ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Dynamic Fairness: Toward Morally Adaptive AI in a Moving World
Provisionally accepted
- University of Kentucky, Lexington, United States
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Most algorithmic fairness frameworks treat justice as a static optimization problem, but fairness in the real world is inherently dynamic—evolving with social values, economic conditions, and technological capabilities. This paper introduces dynamic fairness, a conceptual paradigm that treats ethical AI not as a one-time calibration but as an ongoing process of moral adaptation. We propose a theoretical framework integrating causal reasoning, adaptive mechanisms, and counterfactual governance to enable AI systems that could evolve alongside society's understanding of justice. Through conceptual analysis, formal problem specification, and illustrative synthetic examples, we demonstrate how dynamic fairness addresses limitations of static approaches. We argue that in a rapidly changing world, algorithmic justice requires systems capable of continuous moral learning, while acknowledging significant implementation challenges and risks.
Keywords: adaptive systems, AI ethics, Algorithmic Fairness, causal inference, dynamic modeling, reinforcement learning
Received: 20 Aug 2025; Accepted: 16 Jan 2026.
Copyright: © 2026 Stillwell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Roger Stillwell
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