Preserving Attribution and Accountability in AI-Scale Systems

What problem does the paper answer?

How can authorship and temporal priority be preserved in AI-scale information systems without relying on centralized authority, enforcement mechanisms, or prior adjudication of meaning?

Attribution as infrastructure

The paper reframes attribution as an infrastructural condition for accountability rather than as a downstream matter of credit or enforcement. It identifies attribution collapse as a socio-technical failure mode in AI-mediated systems and introduces BlockClaim as a minimal provenance framework for preserving authored claims as historical anchors.

General-reader summary

This paper argues that attribution is not merely a matter of credit, but a foundational condition for accountability in AI-scale information systems. As AI-mediated systems copy, transform, summarize, and recombine informational material, the links between claims, authors, and moments of assertion can erode. The paper identifies this erosion as attribution collapse and examines why many governance and ethics frameworks depend on provenance conditions they do not themselves preserve. It introduces BlockClaim as a minimal, non-adjudicative provenance framework designed to preserve authorship continuity and temporal priority without relying on centralized authority, enforcement, or prior judgment of truth. The contribution is infrastructural: stabilize the historical trace so that later ethical, legal, scholarly, and institutional evaluation remains possible.

Manuscript abstract

Contemporary AI-mediated information systems operate at scales that can increasingly undermine the preservation of authorship continuity and temporal priority. While debates in AI governance, ethics, and accountability often presuppose the availability of stable attribution, this paper argues that such stability is a fragile infrastructural condition rather than a given. As information is copied, transformed, and recombined through automated systems, the linkage between claims, authors, and moments of assertion can progressively erode.

This paper examines attribution collapse as a foundational socio-technical problem amplified by AI-scale systems and asks how authorship and temporal priority can be preserved without reliance on centralized authority, enforcement mechanisms, or prior adjudication. Adopting a conceptual and infrastructural approach, it identifies recurring failure modes in existing attribution practices and the design requirements for provenance mechanisms that can persist under conditions of automation, transformation, and scale.

On this basis, the paper introduces BlockClaim as a minimal provenance framework designed to preserve attribution continuity and temporal integrity while remaining neutral with respect to truth, ethics, and enforcement. Rather than proposing a comprehensive governance solution, the framework is positioned as an enabling condition for accountability, supporting downstream ethical, legal, and institutional processes by stabilizing the historical trace of claims. The paper concludes by reflecting on the implications of non-adjudicative provenance for philosophy of technology and AI-mediated social systems.

Terms to remember

The future needs a memory

AI systems can preserve fragments of content while weakening the record of who asserted what, when, and in what context. Without that record, accountability becomes harder to ground. This paper matters because it treats provenance as a precondition for later evaluation, disagreement, responsibility, and institutional learning.

Place in the larger research program

This article sits near the beginning of the attribution, accountability, and continuity sequence. It establishes the importance of preserving attribution before later papers extend the problem toward external evaluability, distributed witnessing, historical continuity, and retrieval under computational mediation.

Submission and revision path

• Original numbering begins at 03 for portfolio filenames.
• Early submission path included AI & Society, followed by Discover Artificial Intelligence.
• The paper underwent a major revision before publication.
• The final published article became the first major public anchor for the attribution and accountability line of the research program.

Accountability depends upon preserving attribution.