cs.CY · 2026-08-13 · No. 83

Computers and Society, 2026-08-13.

4 new papers in cs.CY. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

4 entries
  1. 01

    Co-constructing sociotechnical AI governance: participatory system mapping using algorithm registers

    Íñigo de Troya, Maurus Enbergs, Neelke Doorn, Roel Dobbe

    cs.CY · cs.AI · eess.SY

    Algorithm registers have been championed as a means of providing transparency on the use of algorithms in public services. Yet potential publics differ in their expectations of what should be made transparent and how, as well as in their interest in and ability to parse the information currently published in the registers. Moreover, it remains unclear how these instruments can represent the sociotechnical systems in which these algorithms are...

    arxiv.org/abs/2608.12166 · PDF

  2. 02

    No One to Blame: A Framework of Constitutive AI Unaccountability

    Long Hoang Nguyen, Eva Späthe, Sebastian Lins, Ali Sunyaev

    cs.CY · cs.AI

    The increasing deployment of autonomous, agentic AI systems challenges traditional accountability mechanisms. Existing research predominantly frames AI accountability gaps as barriers that can be overcome through better standards, transparency, and institutional reform. We argue that this framing is insufficient: certain configurations of actors, systems, and institutions render AI accountability conceptually unachievable regardless of...

    arxiv.org/abs/2608.12104 · PDF

  3. 03

    Benchmark-Based Comparative Assessment of Publicly Benchmarked Indian Foundation Models: A Capability and Evaluation-Maturity Framework

    Avinash Agarwal, Vridhi Jain

    cs.CY · cs.AI · cs.HC

    Governments increasingly fund indigenous foundation models to strengthen national AI capability, digital sovereignty, and multilingual computing. Assessing the progress of such national ecosystems is complicated by inconsistent benchmark reporting, proprietary evaluation methodologies, and rapidly evolving model releases. This paper presents a structured, benchmark-based comparative assessment of publicly benchmarked Indian foundation models...

    arxiv.org/abs/2608.11891 · PDF

  4. 04

    Toward Meaningful Transparency for AI Chatbots: Disclosing Persuasive Intent Reduces Persuasion

    Adrian Rauchfleisch, Andreas Jungherr

    cs.CY · cs.AI · cs.HC

    The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content provenance and AI involvement. But the effects of such disclosures remain uncertain. We test two disclosure approaches in their impact on an AI chatbot's persuasive appeal. In a preregistered experiment, 1,500 UK adults held a short conversation with a persuasive chatbot about one of 60 policy...

    arxiv.org/abs/2608.11794 · PDF

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