Epistemic Deference to AI

TL;DR

This paper explores when and how humans should defer to AI outputs, proposing a nuanced approach that balances AI reliability with human oversight to address epistemic concerns in high-stakes decisions.

Contribution

It introduces the total evidence view of AI deference, offering a balanced framework that mitigates risks of uncritical reliance on AI systems.

Findings

AEAs are justified in high-reliability contexts

Total evidence approach preserves human engagement

Provides criteria for justified AI deference

Abstract

When should we defer to AI outputs over human expert judgment? Drawing on recent work in social epistemology, I motivate the idea that some AI systems qualify as Artificial Epistemic Authorities (AEAs) due to their demonstrated reliability and epistemic superiority. I then introduce AI Preemptionism, the view that AEA outputs should replace rather than supplement a user's independent epistemic reasons. I show that classic objections to preemptionism - such as uncritical deference, epistemic entrenchment, and unhinging epistemic bases - apply in amplified form to AEAs, given their opacity, self-reinforcing authority, and lack of epistemic failure markers. Against this, I develop a more promising alternative: a total evidence view of AI deference. According to this view, AEA outputs should function as contributory reasons rather than outright replacements for a user's independent epistemic considerations. This approach has three key advantages: (i) it mitigates expertise atrophy by keeping human users engaged, (ii) it provides an epistemic case for meaningful human oversight and control, and (iii) it explains the justified mistrust of AI when reliability conditions are unmet. While demanding in practice, this account offers a principled way to determine when AI deference is justified, particularly in high-stakes contexts requiring rigorous reliability.