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What happens when reviewers receive AI feedback in their reviews? [9.57486570505445]
Advocates see AI's potential to reduce reviewer burden and improve quality, while critics warn of risks to fairness, accountability, and trust.<n>At ICLR 2025, an official AI feedback tool was deployed to provide reviewers with post-review suggestions.<n>This work contributes the first empirical evidence of such an AI tool in a live review process.
arXiv Detail & Related papers (2026-02-14T15:22:33Z)
Full Disclosure, Less Trust? How the Level of Detail about AI Use in News Writing Affects Readers' Trust [10.22272389430846]
Findings show that not all AI disclosures lead to a transparency dilemma, but instead reflect a trade-off between readers' desire for more transparency and their trust in AI-assisted news content.
arXiv Detail & Related papers (2026-01-14T16:45:45Z)
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Self-diagnosis is unreliable on complex tasks unless aided by reliable external feedback.<n>We introduce a new collaborative MAD protocol, termed ColMAD, that reframes MAD as a non-zero sum game.<n>We show that ColMAD significantly outperforms previous competitive MAD by 19%.
arXiv Detail & Related papers (2025-10-23T19:46:00Z)
CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection [60.52240468810558]
We introduce CoCoNUTS, a content-oriented benchmark built upon a fine-grained dataset of AI-generated peer reviews.<n>We also develop CoCoDet, an AI review detector via a multi-task learning framework, to achieve more accurate and robust detection of AI involvement in review content.
arXiv Detail & Related papers (2025-08-28T06:03:11Z)
Identity Theft in AI Conference Peer Review [50.18240135317708]
We discuss newly uncovered cases of identity theft in the scientific peer-review process within artificial intelligence (AI) research.<n>We detail how dishonest researchers exploit the peer-review system by creating fraudulent reviewer profiles to manipulate paper evaluations.
arXiv Detail & Related papers (2025-08-06T02:36:52Z)
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Bullshit, as conceptualized by philosopher Harry Frankfurt, refers to statements made without regard to their truth value.<n>We introduce the Bullshit Index, a novel metric quantifying large language model's indifference to truth.<n>We observe prevalent machine bullshit in political contexts, with weasel words as the dominant strategy.
arXiv Detail & Related papers (2025-07-10T07:11:57Z)
AI-washing: The Asymmetric Effects of Its Two Types on Consumer Moral Judgments [0.0]
This paper introduces AI-washing as overstating (deceptive boasting) or understating (deceptive denial) a company's real AI usage.<n>A 2x2 experiment examines how these false claims affect consumer attitudes and purchase intentions.
arXiv Detail & Related papers (2025-07-06T11:28:45Z)
Veracity: An Open-Source AI Fact-Checking System [11.476157136162989]
This paper introduces Veracity, an open-source AI system designed to combat misinformation through transparent and accessible fact-checking.<n>Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications.
arXiv Detail & Related papers (2025-06-18T18:24:59Z)
The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
arXiv Detail & Related papers (2025-06-09T18:37:14Z)
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We study whether AI debate can guide biased judges toward the truth by having two AI systems debate opposing sides of controversial COVID-19 factuality claims.<n>In our human study, we find that debate-where two AI advisor systems present opposing evidence-based arguments-consistently improves judgment accuracy and confidence calibration.<n>In our AI judge study, we find that AI judges with human-like personas achieve even higher accuracy (78.5%) than human judges (70.1%) and default AI judges without personas (69.8%)
arXiv Detail & Related papers (2025-06-02T19:01:53Z)
Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing [55.2480439325792]
Misclassification can lead to false plagiarism accusations and misleading claims about AI prevalence in online content. We systematically evaluate eleven state-of-the-art AI-text detectors using our AI-Polished-Text Evaluation dataset. Our findings reveal that detectors frequently misclassify even minimally polished text as AI-generated, struggle to differentiate between degrees of AI involvement, and exhibit biases against older and smaller models.
arXiv Detail & Related papers (2025-02-21T18:45:37Z)
Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness [13.63944785085617]
We investigate how capability disclosure influenced behaviors with a superhuman game AI in competitive StarCraft II scenarios. Our results reveal transparency is double-edged: while disclosure could alleviate suspicion, it also provoked frustration and strategic defeatism. This work demonstrates that transparency is not a cure-all; successfully leveraging disclosure to enhance trust and accountability requires careful tailoring to user characteristics.
arXiv Detail & Related papers (2025-01-31T05:50:50Z)
On scalable oversight with weak LLMs judging strong LLMs [67.8628575615614]
We study debate, where two AI's compete to convince a judge; consultancy, where a single AI tries to convince a judge that asks questions. We use large language models (LLMs) as both AI agents and as stand-ins for human judges, taking the judge models to be weaker than agent models.
arXiv Detail & Related papers (2024-07-05T16:29:15Z)
Missci: Reconstructing Fallacies in Misrepresented Science [84.32990746227385]
Health-related misinformation on social networks can lead to poor decision-making and real-world dangers. Missci is a novel argumentation theoretical model for fallacious reasoning. We present Missci as a dataset to test the critical reasoning abilities of large language models.
arXiv Detail & Related papers (2024-06-05T12:11:10Z)
Truth-Aware Context Selection: Mitigating Hallucinations of Large Language Models Being Misled by Untruthful Contexts [31.769428095250912]
Large Language Models (LLMs) are easily misled by untruthful contexts provided by users or knowledge augmentation tools. We propose Truth-Aware Context Selection (TACS) to adaptively recognize and mask untruthful context from the inputs. We show that TACS can effectively filter untruthful context and significantly improve the overall quality of LLMs' responses when presented with misleading information.
arXiv Detail & Related papers (2024-03-12T11:40:44Z)
Responsible AI Considerations in Text Summarization Research: A Review of Current Practices [89.85174013619883]
We focus on text summarization, a common NLP task largely overlooked by the responsible AI community. We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022. We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals.
arXiv Detail & Related papers (2023-11-18T15:35:36Z)
Fact-checking information from large language models can decrease headline discernment [6.814801748069122]
We investigate the impact of fact-checking information generated by a popular large language model on belief in, and sharing intent of, political news headlines. We find that this information does not significantly improve participants' ability to discern headline accuracy or share accurate news. Our findings highlight an important source of potential harm stemming from AI applications.
arXiv Detail & Related papers (2023-08-21T15:47:37Z)
Deceptive AI Systems That Give Explanations Are Just as Convincing as Honest AI Systems in Human-Machine Decision Making [38.71592583606443]
The ability to discern between true and false information is essential to making sound decisions. With the recent increase in AI-based disinformation campaigns, it has become critical to understand the influence of deceptive systems on human information processing.
arXiv Detail & Related papers (2022-09-23T20:09:03Z)
Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z)
Zombies in the Loop? Humans Trust Untrustworthy AI-Advisors for Ethical Decisions [0.0]
We find that ethical advice from an AI-powered algorithm is trusted even when its users know nothing about its training data. We suggest digital literacy as a potential remedy to ensure the responsible use of AI.
arXiv Detail & Related papers (2021-06-30T15:19:20Z)
Machine Learning Explanations to Prevent Overtrust in Fake News Detection [64.46876057393703]
This research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining.
arXiv Detail & Related papers (2020-07-24T05:42:29Z)
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making [53.62514158534574]
We study whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. We show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making.
arXiv Detail & Related papers (2020-01-07T15:33:48Z)

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