Peer-induced Fairness: A Causal Approach for Algorithmic Fairness Auditing

With the EU AI Act effective from 1 August 2024, high-risk applications like credit scoring must adhere to stringent transparency and quality standards, including algorithmic fairness evaluations. Consequently, developing tools for auditing algorithmic fairness has become crucial. This paper addresses a key question: how can we scientifically audit algorithmic fairness? It is vital to determine whether adverse decisions result from algorithmic discrimination or the subjects' inherent limitations. We introduce a novel auditing framework, ``peer-induced fairness'', leveraging counterfactual fairness and advanced causal inference techniques within credit approval systems. Our approach assesses fairness at the individual level through peer comparisons, independent of specific AI methodologies. It effectively tackles challenges like data scarcity and imbalance, common in traditional models, particularly in credit approval. Model-agnostic and flexible, the framework functions as both a self-audit tool for stakeholders and an external audit tool for regulators, offering ease of integration. It also meets the EU AI Act's transparency requirements by providing clear feedback on whether adverse decisions stem from personal capabilities or discrimination. We demonstrate the framework's usefulness by applying it to SME credit approval, revealing significant bias: 41.51% of micro-firms face discrimination compared to non-micro firms. These findings highlight the framework's potential for diverse AI applications.

Peer-induced Fairness: A Causal Approach to Reveal Algorithmic Unfairness in Credit Approval

This paper introduces a novel framework, "peer-induced fairness", to scientifically audit algorithmic fairness. It addresses a critical but often overlooked issue: distinguishing between adverse outcomes due to algorithmic discrimination and those resulting from individuals' insufficient capabilities. By utilizing counterfactual fairness and advanced causal inference techniques, such as the Single World Intervention Graph, this model-agnostic approach evaluates fairness at the individual level through peer comparisons and hypothesis testing. It also tackles challenges like data scarcity and imbalance, offering a flexible, plug-and-play self-audit tool for stakeholders and an external audit tool for regulators, while providing explainable feedback for those affected by unfavorable decisions.