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A Tool for Fairness Assessment and Red-Lining Detection in AI Systems

Abstract:

Fairness in AI is essential to data analysis, ensuring ethical decision-making. Various fairness metrics are used depending on the specific use case. In this work, we propose a fairness assessment tool that provides a comprehensive analysis, incorporating state-of-the-art fairness metrics and association analysis to detect and include the red-lining effect. Features that exhibit strong associations with sensitive attributes can contribute to indirect discrimination. For this reason, we identify hidden biases through association analysis, detecting proxy variables that may perpetuate discrimination and lead to a more discriminatory machine learning model. We introduce a generalized fairness analysis framework capable of addressing complex scenarios, supported by a query mechanism designed to capture extensive contextual information for a more representative evaluation. Notably, fairness concerns in the business sector often involve complex, context-dependent scenarios. Our tool enables users to formulate such problems effectively and retrieve important insights. In this work, we present the flow of the tool along with some indicative results on complex fairness problems.