Computational reliabilism is a concept drawn from epistemology, specifically applied to the domain of human-AI interaction and justificatory AI. It addresses key theoretical challenges in understanding human-AI complementarity, which traditionally has been formalized merely as a post hoc indicator of relative predictive accuracy. By leveraging the philosophical discourse on reliabilism, this framework argues that historical instances where human-AI teams outperform either alone serve as evidence that the interaction constitutes a reliable epistemic process for a given predictive task. This approach moves beyond simple accuracy metrics to consider the epistemic alignment and trustworthiness of the combined human-AI system. It is primarily used by researchers in human-AI interaction, AI ethics, and computational epistemology to develop more robust and theoretically grounded methods for designing, evaluating, and justifying AI-assisted decision-making systems.
Computational reliabilism is a philosophical approach that helps us understand why humans and AI working together can be better than either alone. It suggests that when a human-AI team performs well, it's because their combined process for making decisions is reliable, not just accurate. This framework helps researchers build more trustworthy AI systems.
epistemological reliabilism (applied to computation)
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