Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems
Abstract
EXplainable Artificial Intelligence (XAI) approaches are used to bring transparency to machine learning and artificial intelligence models, and hence, improve the decision-making process for their end-users. While these methods aim to improve human understanding and their mental models, cognitive biases can still influence a user’s mental model and decision-making in ways that system designers do not anticipate. This paper presents research on cognitive biases due to ordering effects in intelligent systems. We conducted a controlled user study to understand how the order of observing system weaknesses and strengths can affect the user’s mental model, task performance, and reliance on the intelligent system, and we investigate the role of explanations in addressing this bias. Using an explainable video activity recognition tool in the cooking domain, we asked participants to verify whether a set of kitchen policies are being followed, with each policy focusing on a weakness or a strength. We controlled the order of the policies and the presence of explanations to test our hypotheses. Our main finding shows that those who observed system strengths early-on were more prone to automation bias and made significantly more errors due to positive first impressions of the system, while they built a more accurate mental model of the system competencies. On the other hand, those who encountered weaknesses earlier made significantly fewer errors since they tended to rely more on themselves, while they also underestimated model competencies due to having a more negative first impression of the model. Our work presents strong findings that aim to make intelligent system designers aware of such biases when designing such tools.
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Many studies have found that cognitive biases (particularly anchoring bias and automation bias more broadly) can lead users to misunderstand model behavior and place unwarranted trust in AI systems.
“Many studies have found that cognitive biases (particularly anchoring bias and automation bias more broadly) can lead users to misunderstand model behavior and place unwarranted trust in AI systems [20, 34, 52, 55].”
From Interactive Model Cards: A Human-Centered Approach to Model Documentation (2022) · cites this paper as [52]
The opportunity to be really curious and play around can help mitigate anchoring bias.
“The opportunity to 'be really curious to just kind of play around' [A2-05] can help mitigate anchoring bias [52].”
From Interactive Model Cards: A Human-Centered Approach to Model Documentation (2022) · cites this paper as [52]
Recent research has shown that recommendation-driven explainable/interpretable AI has little effect on decision making, although this is not always the case.
“However, recent research has shown that the recommendation-driven explainable/interpretable AI has little effect on decision making [1, 6-8, 28, 32], although this is not always the case [22,23,41].”
From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [28]
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“stochastic [6] and can be biased by first impressions of the agent [51].”
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Sun, J., Xing, Z., Lu, Q., Xu, X., Zhu, L., Hoang, T., & Zhao, D. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00089 · Cited by 22 · see citing works
“Studies on XAI [22], [63] show irrelevant explanations to AI decisions are even worse than no explanations.”
Eckhardt, S., Kühl, N., Dolata, M., & Schwabe, G. (2025) · ACM Computing Surveys · doi:10.1145/3776528 · Cited by 29 · see citing works
“Most of these articles exclusively use a survey to measure reliance [e.g. 33, 92, 98], while others combine surveys with metrics like gaze duration or perception-based measures [22, 74].”
Kaur, H., Conrad, M. R., Rule, D., Lampe, C. A. C., & Gilbert, É. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637354 · Cited by 15 · see citing works
“Some recent work relies on cognitive science theories to assert that cognitive heuristics can affect the effectiveness of explainability tools in various domain-specific settings [3, 11, 13, 32, 58, 90, 103, 103].”
BackgroundA Diachronic Perspective on User Trust in AI under Uncertainty
Dhuliawala, S., Zouhar, V., El‐Assady, M., & Sachan, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.339 · Cited by 14 · see citing works
Framework for human–XAI symbiosis: extended self from the dual-process theory perspective
Litvinova, Y., Mikalef, P., & Luo, X. R. (2024) · Journal of Business Analytics · doi:10.1080/2573234x.2024.2396366 · Cited by 15 · see citing works
“Similarly, the order in which humans encounter XAI errors impacts how they evaluate XAI accuracy (Nourani et al., 2021).”
InfluentialExplainable activity recognition in videos: Lessons learned
Roy, C., Nourani, M., Honeycutt, D. R., Block, J. E., Rahman, T., Ragan, E. D., Ruozzi, N., & Gogate, V. (2021) · Applied AI Letters · doi:10.1002/ail2.59 · Cited by 13 · see citing works
Self-citationSalimzadeh, S., & Gadiraju, U. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3627043.3659567 · Cited by 11 · see citing works
An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making
Jakubik, J., Schoeffer, J., Hoge, V., Vössing, M., & Kühl, N. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23618-1_24 · Cited by 10 · see citing works
Ma, S., Sun, M., & Ma, X. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3571813 · Cited by 10 · see citing works
“…making model behavior more intelligible through explanations (a.k.a. explainable AI [40, 54, 103, 188]) and informing users of the inner working status of the AI system (e.g., capabilities, confidence, uncertainties, trustworthiness, etc. [9, 10, 48, 128]) in easily understandable ways [48, 150].”
BackgroundLee, J., Jung, K., Newman, E., Chow, E., & Chen, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713244 · Cited by 9 · see citing works
The Amplifying Effect of Explainability in AI-assisted Decision-making in Groups
Duarte, R. D. B., Abreu, M. C., Campos, J., & Paiva, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713534 · Cited by 9 · see citing works
“One possible explanation for this effect is the amplification of cognitive biases such as the anchoring bias [7, 43, 61] and automation bias [35] in group dynamics.”
The Influence of Curiosity Traits and On-Demand Explanations in AI-Assisted Decision-Making
Cau, F. M., & Spano, L. D. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712165 · Cited by 8 · see citing works
de Jong, S., Bos, M. W., van Berkel, N., & Lamers, M. H. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2535732 · Cited by 13 · see citing works
Workshop on Understanding and Mitigating Cognitive Biases in Human-AI Collaboration
Boonprakong, N., He, G., Gadiraju, U., van Berkel, N., Wang, D., Chen, S., Liu, J.-Q., Tag, B., Gonçalves, J., & Dingler, T. (2023) · CSCW Companion · doi:10.1145/3584931.3611284 · Cited by 7 · see citing works
“Moreover, studies have shown that AI explanations can exacerbate our cognitive biases and compromise AI-assisted decision-making, such as trust in AI [33], reliance on AI [5, 10, 21, 39], and interpretation of AI results [27, 46].”
BackgroundMajority voting of doctors improves appropriateness of AI reliance in pathology
Gu, H., Yang, C., Magaki, S., Zarrin‐Khameh, N., Lakis, N. S., Cobos, I., Khanlou, N., Zhang, X. R., Assi, J. S., Byers, J. T., et al. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103315 · Cited by 6 · see citing works
Bubbleu: Exploring Augmented Reality Game Design with Uncertain AI-based Interaction
Kim, M., Lee, K., Balan, R. K., & Lee, Y. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581270 · Cited by 10 · see citing works
“Studies have shown that the timing of encountering AI model errors [44, 68], level of expertise [53, 67], and fidelity of the information used to explain the AI model results [45, 93] affects the user’s trust and understanding towards the system.”
BackgroundDETOXER: A Visual Debugging Tool With Multiscope Explanations for Temporal Multilabel Classification
Nourani, M., Roy, C., Honeycutt, D. R., Ragan, E. D., & Gogate, V. (2022) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2022.3201465 · Cited by 7 · see citing works
“When working with multilabel classification, instances that are detected as matches for any given class are often a smaller subset than nonmatches and easier to examine, while users may be biased toward inspecting more false positives than negatives.(4) We provide additional global explanations to help contextualize i…”
Self-citationSchäfer, U., Sipos, L., & Müller-Birn, C. (2025) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3757451 · Cited by 11 · see citing works
“However, multiple factors have been found to influence users’ mental models of an AI system and thus team performance, such as trust [191], confidence [191], observing system weaknesses [128], error boundaries [8], and perceived accuracy [92].”
Passi, S., Dhanorkar, S., & Vorvoreanu, M. (2025) · Springer eBooks · doi:10.1007/978-981-95-3658-0_98 · Cited by 6 · see citing works
Predicting the need for XAI from high-granularity interaction data
de Santana, V. F., Fucs, A., Segura, V., de Moraes, D. B., & Cerqueira, R. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103029 · Cited by 6 · see citing works
Wizard of Errors: Introducing and Evaluating Machine Learning Errors in Wizard of Oz Studies
Jansen, A., & Colombo, S. (2022) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3491101.3519684 · Cited by 6 · see citing works
“Previous studies have shown that ML errors can greatly impact the UX [1, 10, 11], the mental models users form to interact with the system, and their trust in the ML model [17].”
Raees, M., Khan, V.-J., Lykourentzou, I., & Papangelis, K. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791467 · Cited by 5 · see citing works
“These studies use (experiment) conditions to test the effect of feature-based [60, 153, 156, 164], example-based [17, 23, 108, 146], model-based [8, 33, 55, 114], or general explanations methods [102, 117, 127].”
InfluentialExploring the impact of explainable AI and cognitive capabilities on users’ decisions
Cau, F. M., & Spano, L. D. (2025) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-025-09438-0 · Cited by 9 · see citing works
“To account for ordering effects (Nourani et al. 2021), we prepared 400 random permutations for the practice and main session instances, ensuring each participant sees differently ordered loan requests.”
Thomas, I., Oh, S. Y., & Szafir, D. A. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645207 · Cited by 5 · see citing works
“However, the ways systems query for analyst input may negatively impact decision making by perpetuating preexisting bias [41, 44] or by leading to improper mental models [23, 54].”
BackgroundMethodResilience Through Appropriation: Pilots’ View on Complex Decision Support
Zhang, Z. T., Storath, C., Liu, Y., & Butz, A. M. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584056 · Cited by 5 · see citing works
“Reflecting the HB tradition of decision-making research, there has recently been a quickly growing number of controlled studies on intelligent DSTs, often through the lens of human cognitive biases [6, 11, 46].”
BackgroundTrust Junk and Evil Knobs: Calibrating Trust in AI Visualization
Wall, E., Matzen, L. E., El‐Assady, M., Masters, P., Hosseinpour, H., Endert, A., Borgo, R., Chau, P., Perer, A., Schupp, H. T., et al. (2024) · IEEE Pacific Visualization Conference (PacificVis) · doi:10.1109/pacificvis60374.2024.00012 · Cited by 7 · see citing works
“Good initial performance from an AI system can lead to over-reliance on later outputs [64] whereas early errors have an out-sized negative impact on trust [64,65].”
BackgroundThe Role of Initial Acceptance Attitudes Toward AI Decisions in Algorithmic Recourse
Tominaga, T., Yamashita, N., & Kurashima, T. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713573 · Cited by 4 · see citing works
“Conversely, when users initially view AI as highly accurate, they are prone to accept its suggestions [61, 62, 91].”
When AI reviews science: Can we trust the referee?
Wang, J., Liu, Y., Xu, H., Hu, K., Di, S., Ni, W., Yue, L., Zhang, M.-L., Ren, K., & Chen, L. (2026) · The Innovation Informatics · doi:10.59717/j.xinn-inform.2026.100030 · Cited by 4 · see citing works
“By using persuasive rhetoric in these sections, they may anchor the AI’s initial assessment on a favorable premise before methods and evidence are scrutinized 119 , biasing the downstream evaluation.”
Evaluating What Others Say: The Effect of Accuracy Assessment in Shaping Mental Models of AI Systems
Do, H. J., Brachman, M., Dugan, C., Pan, Q., Rai, P., Johnson, J. M., & Thawani, R. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3686912 · Cited by 4 · see citing works
“Nourani et al. suggested that designers of intelligent systems provide guidance for users, as un-directed use of a system can lead to erroneous mental models [56].”
Detecting and explaining anomalies in industrial Internet of things systems using an autoencoder
Levshun, D., Levshun, D. S., & Kotenko, I. V. (2025) · Ontology of Designing · doi:10.18287/2223-9537-2025-15-1-96-113 · Cited by 4 · see citing works
“Методы апостериорного объяснения используются для объяснения производительности модели после еѐ обучения [27, 28] .”
Brereton, M. F., Ambe, A. H., Lovell, D. R., Sitbon, L., Capel, T., Soro, A., Xu, Y., Moreira, C., Favre, B., & Bradley, A. P. (2023) · Australian Conference on Computer-Human Interaction (OzCHI) · doi:10.1145/3638380.3638435 · Cited by 4 · see citing works
Mental Models of Adversarial Machine LearningPreprint
Bieringer, L., Grosse, K., Michael Backes, A., Biggio, B., & Krombholz, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.03726 · Cited by 4 · see citing works
“Mental models have been found to serve as scaffolds not only for a given ML application [84], but also for its embedding in organizational practices [91].”
Folgado, D., Famiglini, L., Campagner, A., Dores, H., Barandas, M., Gamboa, H. F. S., & Cabitza, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-95838-0_14 · Cited by 3 · see citing works
Human-Centered AI Design Principles for Generative AI
Weisz, J. D., He, J., & Muller, M. J. (2025) · Springer eBooks · doi:10.1007/978-981-95-3658-0_74 · Cited by 3 · see citing works
Sode, K. (2026) · AI & Society · doi:10.1007/s00146-026-02885-2 · Cited by 4 · see citing works
“…pathways remains limited; the claim here is therefore advanced as a theory-guided expectation consistent with broader findings on explanation-induced overconfidence (Rozenblit and Keil 2002; Nourani et al. 2021) and with the institutional “Impossible Task” of oversight under opacity.”
Biases and Ethical Considerations in AI-Driven Knowledge Management
Gafni, R., Kantsepolsky, B., & Sherman, S. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80592-391-620261013 · Cited by 3 · see citing works
Erlei, A., Cau, F. M., Georgiev, R., Kumar, S. C., Bizer, K., & Gadiraju, U. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791420 · Cited by 4 · see citing works
“To control for potential ordering biases [107], we (i) applied block randomisation across the presentation of the three tasks, and (ii) generated 400 random permutations of the ten instances to ensure each participant encountered uniquely ordered task instances.”
Human-AI Teaming: Following the IMOI Framework
Kleanthous, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60611-3_27 · Cited by 3 · see citing works
Antar, A. D., Molaei, S., Chen, Y.-Y., Lee, M. L., & Banovic, N. (2024) · ACM Symposium on User Interface Software and Technology · doi:10.1145/3654777.3676323 · Cited by 8 · see citing works
Building Human-AI Reliance Through Cognitive Engagement and Exploratory AI Assistance
Raees, M., Khan, V.-J., & Papangelis, K. (2025) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3708319.3734171 · Cited by 3 · see citing works
“For instance, this requires supporting semantic alignment with domain users’ business processes and mental models [19].”
InfluentialRelEYEance: Gaze-based Assessment of Users' AI-reliance at Run-time
Wu, Z., Wang, Y., Langer, M., & Feit, A. M. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3725841 · Cited by 2 · see citing works
“…stem from various factors, including biases toward AI such as expecting near-perfect performance [21, 25, 27], a lack of experience with an AI system thus having little evidence about the actual performance of those systems [16, 29], or unfit explanations for system behavior and outputs [2, 20].”
The Enterprising and Elusive Prospects of Human-AI Collaboration
Gadiraju, U., & Balayn, A. (2025) · Springer eBooks · doi:10.1007/978-3-032-01940-0_7 · Cited by 2 · see citing works
Satapathy, J., & Panda, J. K. (2025) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v6.i1.2025.4409 · Cited by 5 · see citing works
AI in the city: impact assessment of artificial intelligence uses in earth observation
Šćepanović, S., Bogucka, E. P., & Quercia, D. (2025) · Urban Informatics · doi:10.1007/s44212-025-00074-y · Cited by 2 · see citing works
“This level of agreement, which is lower than the alignment between the majority label and the LLM, suggests a potential over-reliance on the LLM labels, likely influenced by the well-documented anchoring effect (Furnham & Boo, 2011; Nourani et al., 2021).”
Human-Centered Explainable AI (XAI)
Liao, Q. V., & Varshney, K. R. (2026) · Cambridge University Press eBooks · doi:10.1017/9781108872225.011 · Cited by 314 · see citing works
Manger, C., Vogl, A., Rosbach, E., Ziegler, C., & Riener, A. (2023) · International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) · doi:10.1145/3581961.3609897 · Cited by 2 · see citing works
“Research from other disciplines for example found that providing explanations about the automation’s capabilities can influence the formation of automation bias, depending on whether users were presented with the system’s strengths or weaknesses [15].”
Satapathy, J., & Panda, J. K. (2024) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v5.i3.2024.4409 · Cited by 1 · see citing works
Conceptualizing Multi-party AI Reliance for Design Research
Eckhardt, S., Dolata, M., Bauer-Hänsel, I., & Schwabe, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-61175-9_4 · Cited by 1 · see citing works
Improving Tactical Decision-Making Through Multiobjective Contrastive Explanations
Blom, M. L., Singh, R., Miller, T., Sonenberg, L., Trentelman, K., Saulwick, A., & Wark, S. (2025) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2025.3591163 · Cited by 1 · see citing works
“Empirical studies show that ex-plainability tools can positively impact decision-making [20], [22], [23], [33], however, in many cases, it has minimal effect [34], [35], [36], [37], [38].”
Ahmed, M. H., Alkhazaleh, S., & Aldallal, A. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-87550-2_56 · Cited by 1 · see citing works
Manger, C., Vogl, A., & Riener, A. (2025) · Applied Sciences · doi:10.3390/app152011030 · Cited by 1 · see citing works
Sonenberg, L. (2023) · Logic Journal of IGPL · doi:10.1093/jigpal/jzad006
Der Mensch im Mittelpunkt: Einblick in die Gestaltung Menschenzentrierter Künstlicher Intelligenz
Weitz, K. (2024) · Springer eBooks · doi:10.1007/978-3-658-43816-6_14
Thomaschewski, L., Tausch, A., Berretta, S., Vogel, O., & Kluge, A. (2025) · Zeitschrift für wirtschaftlichen Fabrikbetrieb · doi:10.1515/zwf-2025-1113
Behzad, T., Gurney, N., Wang, N., & Pynadath, D. V. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13187-4_8
Cognitive Process Tracing in Algorithm Augmented Decision Making
Wohlschlegel, J., & Jussupow, E. (2025) · Lecture notes in information systems and organisation · doi:10.1007/978-3-032-00815-2_9
Cognitive models facilitate real-time inference of latent motives
Fitch, A. K., & Kvam, P. D. (2026) · Scientific Reports · doi:10.1038/s41598-026-37587-8
“Furthermore, if cognitive models can help an AI system exhibit more human-like behavior, a person might be able to build a better mental model of the system which could improve trust 70,71 .”
The HEART Interface: Visualizing Risk Score Uncertainty in the Cardiothoracic ICU
Nourani, M., Nguyen, L., Barry, C. L., Jin, Q., & Tory, M. K. (2026) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3742413.3789109
Self-citationExplainability for machine learning models : from data adaptability to user perception
Delaunay, J. (2023) · theses.fr (ABES) · doi:10.70675/933366c4z576ez464cz9fcfz24627f86dd2f
Bertrand, A. (2024) · doi:10.70675/216d3647z78bcz4c68z9ba9z2d0a029cf1b4 · Cited by 1 · see citing works
From Struggle to Success: Context-Aware Guidance for Screen Reader Users in Computer Use
Chen, N., Lu, J., Wang, Z., Qiu, L. K., Chen, S., & Yang, Y. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790661
“…users to develop understanding through hands-on practice [17, 42, 43], helps build robust mental models essential for effective software control [9, 67], reduces the risk of overreliance on AI systems [13, 82, 100], and develops domain knowledge that enables users to better evaluate AI responses…”
From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants
Rismani, S., Blodgett, S. L., Liao, Q. V., Olteanu, A., & Moon, A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791670
“Prior work on predictive algorithms has shown that persuasive explanations can lead users to form mental models that promote over-reliance or under-trust [4–6 , 56].”
- Authors
- Nourani, M., Roy, C., Block, J. E., Honeycutt, D. R., Rahman, T., Ragan, E., & Gogate, V.
- Affiliations
- University of Florida (US)
- The University of Texas at Dallas (US)
- INDIE Lab Department of Computer & Information Science & Engineering, United States
- Venue
- 26th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2021-04-14
- Pages
- 340-350
- OpenAlex
- W3152997756
- Cited by
- 107
- References
- 44
- Citations by year
- 2026: 21 · 2025: 29 · 2024: 20 · 2023: 23 · 2022: 10 · 2021: 4
- FWCI
- 6.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- Funders
- NSF (National Science Foundation) (1900767)
- Defense Advanced Research Projects Agency (N66001-17-2-4032)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- anchoring effect, explainable AI, cognitive biases, automation bias, order effects, first impressions, video activity recognition, cooking, human-AI decision-making
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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