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<p>We advance a novel version of the No Miracles Argument (NMA), tailored explicitly for AI, on which predictive success provides support for the existence of hidden quasi-representations—entities that would count as representations if they embodied aboutness relations. Our primary claim is comparative: reframing the AI-specific NMA in terms of quasi-representation yields a weaker, and thereby more plausible, argument than our previous representation-based formulation, one that inherits whatever force no‑miracles reasoning has in the traditional case. An added advantage is that our new NMA is compatible with selective realism and anti-realism alike, because quasi‑representation can be cashed out in thin terms. To illuminate our approach, we consider how quasi-representations might underpin the predictive accuracy of GraphCast, a cutting-edge AI system used for global weather forecasting. We then defend our quasi-representational NMA against concerns of underspecification and triviality. Finally, we explore the consequences for explainable AI (XAI) if our NMA is sound.</p>

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Explaining AI’s successes: A no miracles argument for quasi-representations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月15日

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  • Darrell P. Rowbottom,
  • André Curtis-Trudel

摘要

We advance a novel version of the No Miracles Argument (NMA), tailored explicitly for AI, on which predictive success provides support for the existence of hidden quasi-representations—entities that would count as representations if they embodied aboutness relations. Our primary claim is comparative: reframing the AI-specific NMA in terms of quasi-representation yields a weaker, and thereby more plausible, argument than our previous representation-based formulation, one that inherits whatever force no‑miracles reasoning has in the traditional case. An added advantage is that our new NMA is compatible with selective realism and anti-realism alike, because quasi‑representation can be cashed out in thin terms. To illuminate our approach, we consider how quasi-representations might underpin the predictive accuracy of GraphCast, a cutting-edge AI system used for global weather forecasting. We then defend our quasi-representational NMA against concerns of underspecification and triviality. Finally, we explore the consequences for explainable AI (XAI) if our NMA is sound.