arXiv:2605.28233v1 Announce Type: cross Abstract: Fairness-accuracy trade-offs are a central concern in the deployment of fairness-aware machine learning methods. When sensitive attributes are unavailable at inference time-the so called unawareness setting, principled methods for obtaining accurate predictions under relaxed fairness constraints are largely missing. In this work, we address this gap by formulating regression under a demographic parity penalty as an optimal transport problem. Our framework unifies both the \emph{aware} and \emph{unaware} settings and characterizes optimal predic
The increasing deployment of AI systems, particularly in sensitive domains, necessitates robust frameworks for ensuring fairness and mitigating bias, even when direct demographic data is unavailable.
This research provides a more principled and unified approach to address critical fairness-accuracy trade-offs in AI, impacting the ethical deployment and regulatory landscape of machine learning.
The ability to achieve relaxed fairness constraints in 'unaware' AI settings, using techniques like optimal transport, offers a new methodology for developers and policymakers to manage bias.
- · AI developers
- · Regulatory bodies
- · Companies deploying AI in sensitive applications
- · Users of AI systems
- · AI systems with unmitigated bias
- · Organizations ignoring fairness considerations
Increased adoption of ethical AI practices and tools that account for fairness in data-limited environments.
Potential for new industry standards or certifications around 'fair-by-design' AI systems, even without explicit demographic data.
Reduced societal friction and increased trust in AI deployments due to more equitable outcomes, potentially accelerating broader AI integration.
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Read at arXiv cs.LG