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<p>This paper develops a typology of AI fairness grounded in speculative realism and object-oriented ontology, introducing four paradigms: Anthropocentric Plateau Fairness (APF), Anthropocentric Stratigraphic Fairness (ASF), Flat Speculative Egalitarianism (FSE), and Hierarchical Speculative Onto-fairness (HSO). By integrating insights from philosophy, social theory, and computer science, the typology elucidates implicit ontological assumptions underlying fairness debates and expands ethical consideration beyond traditional human-centered perspectives. The paper articulates theoretical propositions predicting how AI systems designed within these paradigms would operate, highlighting tensions, trade-offs, and opportunities associated with each. Finally, the paper proposes a paradigm selection matrix to guide practitioners in applying these frameworks to specific algorithmic contexts. This speculative framework offers a structured basis for interdisciplinary dialogue, encouraging AI fairness research to address deeper structural, ecological, and ethical dimensions.</p>

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A speculative realist typology of AI fairness: an Object-Oriented Onto-Ethics

  • 期刊论文

  • 发表日期：

    2026年2月9日

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  • Abdullah Muhammad Dhrubo,
  • Awais Ahmed Brohi

摘要

This paper develops a typology of AI fairness grounded in speculative realism and object-oriented ontology, introducing four paradigms: Anthropocentric Plateau Fairness (APF), Anthropocentric Stratigraphic Fairness (ASF), Flat Speculative Egalitarianism (FSE), and Hierarchical Speculative Onto-fairness (HSO). By integrating insights from philosophy, social theory, and computer science, the typology elucidates implicit ontological assumptions underlying fairness debates and expands ethical consideration beyond traditional human-centered perspectives. The paper articulates theoretical propositions predicting how AI systems designed within these paradigms would operate, highlighting tensions, trade-offs, and opportunities associated with each. Finally, the paper proposes a paradigm selection matrix to guide practitioners in applying these frameworks to specific algorithmic contexts. This speculative framework offers a structured basis for interdisciplinary dialogue, encouraging AI fairness research to address deeper structural, ecological, and ethical dimensions.