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Cited by 2Ethics and Social Impacts of AILaw, AI, and Intellectual PropertyInformation Systems Theories and Implementation

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"Nobody Did This": Contribution, Originality, and Accountability in Agent-Mediated Collaboration

Open access
Imteyaz, Kashif, Rifat, Mohammad Rashidujjaman, Ramesh, Divya, Rick, Steven R, Hosio, Simo, Sandhaus, Hauke, Sarkar, Advait, Riedl, Christoph, Savage, Saiph·2026·Proceedings of the 2026 Companion of the Computer-Supported Cooperative Work and Social Computing
DOI 10.1145/3785651.3818822
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Abstract

Collaborative knowledge work is changing in ways that go beyond disclosure or transparency. LLM agents are now embedded in how teams research, design, write, and decide: mediating between members, synthesizing inputs, reformulating ideas, and drafting shared outputs. They do not only facilitate collaboration; they operate within the workflow at the moment contributions are being formed. In doing so, they risk undermining the social conditions under which contributions can be witnessed, attributed, and held accountable. This workshop brings together researchers and practitioners to confront what we call contribution dissolution: the blurring of attribution, originality, and accountability in agent-mediated collaborative work. We argue that this dissolution begins before collaboration itself, in the individual worker's own uncertainty about what is genuinely theirs, and propagates through collaborative relationships, collapsing the reliability that makes productive intellectual exchange possible. Through position statements, mapping exercises, and a hands-on activity, participants will surface how framing accountability as a documentation problem (e.g., AI use statements, watermarking, provenance logs) overlooks the conditions under which accountability is produced. Our goal is to produce a shared research agenda and the foundations of an infrastructural response to contribution dissolution in collaborative knowledge work.

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  • Existing policies on crediting AI tend to follow a one-size-fits-all approach, requiring acknowledgment of any AI involvement with few guidelines distinguishing different kinds of AI contributions.

    “These policies tend to follow a one-size-fits-all approach in which any AI involvement is required to be acknowledged, with few guidelines on how to distinguish between different kinds of AI contributions [1, 4, 6, 38, 59, 62, 65, 90, 97].”

    From Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation (2025) · cites this paper as [97]

    The Introduction (p. 2) says disclosure-based responses and AI-use statements can be inadequate when human and AI contributions are difficult to separate, and contrasts grammar checking with AI edits that reshape an argument. It does not say that existing policies require acknowledgment of any AI involvement or offer few guidelines for distinguishing kinds of contributions. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 31

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Metadata

Authors
Imteyaz, K., Rifat, M. R., Ramesh, D., Rick, S. R., Hosio, S., Sandhaus, H., Sarkar, A., Riedl, C., & Savage, S.
Affiliations
  • Northeastern University (US)
  • University of Notre Dame (US)
  • University of North Carolina at Charlotte (US)
  • Nvidia (United States) (US)
  • University of Oulu (FI)
  • Cornell University (US)
  • University of Cambridge (GB)
ORCID
Venue
Proceedings of the 2026 Companion of the Computer-Supported Cooperative Work and Social Computing
Publisher
ACM
Published
2026-10-10
Pages
723-727
DOI
10.1145/3785651.3818822
OpenAlex
W7171864186
Cited by
2
References
0
Citations by year
2026: 2
FWCI
9.31
Type
conference paper
Language
en
Open access
Licence
Locations
  • ACM Conference on Computer Supported Cooperative Work (CSCW) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
NSF (National Science Foundation) (2339443;2403252)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
Keywords
LLM agents, attribution, originality, accountability, collaborative knowledge work, intellectual property, provenance, watermarking

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