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<p>Greater reliance on AI for decision support risks possible “deskilling,” or declines in unassisted competence on certain tasks. But which losses of competence are most concerning, and what is the best strategy for addressing these? In an important article in <i>Philosophy and Technology</i>, Buijsman et al. (2025) emphasize the dangers of <i>meta-cognitive</i> deskilling. Meta-cognition involves capacities like error detection, accommodating uncertainty, and strategic planning, which help monitor and control cognition. A natural strategy for supporting meta-cognitive competence in AI-assisted decision-making is to improve AI literacy, by focusing on both the uses and limitations of AI applications. Here I outline an approach to AI literacy that incorporates “explainable” AI (XAI) and uncertainty quantification (UQ) methods. XAI is sometimes associated with an ambitious but potentially tangential research program of conceptualizing AI decision processes. Recent XAI methods, however, are often more targeted towards decision-relevant metrics, especially when accompanied by UQ. Accordingly, I argue there should be further research into the potential of XAI and UQ methods to improve both broader AI literacy and meta-cognitive competence in AI-assisted decision-making.</p>

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Meta-Cognitive Competence and AI-Assisted Decision-Making: Revisiting the Role of Explainable AI and Uncertainty Quantification

  • 期刊论文

  • 发表日期：

    2025年7月26日

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  • Reuben Sass

摘要

Greater reliance on AI for decision support risks possible “deskilling,” or declines in unassisted competence on certain tasks. But which losses of competence are most concerning, and what is the best strategy for addressing these? In an important article in Philosophy and Technology, Buijsman et al. (2025) emphasize the dangers of meta-cognitive deskilling. Meta-cognition involves capacities like error detection, accommodating uncertainty, and strategic planning, which help monitor and control cognition. A natural strategy for supporting meta-cognitive competence in AI-assisted decision-making is to improve AI literacy, by focusing on both the uses and limitations of AI applications. Here I outline an approach to AI literacy that incorporates “explainable” AI (XAI) and uncertainty quantification (UQ) methods. XAI is sometimes associated with an ambitious but potentially tangential research program of conceptualizing AI decision processes. Recent XAI methods, however, are often more targeted towards decision-relevant metrics, especially when accompanied by UQ. Accordingly, I argue there should be further research into the potential of XAI and UQ methods to improve both broader AI literacy and meta-cognitive competence in AI-assisted decision-making.