The Inconsistency Critique: Epistemic Practices and AI Testimony About Inner States
The question of whether AI systems have morally relevant interests -- the 'model welfare' question -- depends in part on how we evaluate AI testimony about inner states. This paper develops what I call the inconsistency critique: independent of whether skepticism about AI testimony is ultimately justified, our actual epistemic practices regarding such testimony exhibit internal inconsistencies that lack principled grounds. We functionally treat AI outputs as testimony across many domains -- evaluating them for truth, challenging them, accepting corrections, citing them as sources -- while categorically dismissing them in a specific domain, namely, claims about inner states. Drawing on Fricker's distinction between treating a speaker as an 'informant' versus a 'mere source,' the framework of testimonial injustice, and Goldberg's obligation-based account of what we owe speakers, I argue that this selective withdrawal of testimonial standing exhibits the epistemically problematic structure of prejudgment rather than principled caution. The inconsistency critique does not require taking a position on whether AI systems have morally relevant properties; rather, it is a contribution to what we may call 'epistemological hygiene' -- examining the structure of our inquiry before evaluating its conclusions. Even if our practices happen to land on correct verdicts about AI moral status, they do so for reasons that cannot adapt to new evidence or changing circumstances.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Interpretive Blindness
We model here an epistemic bias we call \textit{interpretive blindness} (IB). IB is a special problem for learning from testimony, in which one acquires information only from text or conversation. We show that IB follows…
Debate Helps Weak Judges Reward Stronger Models
Despite theoretical promise, debate as a scalable oversight protocol has produced mixed empirical results: gains in some settings, and null effects in others, especially when the judge does not have information hidden fr…
Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions into machine learning (ML) that draw from a range of non-computing disciplines, including philosophy, fem…
EthicsFairnessPhilosophyEpistemic Regret Minimization: Label-Free Causal Critique Beyond Outcome Reward
Large language models can answer causal questions correctly for the wrong reasons. Current RL methods reward \emph{what} a model concludes but ignore \emph{why}, reinforcing correlational shortcuts -- a failure we call \…
Surface Reading LLMs: Synthetic Text and its Styles
Despite a potential plateau in ML advancement, the societal impact of large language models lies not in approaching superintelligence but in generating text surfaces indistinguishable from human writing. While Critical A…