Achieving Socio-Economic Parity through the Lens of EU AI Act

2025-03-29

AI Summary

Existing AI fairness frameworks largely overlook socioeconomic status (SES), perpetuating systemic bias against disadvantaged populations. Method: We propose Socio-Economic Parity (SEP), a novel fairness notion that explicitly incorporates SES into algorithmic fairness constraints—distinguishing between controllable individual effort and structural disadvantage—and enables targeted affirmative action. Leveraging the Adult dataset, we formalize quantifiable SEP metrics and design optimization models satisfying SEP constraints while aligning with the EU AI Act’s regulatory requirements. Contribution/Results: Empirical evaluation demonstrates that SEP effectively mitigates SES-driven discriminatory bias, achieving a robust trade-off between safeguarding fundamental rights and advancing substantive outcome equality. Our framework provides a technically rigorous, policy-aligned, and deployable paradigm for responsible AI governance.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Algorithmic accountability and transparency on the web
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Unfair treatment and discrimination are critical ethical concerns in AI systems, particularly as their adoption expands across diverse domains. Addressing these challenges, the recent introduction of the EU AI Act establishes a unified legal framework to ensure legal certainty for AI innovation and investment while safeguarding public interests, such as health, safety, fundamental rights, democracy, and the rule of law (Recital 8). The Act encourages stakeholders to initiate dialogue on existing AI fairness notions to address discriminatory outcomes of AI systems. However, these notions often overlook the critical role of Socio-Economic Status (SES), inadvertently perpetuating biases that favour the economically advantaged. This is concerning, given that principles of equalization advocate for equalizing resources or opportunities to mitigate disadvantages beyond an individual's control. While provisions for discrimination are laid down in the AI Act, specialized directions should be broadened, particularly in addressing economic disparities perpetuated by AI systems. In this work, we explore the limitations of popular AI fairness notions using a real-world dataset (Adult), highlighting their inability to address SES-driven disparities. To fill this gap, we propose a novel fairness notion, Socio-Economic Parity (SEP), which incorporates SES and promotes positive actions for underprivileged groups while accounting for factors within an individual's control, such as working hours, which can serve as a proxy for effort. We define a corresponding fairness measure and optimize a model constrained by SEP to demonstrate practical utility. Our results show the effectiveness of SEP in mitigating SES-driven biases. By analyzing the AI Act alongside our method, we lay a foundation for aligning AI fairness with SES factors while ensuring legal compliance.

Problem

• Addressing AI fairness gaps in socio-economic status (SES) disparities.
• Proposing Socio-Economic Parity (SEP) to mitigate SES-driven biases in AI.
• Aligning AI fairness with EU AI Act's legal and ethical requirements.

Innovation

• Proposes Socio-Economic Parity (SEP) fairness notion
• Incorporates Socio-Economic Status (SES) in AI fairness
• Optimizes model constrained by SEP measure