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arxiv: 2605.09793 · v1 · submitted 2026-05-10 · 💻 cs.HC

Recognition: 2 theorem links

· Lean Theorem

Push and Pushback in Contesting AI: Demands for and Resistance to Accountability

Jae Woo Lee, Jatinder Singh, Lucas Lichner, Renwen Zhang, Sijia Xiao, Yulu Pi

Pith reviewed 2026-05-12 02:38 UTC · model grok-4.3

classification 💻 cs.HC
keywords AI contestationaccountabilityinstitutional resistancethematic analysiscase studiesAI governancedemands for redressresponse tactics
0
0 comments X

The pith

AI contestation unfolds as an iterative push for accountability from affected groups that institutions often resist through targeted tactics.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper analyzes 43 real-world cases where communities, workers, and advocates direct demands at AI developers and deployers for redress or change. It frames these contests as accountability-seeking processes in which challengers from below press actors from above, who then accept, resist, or sidestep the demands. Thematic analysis yields categories for the strategies challengers use, the response tactics institutions employ, the resulting outcome types, and the contextual factors that influence both sides. The account shows that accountability is frequently limited in practice rather than fully granted. These categories supply concrete guidance for anticipating institutional evasion and strengthening future contestation efforts.

Core claim

Our thematic analysis of 43 cases produces empirically grounded categories of contestation strategies, institutional response tactics, outcome types, and the contextual factors that shape them. Situating the work in a relational model of accountability, we treat contestation as a dynamic process in which actors from below direct explicit demands at actors from above, who respond by accepting, resisting, or circumventing accountability. We show that those being contested routinely deploy a range of strategies to limit their accountability.

What carries the argument

Thematic analysis of 43 real-world AI contestation cases, organized through a relational accountability model that distinguishes demands from below and responses from above.

Load-bearing premise

The 43 chosen cases capture representative patterns of AI contestation without major selection bias or interpretive subjectivity in the thematic coding.

What would settle it

A larger, independently sampled collection of AI contestation cases that produces substantially different strategy categories, response tactics, or outcome patterns would undermine the claimed generality of the framework.

Figures

Figures reproduced from arXiv: 2605.09793 by Jae Woo Lee, Jatinder Singh, Lucas Lichner, Renwen Zhang, Sijia Xiao, Yulu Pi.

Figure 1
Figure 1. Figure 1: A characterization of AI contestation dynamics, capturing the two-sided accountability relationship between actors [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: We identify four types of contesters, five dimensions of AI they contest, and six institutional response strategies. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
read the original abstract

As AI becomes increasingly embedded in daily life, it has been shown to fail critically, cause harm, and spark public controversy, prompting affected communities, workers, and public-interest groups to contest it. Yet how these contestations unfold in practice remains underexplored. We address this gap by developing an empirically grounded account of AI contestation dynamics. We do so through a thematic analysis of 43 real-world cases in which affected actors direct demands toward those responsible for AI development and deployment, seeking redress, influence, or changes to AI practices. Situating our work within Bovens's relational model of accountability, we conceptualize contestation as accountability-seeking: a dynamic, iterative process in which actors "from below" direct explicit demands at actors "from above," who respond by accepting, resisting, or circumventing accountability. Our analysis produces empirically grounded categories of contestation strategies, institutional response tactics, outcome types, and the contextual factors that shape them, illuminating how accountability is pursued and evaded in practice. We show that those being contested often deploy a range of strategies to limit their accountability. Based on these insights, we offer guidance for researchers, policymakers, advocates, and other stakeholders seeking to support effective AI contestation, with particular attention to anticipating and countering institutional strategies used to evade accountability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper conducts a thematic analysis of 43 real-world cases of AI contestation to develop an empirically grounded framework of contestation dynamics, including strategies used by affected actors, response tactics by institutions, outcome types, and contextual factors. Drawing on Bovens's relational accountability model, it conceptualizes contestation as accountability-seeking and provides guidance for supporting effective contestation while countering evasion strategies.

Significance. If the methodological details support the robustness of the categories, this study offers important contributions to understanding practical AI accountability processes in HCI and related fields. It provides concrete, empirically derived insights into how accountability is pursued and resisted, which can inform advocacy, policy, and research on AI governance. The use of an established theoretical model with independent cases is a strength.

major comments (2)
  1. [Methods] Methods section: The description of how the 43 cases were selected (including any sampling frame, inclusion/exclusion criteria, or efforts to mitigate bias such as toward high-profile Western incidents) is not provided in sufficient detail. This directly undermines verification of the central claim that the analysis produces representative, empirically grounded categories of contestation strategies and institutional tactics.
  2. [Methods] Methods section: Details on the thematic analysis process, such as the inductive coding procedure, number of coders, and any inter-rater reliability metrics, are absent. Without these, the link between the raw cases and the derived categories cannot be fully assessed for subjectivity or replicability, which is load-bearing for the paper's assertion of illuminating accountability dynamics in practice.
minor comments (1)
  1. [Abstract] Abstract: A short statement acknowledging the qualitative, non-probability sample and its implications for generalizability would help readers interpret the scope of the categories and guidance offered.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive feedback on our manuscript. The comments on the methods section are well-taken and point to areas where greater transparency will strengthen the paper. We have revised the methods section in response to both major comments, adding the requested details on case selection and the thematic analysis process. These changes improve the replicability and credibility of our empirically grounded framework without altering the core findings or contributions.

read point-by-point responses

  1. Referee: [Methods] Methods section: The description of how the 43 cases were selected (including any sampling frame, inclusion/exclusion criteria, or efforts to mitigate bias such as toward high-profile Western incidents) is not provided in sufficient detail. This directly undermines verification of the central claim that the analysis produces representative, empirically grounded categories of contestation strategies and institutional tactics.

    Authors: We agree that the original methods section lacked sufficient detail on case selection. The 43 cases were drawn from publicly available sources including news reports, advocacy organization publications, and documented incidents of AI-related harms and contestation. To address this, we have expanded the methods section to explicitly describe the sampling approach: a purposive sampling frame focused on cases involving explicit demands directed at AI developers or deployers by affected actors (communities, workers, or public-interest groups). Inclusion criteria required evidence of contestation (e.g., public campaigns, legal actions, or organized protests seeking accountability), while exclusion criteria omitted purely technical failures without contestation or cases lacking sufficient public documentation. We also describe efforts to mitigate Western/high-profile bias by including searches across multiple languages and regions, incorporating cases from the Global South where available. This revision directly supports the claim of empirically grounded categories by making the selection process verifiable. revision: yes

  2. Referee: [Methods] Methods section: Details on the thematic analysis process, such as the inductive coding procedure, number of coders, and any inter-rater reliability metrics, are absent. Without these, the link between the raw cases and the derived categories cannot be fully assessed for subjectivity or replicability, which is load-bearing for the paper's assertion of illuminating accountability dynamics in practice.

    Authors: We acknowledge that the original manuscript did not provide adequate detail on the thematic analysis procedure. The analysis followed an inductive approach: initial open coding of all 43 cases to identify recurring patterns in demands, institutional responses, outcomes, and contextual factors, followed by axial coding to group these into higher-level categories aligned with Bovens's relational accountability model, with iterative refinement through team discussions. In the revised manuscript, we now specify that the coding was conducted by two authors independently on a subset of cases, with all authors reviewing and discussing discrepancies to reach consensus; we also note the absence of formal inter-rater reliability statistics (common in qualitative thematic analysis) and describe steps taken to enhance rigor, such as maintaining an audit trail of coding decisions and member-checking categories against the data. This addition addresses concerns about subjectivity and replicability while preserving the qualitative, interpretive nature of the study. revision: yes

Circularity Check

0 steps flagged

No circularity: categories derived inductively from independent cases using external model

full rationale

The paper performs a thematic analysis of 43 real-world cases to generate categories of contestation strategies, institutional responses, outcomes, and contextual factors. These are situated in Bovens's relational accountability model, which is an external reference rather than a self-citation. No equations, fitted parameters, self-definitional constructs, or load-bearing self-citations appear in the derivation chain. The central claims rest on empirical coding of independent cases rather than reducing to the inputs by construction, making the analysis self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the applicability of Bovens's relational model to AI and the sufficiency of the 43 cases for generating generalizable categories; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Bovens's relational model of accountability applies directly to AI contestation as accountability-seeking from below
    Invoked to conceptualize the dynamic process of demands and responses.

pith-pipeline@v0.9.0 · 5548 in / 1143 out tokens · 61954 ms · 2026-05-12T02:38:35.975336+00:00 · methodology

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