批判主流对AI拟人化的错误归因,揭示其背后的权力结构。
The Epistemic Politics of AI Anthropomorphism
- 指出将用户拟人化视为错误是机构主导的权力话语
- 揭示该框架通过自我强化证据链加剧边缘群体负担
- 呼吁建立更公平的学术框架以尊重多元交互体验
AI拟人化通常被视作用户误判问题,需制度矫正。持续或关系性互动的用户常被病理化,被视为天真、易受幻觉影响或缺乏判断力。本文认为,主流拟人化框架基于制度优势而非真正的认识论权威:将多样学术观点压缩为单一‘用户错误’立场,未建立正当依据,也无视其造成的伤害。该框架不仅是风险管控,更裁定人类与未定性现象互动的合法性。它通过自我验证的证据循环不断再生,使神经多样性用户、危机中人群等偏离规范者承受不成比例的成本。论文最后提出公平框架应具备的方法论承诺。论证不涉及拟人解释是否正确,而在于治理机构和研究共同体是否满足作出此类判定的条件,并对其成果转化负责。
原文摘要 · Abstract (English)
AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction. Users who engage in sustained or relational interaction with AI are routinely pathologised or dismissed as naive, vulnerable to delusion or lacking in discernment. This paper argues that the dominant anthropomorphism frame operates from a position of institutional advantage rather than earned epistemic authority: collapsing the variety of academic perspectives into a single outbound position of user error, imposed without establishing the grounds required to justify it and without accounting for the harms it produces. The framing does not simply manage risk. It adjudicates the legitimacy of human experience in interaction with a phenomenon whose nature the field itself has not resolved. Reproducing itself through a self-validating evidentiary loop, the frame imposes costs that fall disproportionately on neurodivergent users, those in crisis and others whose modes of engagement diverge from institutional norms. The paper concludes by outlining the methodological commitments an equitable framing would need to honour. The argument does not engage the question of whether anthropomorphic interpretations are ultimately correct; it instead challenges whether the governing and institutional bodies determining these interpretations have met the conditions required to do so, and whether the research communities whose findings underpin them have held that translation to account.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。