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Conflict Resolution Quarterly
Article . 2025 . Peer-reviewed
License: CC BY
Data sources: Crossref
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ZENODO
Article . 2025
License: CC BY SA
Data sources: ZENODO
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AI and the Future of Disputing: Naming, Blaming, Claiming, and Preventing

Authors: Ethan Katsh; Talia Schwartz‐Maor; Orna Rabinovich‐Einy;

AI and the Future of Disputing: Naming, Blaming, Claiming, and Preventing

Abstract

ABSTRACT This paper explores the transformative impact of artificial intelligence (AI) on dispute resolution mechanisms. Our analysis builds on the longstanding framework for explaining the stages through which disputes evolve: the “naming, blaming, claiming” model by Felstiner, Abel, and Sarat (1981). Drawing on the evolution of online dispute resolution (ODR) and insights gained from the application of AI in healthcare, we examine how technological advancements challenge the traditional trajectory of dispute resolution. In particular, we propose a re‐categorization of the stages of disputing, emphasizing the emergence of a “prevention” stage facilitated by AI. By incorporating AI into the existing theoretical framework, we argue that dispute processes are not only restructured at the individual level, in terms of how claims are raised and addressed, but also at a systemic level, in the way data is leveraged to identify patterns and potential sources of conflict.

Related Organizations
Keywords

online dispute resolution (ODR), prevention, algorithmic dispute resolution, alternative dispute resolution (ADR), artificial intelligence (AI)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Average
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