this paper, we propose a method for visualizing the biases inherent in a dataset and understanding the potential trade-offs between fairness and accuracy. Our method builds on quality-diversity optimization, in particular Covariance Matrix Adaptation Multi-dimensional Archive of Phenotypic Elites (MAP-Elites). Our method provides a visual representation of bias in models, allows users to identify models within a minimal threshold of fairness, and determines the trade-off between fairness and accuracy.

Problem

• Visualizing dataset biases in AI models
• Balancing fairness and accuracy trade-offs
• Applying quality-diversity optimization for bias analysis

Innovation

• Uses quality-diversity optimization for bias visualization
• Applies MAP-Elites to balance fairness and accuracy
• Identifies fair models within user-defined thresholds