AAAI

The Epistemic Politics of AI Anthropomorphism

AI拟人性的认知政治

Donna M Bye, Levin Kuhlmann

AI总结 本文批判AI拟人性主导框架基于机构优势而非可靠认知权威，指出其对神经多样性等用户的不公，最后提出公平框架的方法论承诺。

Comments 20 pages, 3 figures, 9 tables. Extended version, including supplementary materials, of a paper to appear in the Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2026

When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Models

当可解释性分配不均：混合可解释模型中的公平性

Ziba Jabbar Zare, Ulrich Aïvodji, Julien Ferry, Thibaut Vidal

机构 * Department of Mechanical, Industrial and Aerospace Engineering, Concordia University（康科迪亚大学机械、工业与航空航天工程系） 纠错

AI总结 针对混合可解释模型将不同群体不均地分配给可解释组件与黑箱组件的问题，提出可解释性覆盖差异（ICD）度量，并通过约束缓解不公平性。

Comments This work has been accepted for publication at the 2026 AAAI/ACM Conference on AI, Ethics, and Society (AIES)

HyWA: Architecture-Preserving Personalized Voice Activity Detection for Full-Duplex Voice Assistants

HyWA: 基于超网络的个性化语音活动检测权重适应

Hamed Jafarzadeh Asl, Amin Edraki, Mahsa Ghazvini Nejad, Masoud Asgharian, Mohammadreza Sadeghi, Yuanhao Yu, Vahid Partovi Nia

机构 * Huawei Noah’s Ark Lab（华为诺亚实验室） ； McGill University（麦吉尔大学） 纠错

AI总结 HyWA通过超网络为VAD模型的选定层生成个性化权重，提升个性化语音活动检测的性能和部署效率。

Comments Submitted to AAAI/IAAI 2027. Hamed, Amin, and Mahsa contributed equally to this work