arXiv:2608. 15326v1 Announce Type: new Abstract: Artificial intelligence (AI) benchmarks are not neutral tools of evaluation but socio-technical artefacts that shape competition, power, and research priorities within AI.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2606. 15708v1 Announce Type: new Abstract: Welcome to the ninth edition of the AI Index report.
The paper argues that AI should be evaluated not only by principles but by concrete protocols that translate commitments into roles, requirements, records, oversight, and assessment. It introduces a rupture test linking institutional baselines to system evaluation, and distinguishes evidence‑bounded deployment from measurement‑bounded governance. The authors propose the RISE AI architecture to make bounded, evidence‑based claims about Responsibility, Inclusivity, Safety, and Empowerment, emphasizing the need for engineering, institutional repair, and ongoing moral judgment.
arXiv:2608. 12104v1 Announce Type: cross Abstract: The increasing deployment of autonomous, agentic AI systems challenges traditional accountability mechanisms.
arXiv:2608. 08408v1 Announce Type: cross Abstract: Logics of abstraction in computational AI research often push important forms of knowledge and reflection aside: dominant standards of legitimacy separate from lived experience of harm; the goals of work misalign with the practices that operationalize them; and career demands crowd out critical reflection.
arXiv:2606. 31755v1 Announce Type: cross Abstract: Research on artificial intelligence (AI) in the public sector often treats "AI" as a single category, neglecting technical distinctions between different AI systems.
arXiv:2606. 09408v1 Announce Type: cross Abstract: We present an ethnographic study of an alternative approach to data work, developed by a civic-tech initiative that builds datasets for training and benchmarking online safety systems.