Browse the critiques

14 of 14 critiques

• needs reviewEconomics & financeseverity lowabstract only

  Critique of “AI meets politics: Examining the effects of different targeting strategies across 15 countries”

  Sanne Kruikemeier, Svenja Schäfer, Alice Hamilton et al. · New Media & Society · 2026-06-04

  A well-powered, cross-national experiment whose central positive finding is credibly identified; the cautions, legible from the abstract, are the power-dependent null claims and the generalisation from attitudinal outcomes to electoral influence. Severity low.

• needs reviewManagement, IS & marketingseverity lowabstract only

  Critique of “Artificial Collusion: Examining Supracompetitive Pricing by Q-Learning Algorithms”

  Arnoud den Boer, Janusz M Meylahn, Maarten Pieter Schinkel · Management Science · 2026-06-09

  A valuable, carefully argued correction to an over-strong prior on algorithmic collusion. The caution, visible from the abstract, is that the general policy reassurance outruns the Q-learning-specific analysis and sits awkwardly beside the paper's own hedge. Severity low; the concern is the breadth of the policy inference, not the technical analysis.

• ✓ calibratedEconomics & financeseverity moderateopen access

  Critique of “Generative AI at Work”

  Erik Brynjolfsson, Danielle Li, Lindsey R. Raymond · The Quarterly Journal of Economics · 2025-02-04

  A genuinely important, well-executed empirical study whose central productivity and heterogeneity findings are well supported and whose secondary mechanisms are appropriately hedged by the authors. The principal caveats are non-random rollout timing, reliance on LLM-derived secondary outcomes, and proprietary underlying data that cannot be independently audited. Severity moderate; publish.

• needs reviewManagement, IS & marketingseverity lowabstract only

  Critique of “Can ChatGPT Kill User-Generated Q&A Platforms?”

  Junzhi Xue, Lizheng Wang, Jinyang Zheng et al. · Information Systems Research · 2026-05-21

  A careful, quantified single-platform study whose own conclusion is suitably measured; the cautions, visible from the abstract, are the gap between the 'kill' framing and the coexistence finding, and a causal reading anchored to introduction timing. Severity low; the substantive claims are hedged and the concerns are about framing and external validity.

• ✓ calibratedOther / interdisciplinaryseverity moderateopen access

  Critique of “Scaffolding Human–AI Collaboration: A Field Experiment on Behavioral Protocols and Cognitive Reframing”

  Alex Farach, Alexia Cambon, Lev Tankelevitch et al. · arXiv (working paper) · 2026-04-09

  A transparent, well-reported field experiment on an important question whose causal claims are appropriately bounded by limitations the authors disclose: an AM/PM session confound, differential attrition, an LLM-graded length-sensitive outcome, no pre-registration, and a narrower set of belief-change effects surviving correction. These are identification, statistics and measurement cautions, openly stated. Severity moderate; the work is candid, and the signals are real but provisional.

• needs reviewEconomics & financeseverity moderateabstract only

  Critique of “From rule of law to rule of algorithm: Generative Artificial Intelligence's threat to democracy”

  A.T. Kingsmith · Big Data & Society · 2026-05-30

  A timely governance commentary whose central claims outrun what a conceptual argument can establish: the 'qualitative break' and accountability-dissolution claims are asserted, not evidenced, and the framing is unscoped. These are claim-evidence and overclaiming cautions proper to the genre. Severity moderate.

• needs reviewEconomics & financeseverity lowabstract only

  Critique of “Generative AI, propaganda, and digital authoritarianism: Comparative insights from six democratically weakened countries”

  Gabrielle D. Beacken, Inga K Trauthig, Samuel Woolley · Big Data & Society · 2026-06-01

  A strong, anti-determinist comparative study whose descriptive findings are well supported; the cautions, visible from the abstract, are the leap from elite-adoption interviews to causal claims about democratic erosion, and the reproducibility of interpretive thematic analysis. Severity low.

• needs reviewManagement, IS & marketingseverity lowabstract only

  Critique of “Made With AI: Consumer Engagement with Social Media Containing AI Disclosures”

  Stephan Carney, Ignacio Riveros, Stephanie Tully · Journal of Consumer Research · 2026-05-05

  A methodologically strong, policy-relevant study whose central effect is well supported; the cautions, visible from the abstract, are single-platform field evidence and the foregrounding of one mechanism, so the disclosure-design implications should stay close to what was tested. Severity low.

• ✓ calibratedOther / interdisciplinaryseverity moderateopen access

  Critique of “The Impact of AI on Developer Productivity: Evidence from GitHub Copilot”

  Sida Peng, Eirini Kalliamvakou, Peter Cihon et al. · arXiv (working paper) · 2023-02-13

  A cleanly-identified RCT whose internal causal claim is well-supported for its task; the cautions, all visible in the full text, are the imprecision of the headline estimate, the narrow single-task/freelancer scope (which the authors concede), the speed-not-quality outcome, and the lack of independent auditability given developer-run instrumentation. Severity moderate.

• needs reviewCommunication & mediaseverity lowabstract only

  Critique of “Refusal as silence: Gendered disparities in Vision-Language Model responses”

  Sha Luo, S Kim, Zening Duan et al. · New Media & Society · 2026-05-04

  A well-designed identity audit with a striking, policy-relevant finding; the cautions, visible from the abstract, are reproducibility (a non-deterministic, version-dependent model with no stated run protocol) and single-model/single-task scope. Severity low.

• needs reviewManagement, IS & marketingseverity lowabstract only

  Critique of “The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork”

  Fabrizio Dell’Acqua, Charles Ayoubi, Hila Lifshitz‐Assaf et al. · Organization Science · 2026-06-12

  A well-designed field experiment whose internal result is credibly identified for its setting; the principal caution, visible from the abstract alone, is the generalisation from one firm and one task family to knowledge work in general, plus reliance on self-report for the social claim. Severity low; the design is sound and the over-reach is in framing, not method.

• ✓ calibratedCommunication & mediaseverity lowabstract only

  Critique of “The politics of artificial intelligence alignment: Public reactions to AI moderation in the case of Google’s Gemini”

  Adrian Rauchfleisch, Andreas Jungherr · New Media & Society · 2026-06-01

  A preregistered, well-theorised experiment whose main effect is credible for its primary stimulus; the cautions, visible from the abstract, are the reliance on pooling across a significant and a non-significant condition and the single-product scope. Severity low.

• needs reviewManagement, IS & marketingseverity moderateabstract only

  Critique of “Unraveling Generative AI from a Human Intelligence Perspective: A Battery of Experiments”

  Wen Wang, Siqi Pei, Tianshu Sun · Information Systems Research · 2026-05-08

  An ambitious, policy-facing evaluation framework whose central wording over-reads benchmark performance as 'intelligence' and whose forecasting claim outruns its experimental basis. These are construct-validity and generalisation concerns legible from the abstract; deeper methodological assessment would require the full text. Severity moderate.

• illustrativeManagement, IS & marketingseverity highopen access

  Critique of “Generative AI Adoption and Organizational Productivity: Evidence from 500 Firms”

  A. Researcher, B. Co-author · Journal of Strategic Management and Technology · 2026

  The paper should be read as a useful but overstated contribution. Its descriptive findings may help map AI adoption, but its causal, policy and AGI claims require substantial weakening. Severity is High: the central causal and policy claims need weakening, but the descriptive core survives. Confidence is Medium: the assessment is well grounded in the design, while some judgements about magnitude depend on materials not fully available.