Pith. sign in

REVIEW 4 major objections 5 minor 166 references

A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This review argues that integrating cognitive models with AI-based analytics inside risk-informed human reliability assessment markedly improves prediction of human error, but only if data, transparency, and validation improve.

desk verdict A useful but uneven review that overstates its central thesis; the survey of HRA and cognitive models is solid, but the 'predictive fidelity' claim is unsupported and the bibliometric scaffolding is shaky. read the letter →

arxiv 2507.01017 v1 pith:VEM45OES submitted 2025-06-10 cs.HC

Xingyu Xiao , Hongxu Zhu , Jingang Liang , Jiejuan Tong , Haitao Wang This is my paper
classification cs.HC
keywords humanerrorrisk-informeddecisionmakingreliabilityassessmentartificialintelligenceperformancemodelscognitivesciencesafety-criticalsystemspredictivefidelity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Human error remains a dominant risk driver in nuclear power, aviation, and healthcare, and the paper's central claim is that the field can curb it by combining human reliability assessment (HRA) with cognitive models and AI analytics in a single risk-informed pipeline. The review synthesizes three strands: error taxonomy and mitigation, probabilistic HRA methods from THERP/CREAM to dynamic and Bayesian variants, and cognitive architectures plus AI techniques for real-time error detection and operator-state estimation. Its recurring insight is that mechanistic accounts of perception, memory, and decision-making enrich error prediction, and AI can carry that enrichment into dynamic, real-time settings. The paper argues the payoff is higher predictive fidelity, but only if data scarcity, algorithmic opacity, and over-reliance on expert judgment are addressed.

What carries the argument

The central mechanism is the integrated HRA-AI pipeline: a risk-informed workflow in which qualitative error taxonomies (slips, lapses, mistakes; omission versus commission) and mechanistic cognitive architectures such as ACT-R and QN-MHP supply the structure and features that AI algorithms—Bayesian networks, anomaly detectors, and large language model agents—learn from, while those algorithms update human error probabilities in real time through performance shaping factors. The named workhorse is dynamic human reliability assessment (D-HRA), which replaces static point estimates with temporally evolving risk informed by simulator data and operator state.

What would settle it

A pooled re-analysis of the cited AI-HRA case studies that found no consistent predictive advantage for cognitive-model-integrated pipelines over standard HRA or AI alone would falsify the claim; so would a controlled simulator study in which integrated-pipeline error probabilities were no better calibrated than THERP or CREAM estimates.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim is a convergence claim: the error taxonomies developed in cognitive science, the probabilistic machinery of HRA, and modern AI analytics are not competing alternatives but stages of one risk-informed pipeline. Cognitive models supply mechanistic accounts of perception, memory, and decision-making that explain why errors happen; HRA supplies the quantitative scaffolding of human error probabilities and performance shaping factors; AI supplies the capacity to monitor operator state in real time and update those probabilities dynamically. The review states this recurring insight directly: integrating cognitive models with AI-based analytics inside risk-informed HRA pipelines markedly enhances predictive fidelity, while demanding richer datasets, transparent algorithms, and rigorous validation. The paper also identifies the directions it says the field must take—resilience engineering, operationalizing the iceberg model of incident causation, and cross-domain data consortia—to move from case-study demonstrations to general practice.

Load-bearing premise

The review's central claim rests on the assumption that the few case studies it highlights—such as an embryo-identification task with a reported 100% success rate and a decision-support score rising from 0.8377 to 0.9116—stand in for real safety-critical operations, and that 'predictive fidelity' names a measurable outcome rather than a slogan.

Editorial extensions

If this is right

  • In high-stakes domains, HRA outputs would shift from static human error probabilities to live estimates that update with operator state and task context.
  • AI-augmented pipelines would make error detection proactive, monitoring physiological and behavioral signals to flag fatigue or overload before a mistake occurs.
  • Cognitive architectures would supply the missing mechanistic link between performance metrics and reliability, closing the gap the review identifies.
  • None of this lands without shared data standards, cross-industry data sharing, and validation against simulator or operating data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • I infer that a direct test of the paper's central claim would pit identical HRA tasks with and without cognitive-model features against the same operator-error dataset, measuring calibration of predicted versus observed error rates.
  • The case-study metrics are not yet comparable across domains; operationalizing 'predictive fidelity' as HEP calibration or discrimination would let future research pool evidence.
  • The resilience reframing suggests a shift in target variables: instead of minimizing error counts alone, AI-HRA systems could measure recovery time and adaptive performance under stress.
  • Cross-domain data consortia would let rare-event industries like nuclear and aviation borrow statistical power from simulator-heavy domains such as driving and healthcare, making small-sample HRA models trainable.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This manuscript is a narrative review spanning human error, risk-informed decision making (RIDM), human reliability assessment (HRA), artificial intelligence (AI), and human performance modeling (HPM). It surveys error taxonomies and their claimed quantitative impact (Section 2); RIDM frameworks and three generations of HRA methods together with dynamic HRA (Section 3); cognitive architectures including ACT-R, SOAR, EPIC, and QN-MHP (Section 4); and AI techniques for error detection, AI-enhanced HRA, and HPM integration (Section 5). The Abstract's central thesis is that integrating cognitive models with AI-based analytics inside risk-informed HRA pipelines 'markedly enhances predictive fidelity.' The paper closes with open challenges (Section 6) and future directions including resilience-oriented HRA, operationalizing the iceberg model, and cross-domain data consortia (Section 7).

Significance. If fully supported, the synthesis would be a useful interdisciplinary map for the HRA and human-factors communities: the paper covers the HRA generations (THERP, CREAM, ATHEANA, SPAR-H, IDHEAS-G, Phoenix), four cognitive architectures, and recent Bayesian, fuzzy, and LLM-based HRA work, and it proposes a concrete research agenda (resilience-oriented HRA, grounded-theory data collection, cross-domain data consortia). It is also candid about the field's gaps in Section 6. However, the evidence base consists of selected illustrative examples rather than a systematic synthesis: there is no search protocol, no inclusion criteria, no quality appraisal, and no comparative analysis, and the central 'predictive fidelity' claim is not demonstrated by the cited studies. The value of the review is therefore contingent on a substantial revision of its claims and the addition of verifiable methodology.

major comments (4)
  1. [Abstract; Sections 5.2-5.4 and 6] The central claim that integration 'markedly enhances predictive fidelity' is not supported by the body of the review, and the manuscript's own Section 6 concedes the missing evidence. No definition or metric for 'predictive fidelity' is ever given. The application studies in Sections 5.2-5.4 (embryo identification [123], GAN anomaly detection [124], accident-report classification [125], POMDP action planning [127], the rehabilitation assistant [122]) are standalone AI classifiers or decision-support tools; none integrates a cognitive model with an HRA method and AI analytics in a single pipeline. In particular, the F1 improvement from 0.8377 to 0.9116 in [122] measures the AI system's own classification performance after tuning, not human-error prediction within an HRA context. Section 5.4 itself concludes that 'most of the existing work focuses on detection, diagnosis, and optimization, with a lack of mechanistic understanding,' and Section 6 lists 'insufficient application of AI in current practices' and 'lack of rigorous quantitative approaches from cognitive models to human reliability models' as open problems. These admissions directly contradict the abstract's 'recurring insight.' The authors should either temper the claim to an untested research hypothesis or supply a comparative synthesis (e.g., a table listing which studies combine cognitive models, HRA, and AI, with reported outcomes and baselines).
  2. [Section 2 (Table 1) and Tables 2-5] The bibliometric analyses are not reproducible and at least one is internally inconsistent. The text states that 'We collected 1,000 relevant indices on "human error" from the Web of Science' (Section 2, before Figure 2), yet Table 1 reports 30,156 occurrences for 'human factors and ergonomics'; even allowing multiple keywords per document, a frequency of 30,156 in a 1,000-document corpus is implausible without documentation of the underlying query and time window. Tables 2-5 report similarly large frequencies (e.g., 26,126 occurrences of 'automotive industry' in Table 3) without stating corpus sizes, search dates, database editions, or normalization procedures, and the keyword sets do not obviously correspond to the stated topics (e.g., 'public goods game' and 'centipede game' are the top rows of the 'risk informed' table). Since the review uses these tables to characterize whole research fields, the authors should document the bibliometric methodology in full or remove the quantitative framing.
  3. [Section 2.2, refs [15] and [30]] Several citation-to-claim mismatches occur in load-bearing factual statements. The claim that '94% of serious accidents are caused by human error (e.g., Rushe 2019 [15])' cites reference [15], which is Read et al. 2021 'State of science: Evolving perspectives on human error', not a 2019 Rushe article. The sentence 'the crash of a U.S. weather satellite [30] in November 1999' cites Fujita and Caracena 1977, which analyzes three weather-related aircraft accidents and contains no satellite crash; this claim should be re-sourced or removed. In addition, Section 2.1 attributes a 'visual model' to 'Nuberg [10]' while reference [10] is Petersen 2003, the same author named elsewhere in that paragraph. For a review, citation accuracy is part of the evidentiary basis; these errors should be corrected in a full reference audit.
  4. [Section 1 (methodology); title] The paper is titled a 'Comprehensive Review,' but no review methodology is stated: there is no search strategy, database query, inclusion/exclusion criteria, time window, or quality appraisal of the cited studies. The selection of case studies in Sections 5.2-5.4 is presented without justification of representativeness, and Section 6 acknowledges the relevant gaps. Without a stated protocol, the 'comprehensive' claim and the representativeness of the bibliometric and case-study evidence cannot be assessed. The authors should either add a short methods subsection describing how sources were identified and selected, or revise the title and claims to describe a narrative or scoping review.
minor comments (5)
  1. [Abstract] The passage 'persistent limitations: scarce high-quality data, algorithmic opacity, and residual reliance on expert judgment, continue to constrain progress' is ungrammatical; the colon should be replaced with a dash pair or the clause restructured.
  2. [Section 3.1] The sentence about school selection ('...to support informed decision-making [51] providing a structured approach for contemporary syllabus-based school selection') is a run-on and should be split into two sentences.
  3. [Reference list] Reference [92] (Deneulin and Shahani, 'An Introduction to the Human Development and Capability Approach') does not match the claim in Section 4.2 about human performance modeling; a relevant source should be substituted.
  4. [Reference list] Reference [87] contains the typo 'Maxerll AFB' (should be 'Maxwell AFB'), and the title of reference [81] contains 'survery' for 'survey'.
  5. [Tables 1-5; Section 5.2] The table headers appear in the text as 'T able 1' through 'T able 5' with a stray space, and in-text LaTeX artifacts such as 'G¨ond¨ocs' and 'p ¡ 0.01' in Section 5.2 should be rendered as proper text ('Göndöcs' and 'p < 0.01').

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: this review's central claim is a weakly supported synthesis, not a reduction to its own inputs.

full rationale

This paper is a literature review, not a derivation chain. It introduces no equations, fits no parameters, and makes no prediction from a fitted model, so no step reduces to its own inputs by construction. The central claim — that integrating cognitive models with AI-based analytics inside risk-informed HRA pipelines 'markedly enhances predictive fidelity' (Abstract) — is presented as a 'recurring insight' distilled from the surveyed literature, not as a result derived from the paper's own analysis. The quantitative improvements cited (Hammer et al.'s 100% embryo identification; Lee et al.'s F1 increase from 0.8377 to 0.9116) belong to external studies and are reported as evidence, not as the authors' own fitted parameters later renamed as predictions. The authors' self-citations ([4], [145], [146], [156]) appear only as background support for adaptive decision support, deep learning applied to equipment reliability, and virtual human technology; none is load-bearing for the review's thesis, and none imports a uniqueness theorem or an ansatz. The paper's main weaknesses are evidentiary rather than circular: Section 6 concedes 'insufficient application of AI in current practices,' 'reliance on subjective knowledge,' and 'lack of rigorous quantitative approaches from cognitive models to human reliability models,' which undercut rather than entail the Abstract's claimed 'recurring insight,' and the bibliometric tables are internally inconsistent with the stated 1,000-document corpus (Table 1 lists 30,156 occurrences for 'human factors and ergonomics'). Those are correctness-risk findings, not circularity. Because no step of the argument is equivalent to its input by definition or by fitting, the appropriate circularity score is 1, reflecting only minor, non-load-bearing self-citations.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The review introduces no free parameters or invented entities. It rests on domain assumptions from the literature, as listed.

assumptions (3)
  • domain assumption Human error contributes to the majority of accidents across safety-critical industries.
    Stated in Section 2.2 with a 50-90% range from cited sources; treated as established rather than re-derived.
  • domain assumption Cognitive architectures can validly represent operator behavior.
    Section 4.2 relies on ACT-R, SOAR, EPIC, and QN-MHP as mechanistic accounts without validating them against the specific HRA contexts.
  • domain assumption AI-based systems can reliably detect and predict human error from operational data.
    Assumed throughout Section 5 based on selected studies; not critically assessed here.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models." pith.science (2026). https://pith.science/paper/VEM45OES

@misc{pith2026250701017,
  author       = {Pith},
  title        = {Pith review of: A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VEM45OES}},
  note         = {Machine review of arXiv:2507.01017}
}
read the original abstract

Human error remains a dominant risk driver in safety-critical sectors such as nuclear power, aviation, and healthcare, where seemingly minor mistakes can cascade into catastrophic outcomes. Although decades of research have produced a rich repertoire of mitigation techniques, persistent limitations: scarce high-quality data, algorithmic opacity, and residual reliance on expert judgment, continue to constrain progress. This review synthesizes recent advances at the intersection of risk-informed decision making, human reliability assessment (HRA), artificial intelligence (AI), and cognitive science to clarify how their convergence can curb human-error risk. We first categorize the principal forms of human error observed in complex sociotechnical environments and outline their quantitative impact on system reliability. Next, we examine risk-informed frameworks that embed HRA within probabilistic and data-driven methodologies, highlighting successes and gaps. We then survey cognitive and human-performance models, detailing how mechanistic accounts of perception, memory, and decision-making enrich error prediction and complement HRA metrics. Building on these foundations, we critically assess AI-enabled techniques for real-time error detection, operator-state estimation, and AI-augmented HRA workflows. Across these strands, a recurring insight emerges: integrating cognitive models with AI-based analytics inside risk-informed HRA pipelines markedly enhances predictive fidelity, yet doing so demands richer datasets, transparent algorithms, and rigorous validation. Finally, we identify promising research directions, coupling resilience engineering concepts with grounded theory, operationalizing the iceberg model of incident causation, and establishing cross-domain data consortia, to foster a multidisciplinary paradigm that elevates human reliability in high-stakes systems.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

166 extracted references · 74 canonical work pages

  1. [15]

    Ergonomics 64(9), 1091–1114 (2021)

    Read, G.J., Shorrock, S., Walker, G.H., Salmon, P.M.: State of science: Evolving perspectives on ‘human error’. Ergonomics 64(9), 1091–1114 (2021)

    2021

  2. [30]

    Bulletin of the American Meteorological Society 58(11), 1164–1181 (1977)

    Fujita, T.T., Caracena, F.: An analysis of three weather-related aircraft acci- dents. Bulletin of the American Meteorological Society 58(11), 1164–1181 (1977)

    1977

  3. [123]

    Journal of Assisted Reproduction and Genetics 39(10), 2343–2348 (2022)

    Hammer, K.C., Jiang, V.S., Kanakasabapathy, M.K., Thirumalaraju, P., Kan- dula, H., Dimitriadis, I., Souter, I., Bormann, C.L., Shafiee, H.: Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study. Journal of Assisted Reproduction and Genetics 39(10), 2343–2348 (2022)

    work page 2022

  4. [124]

    Nuclear Engineering and Technology 55(2), 603–622 (2023)

    Gursel, E., Reddy, B., Khojandi, A., Madadi, M., Coble, J.B., Agarwal, V., Yadav, V., Boring, R.L.: Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance. Nuclear Engineering and Technology 55(2), 603–622 (2023)

    work page 2023

  5. [125]

    Safety science 146, 105528 (2022)

    Morais, C., Yung, K.L., Johnson, K., Moura, R., Beer, M., Patelli, E.: Identi- fication of human errors and influencing factors: A machine learning approach. Safety science 146, 105528 (2022)

    work page 2022

  6. [127]

    In: Artificial Intelligence Applications and Innova- tions: 11th IFIP WG 12.5 International Conference, AIAI 2015, Bayonne, France, September 14-17, 2015, Proceedings 11, pp

    Jean-Baptiste, E.M., Rotshtein, P., Russell, M.: Pomdp based action planning and human error detection. In: Artificial Intelligence Applications and Innova- tions: 11th IFIP WG 12.5 International Conference, AIAI 2015, Bayonne, France, September 14-17, 2015, Proceedings 11, pp. 250–265 (2015). Springer

    work page 2015

  7. [122]

    In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp

    Lee, M.H., Siewiorek, D.P., Smailagic, A., Bernardino, A., Badia, S.B.: A human-ai collaborative approach for clinical decision making on rehabilitation assessment. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2021)

    work page 2021

  8. [10]

    Professional Safety 48(12), 25–32 (2003)

    Petersen, D.: Human error. Professional Safety 48(12), 25–32 (2003)

    2003

Show all 166 references
  1. [1]

    Safety science 40(1-4), 17–30 (2002)

    Amendola, A.: Recent paradigms for risk informed decision making. Safety science 40(1-4), 17–30 (2002)

    2002

  2. [2]

    Technical report, Sandia Labs., Albuquerque, NM (United States) (1974)

    Swain, A.D., Guttmann, H.E.: Human reliability analysis applied to nuclear power. Technical report, Sandia Labs., Albuquerque, NM (United States) (1974)

    1974

  3. [3]

    Elsevier, ??? (1998)

    Hollnagel, E.: Cognitive Reliability and Error Analysis Method (CREAM). Elsevier, ??? (1998)

    1998

  4. [4]

    Energies 17(10), 2439 (2024)

    Xiao, X., Liang, J., Tong, J., Wang, H.: Emergency decision support techniques for nuclear power plants: Current state, challenges, and future trends. Energies 17(10), 2439 (2024)

    2024

  5. [5]

    scientometrics 84(2), 523–538 (2010)

    Van Eck, N., Waltman, L.: Software survey: Vosviewer, a computer program for bibliometric mapping. scientometrics 84(2), 523–538 (2010)

    2010

  6. [6]

    Bmj 320(7237), 768–770 (2000)

    Reason, J.: Human error: models and management. Bmj 320(7237), 768–770 (2000)

    2000

  7. [7]

    Cambridge university press, ??? (1990)

    Reason, J.: Human Error. Cambridge university press, ??? (1990)

    1990

  8. [8]

    Nuclear Engi- neering and Design 240(7), 1897–1905 (2010)

    Lin, C.J., Yenn, T.-C., Yang, C.-W.: Optimizing human–system interface automation design based on a skill-rule-knowledge framework. Nuclear Engi- neering and Design 240(7), 1897–1905 (2010)

    2010

  9. [9]

    Ergonomics 45(4), 290–308 (2002)

    Hobbs, A., Williamson, A.: Skills, rules and knowledge in aircraft maintenance: errors in context. Ergonomics 45(4), 290–308 (2002)

    2002

  10. [11]

    Tech- nical report, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States) (1963)

    Swain, A.D.: Method for performing a human-factors reliability analysis. Tech- nical report, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States) (1963)

    1963

  11. [12]

    Routledge, ??? (2018)

    Kletz, T.: An Engineer’s View of Human Error. Routledge, ??? (2018)

    2018

  12. [13]

    Handbook of human factors and ergonomics, 734–800 (2012)

    Sharit, J.: Human error and human reliability analysis. Handbook of human factors and ergonomics, 734–800 (2012)

    2012

  13. [14]

    The International Journal of Aviation Psychology 11(4), 341–357 (2001)

    Wiegmann, D.A., Shappell, S.A.: Human error perspectives in aviation. The International Journal of Aviation Psychology 11(4), 341–357 (2001)

    2001

  14. [16]

    Reliability 24 Engineering & System Safety 65(1), 1–9 (1999)

    Sasou, K., Reason, J.: Team errors: definition and taxonomy. Reliability 24 Engineering & System Safety 65(1), 1–9 (1999)

    1999

  15. [17]

    Journal of Organizational Behavior 36(3), 382–402 (2015)

    Sieweke, J., Zhao, B.: The impact of team familiarity and team leader experience on team coordination errors: A panel analysis of professional basketball teams. Journal of Organizational Behavior 36(3), 382–402 (2015)

    2015

  16. [18]

    Alonso, A., Baker, D.P., Holtzman, A., Day, R., King, H., Toomey, L., Salas, E.: Reducing medical error in the military health system: how can team training help? Human Resource Management Review 16(3), 396–415 (2006)

    2006

  17. [19]

    Routledge, ??? (2017)

    Helmreich, R.L., Merritt, A.C.: Culture at Work in Aviation and Medicine: National, Organizational and Professional Influences. Routledge, ??? (2017)

    2017

  18. [20]

    BMJ Quality & Safety 12(6), 405–410 (2003)

    Pronovost, P.J., Weast, B., Holzmueller, C.G., Rosenstein, B.J., Kidwell, R.P., Haller, K.B., Feroli, E.R., Sexton, J.B., Rubin, H.R.: Evaluation of the culture of safety: survey of clinicians and managers in an academic medical center. BMJ Quality & Safety 12(6), 405–410 (2003)

    2003

  19. [21]

    Annual review of sociology 11(1), 457–483 (1985)

    Ouchi, W.G., Wilkins, A.L.: Organizational culture. Annual review of sociology 11(1), 457–483 (1985)

    1985

  20. [22]

    Sudbury, UK: HSE Books (1993)

    Health, Commission, S., et al.: Organizing for safety: Third report of the human factors study group of acsni. Sudbury, UK: HSE Books (1993)

    1993

  21. [23]

    Process Safety Progress 21(2), 119–129 (2002)

    Baybutt, P.: Layers of protection analysis for human factors (lopa-hf). Process Safety Progress 21(2), 119–129 (2002)

    2002

  22. [24]

    Plos one 15(4), 0231391 (2020)

    Guo, Y., Sun, Y.: Flight safety assessment based on an integrated human reliability quantification approach. Plos one 15(4), 0231391 (2020)

    2020

  23. [25]

    Technical report, United States

    Stewart, T., et al.: Overview of motor vehicle crashes in 2020. Technical report, United States. Department of Transportation. National Highway Traffic Safety . . . (2022)

    2022

  24. [26]

    Reliability Engineering & system safety 83(2), 153–167 (2004)

    Le Bot, P.: Human reliability data, human error and accident mod- els—illustration through the three mile island accident analysis. Reliability Engineering & system safety 83(2), 153–167 (2004)

    2004

  25. [27]

    Stang, E.: Chernobyl-system accident or human error? Radiation protection dosimetry 68(3-4), 197–201 (1996)

    1996

  26. [28]

    Columbia Journal of World Business 22(1), 59–67 (1987)

    Schwartz, H.S.: On the psychodynamics of organizational disaster: The case of the space shuttle challenger. Columbia Journal of World Business 22(1), 59–67 (1987)

    1987

  27. [29]

    Journal of radiation research 42(SUPPL), 149–155 25 (2001)

    Hayata, I., Kanda, R., Minamihisamatsu, M., Furukawa, A., Sasaki, M.S.: Cytogenetical dose estimation for 3 severely exposed patients in the jco critical- ity accident in tokai-mura. Journal of radiation research 42(SUPPL), 149–155 25 (2001)

    2001

  28. [31]

    CRC Press, ??? (2017)

    Woods, D., Dekker, S., Cook, R., Johannesen, L., Sarter, N.: Behind Human Error. CRC Press, ??? (2017)

    2017

  29. [32]

    The Journal of narrative technique 14(1), 68–73 (1984)

    Johnston, K.G.: Hemingway and freud: The tip of the iceberg. The Journal of narrative technique 14(1), 68–73 (1984)

    1984

  30. [33]

    Journal of management in engineering 20(2), 70–79 (2004)

    Love, P.E., Josephson, P.-E.: Role of error-recovery process in projects. Journal of management in engineering 20(2), 70–79 (2004)

    2004

  31. [34]

    In: 2014 47th Hawaii International Conference on System Sciences, pp

    Greitzer, F.L., Strozer, J., Cohen, S., Bergey, J., Cowley, J., Moore, A., Mundie, D.: Unintentional insider threat: contributing factors, observables, and miti- gation strategies. In: 2014 47th Hawaii International Conference on System Sciences, pp. 2025–2034 (2014). IEEE

    2014

  32. [35]

    final report

    Swain, A.D., Guttmann, H.E.: Handbook of human-reliability analysis with emphasis on nuclear power plant applications. final report. Technical report, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States) (1983)

    1983

  33. [36]

    Industrial Management & Data Systems 119(6), 1242–1267 (2019)

    Wong, W.P., Tan, H.C., Tan, K.H., Tseng, M.-L.: Human factors in informa- tion leakage: mitigation strategies for information sharing integrity. Industrial Management & Data Systems 119(6), 1242–1267 (2019)

    2019

  34. [37]

    International Journal of Security and Its Applications 12(1), 37–46 (2018)

    Ismail, W.B.W., Yusof, M.: Mitigation strategies for unintentional insider threats on information leaks. International Journal of Security and Its Applications 12(1), 37–46 (2018)

    2018

  35. [38]

    In: Proceedings of the 12th Probabilistic Safety Assessment and Management Conference, Paper PSAM-380, pp

    MacLeod, D.E., Parry, G.W., Sloane, B.D., Lawrence, P., Chan, E.M., Trifanov, A.V.: Simplified human reliability analysis process for emergency mitigation equipment (eme) deployment. In: Proceedings of the 12th Probabilistic Safety Assessment and Management Conference, Paper P...

    2014

  36. [39]

    IEEE transactions on systems, man, and cybernetics (3), 257–266 (1983)

    Rasmussen, J.: Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE transactions on systems, man, and cybernetics (3), 257–266 (1983)

    1983

  37. [40]

    Nuclear Engineering and Design 241(10), 4206–4222 (2011)

    Perez, M., Reventos, F., Batet, L., Guba, A., T´ oth, I., Mieusset, T., Bazin, P., De Cr´ ecy, A., Borisov, S., Skorek, T.,et al.: Uncertainty and sensitivity analysis of a lbloca in a pwr nuclear power plant: Results of the phase v of the bemuse programme. Nuclear Engineering...

    2011

  38. [41]

    US Nuclear Regulatory Commission, Washington, DC, USA (2008)

    O’Hara, J.M., Higgins, J.C., Brown, W.S., Fink, R., Persensky, J., Lewis, 26 P., Kramer, J., Szabo, A., Boggi, M.: Human factors considerations with respect to emerging technology in nuclear power plants. US Nuclear Regulatory Commission, Washington, DC, USA (2008)

    2008

  39. [42]

    REPORT-UNIVERSITY OF YORK DEPARTMENT OF COMPUTER SCIENCE YCS (1997)

    Fields, B., Harrison, M., Wright, P.: Thea: Human error analysis for require- ments definition. REPORT-UNIVERSITY OF YORK DEPARTMENT OF COMPUTER SCIENCE YCS (1997)

    1997

  40. [43]

    ACM Transactions on Computer- Human Interaction 30(2), 1–30 (2023)

    Chignell, M., Wang, L., Zare, A., Li, J.: The evolution of hci and human factors: Integrating human and artificial intelligence. ACM Transactions on Computer- Human Interaction 30(2), 1–30 (2023)

    2023

  41. [44]

    Proceedings of the ACM on Human-Computer Interaction 6(CSCW1), 1–32 (2022)

    Fan, M., Yang, X., Yu, T., Liao, Q.V., Zhao, J.: Human-ai collaboration for ux evaluation: effects of explanation and synchronization. Proceedings of the ACM on Human-Computer Interaction 6(CSCW1), 1–32 (2022)

    2022

  42. [45]

    In: Proceedings of the 2019 Chi Conference on Human Factors in Computing Systems, pp

    Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P.N., Inkpen, K., et al.: Guidelines for human-ai interaction. In: Proceedings of the 2019 Chi Conference on Human Factors in Computing Systems, pp. 1–13 (2019)

    2019

  43. [46]

    Reliability Engineering & System Safety 191, 106553 (2019)

    Aven, T., Kristensen, V.: How the distinction between general knowledge and specific knowledge can improve the foundation and practice of risk assessment and risk-informed decision-making. Reliability Engineering & System Safety 191, 106553 (2019)

    2019

  44. [47]

    Artificial Intelligence in Medicine 149, 102769 (2024)

    G¨ ond¨ ocs, D., D¨ orfler, V.: Ai in medical diagnosis: Ai prediction & human judgment. Artificial Intelligence in Medicine 149, 102769 (2024)

    2024

  45. [48]

    Stern, P., Fineberg, H.: Understanding risk: Informing decisions in a democratic society (national research council, washington, dc) (1996)

    1996

  46. [49]

    In: 8th International Conference on Prob- abilistic Safety Assessment and Management (2006)

    Stamatelatos, M., Dezfuli, H., Apostolakis, G.: A proposed risk-informed decision-making framework for nasa. In: 8th International Conference on Prob- abilistic Safety Assessment and Management (2006)

    2006

  47. [50]

    Annals of nuclear energy 33(4), 354–369 (2006)

    Reinert, J.M., Apostolakis, G.E.: Including model uncertainty in risk-informed decision making. Annals of nuclear energy 33(4), 354–369 (2006)

    2006

  48. [51]

    Progress in disaster science 15, 100237 (2022)

    Nakum, V.K., Ahamed, M.S., Isetani, S., Chatterjee, R., Shaw, R., Soma, H.: Developing a framework on school resilience for risk-informed decision-making. Progress in disaster science 15, 100237 (2022)

    2022

  49. [52]

    Technical report, Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States); Essex

    McCafferty, D.B.: Annotated bibliography of human factors applications liter- ature. Technical report, Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States); Essex . . . (1984) 27

    1984

  50. [53]

    Technical report, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States); US Nuclear

    Swain, A.D.: Accident sequence evaluation program: Human reliability analysis procedure. Technical report, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States); US Nuclear . . . (1987)

    1987

  51. [54]

    Journal of radiological protection 30(1), 49 (2010)

    Castiglia, F., Giardina, M., Tomarchio, E.: Risk analysis using fuzzy set theory of the accidental exposure of medical staff during brachytherapy procedures. Journal of radiological protection 30(1), 49 (2010)

    2010

  52. [55]

    International Journal of Industrial Ergonomics 67, 242–258 (2018)

    Wang, W., Liu, X., Qin, Y.: A modified heart method with fanp for human error assessment in high-speed railway dispatching tasks. International Journal of Industrial Ergonomics 67, 242–258 (2018)

    2018

  53. [56]

    In: Safety, Reliability and Risk Analysis, pp

    Bladh, K., Holmberg, J.-E., Pyy, P.: An evaluation of the enhanced bayesian therp method using simulator data. In: Safety, Reliability and Risk Analysis, pp. 265–270. CRC Press, ??? (2008)

    2008

  54. [57]

    Radiation Physics and Chemistry 116, 262–266 (2015)

    Castiglia, F., Giardina, M., Tomarchio, E.: Therp and heart integrated method- ology for human error assessment. Radiation Physics and Chemistry 116, 262–266 (2015)

    2015

  55. [58]

    In: IOP Conference Series: Materials Science and Engineering, vol

    Suryoputro, M., Sari, A., Sugarindra, M., Arifin, R.: Machinery safety of lathe machine using sharp-systemic human action reliability procedure: a pilot case study in academic laboratory. In: IOP Conference Series: Materials Science and Engineering, vol. 277, p. 012017 (2017)....

    2017

  56. [59]

    volume i

    Embrey, D., Humphreys, P., Rosa, E., Kirwan, B., Rea, K.: Slim-maud: an approach to assessing human error probabilities using structured expert judg- ment. volume i. overview of slim-maud. Technical report, Brookhaven National Lab., Upton, NY (USA) (1984)

    1984

  57. [60]

    In: Conference Record for 1988 IEEE Fourth Conference on Human Factors and Power Plants,, pp

    Williams, J.: A data-based method for assessing and reducing human error to improve operational performance. In: Conference Record for 1988 IEEE Fourth Conference on Human Factors and Power Plants,, pp. 436–450 (1988). IEEE

    1988

  58. [61]

    Technical report, NUS Corp., San Diego, CA (USA) (1984)

    Hannaman, G., Spurgin, A.: Systematic human action reliability procedure (sharp). Technical report, NUS Corp., San Diego, CA (USA) (1984)

    1984

  59. [62]

    Technical report, Nuclear Regulatory Commission (1996)

    Cooper, S.E., Ramey-Smith, A., Wreathall, J., Parry, G.: A technique for human error analysis (atheana). Technical report, Nuclear Regulatory Commission (1996)

    1996

  60. [63]

    US Nuclear Regulatory Commission 230(4), 35 (2005)

    Gertman, D., Blackman, H., Marble, J., Byers, J., Smith, C., et al.: The spar-h human reliability analysis method. US Nuclear Regulatory Commission 230(4), 35 (2005)

    2005

  61. [64]

    Reliability Engineering & 28 System Safety 145, 301–315 (2016)

    Ekanem, N.J., Mosleh, A., Shen, S.-H.: Phoenix–a model-based human reliability analysis methodology: qualitative analysis procedure. Reliability Engineering & 28 System Safety 145, 301–315 (2016)

    2016

  62. [65]

    Reliability Engineering & System Safety 193, 106672 (2020)

    Ramos, M.A., Droguett, E.L., Mosleh, A., Moura, M.D.C.: A human reliabil- ity analysis methodology for oil refineries and petrochemical plants operation: Phoenix-pro qualitative framework. Reliability Engineering & System Safety 193, 106672 (2020)

    2020

  63. [66]

    Reliability Engineering & System Safety139, 17–32 (2015)

    Di Pasquale, V., Miranda, S., Iannone, R., Riemma, S.: A simulator for human error probability analysis (sherpa). Reliability Engineering & System Safety139, 17–32 (2015)

    2015

  64. [67]

    Safety science 47(2), 250–264 (2009)

    Leva, M.C., De Ambroggi, M., Grippa, D., De Garis, R., Trucco, P., Str¨ ater, O.: Quantitative analysis of atm safety issues using retrospective accident data: The dynamic risk modelling project. Safety science 47(2), 250–264 (2009)

    2009

  65. [68]

    Reliability Engineering & System Safety 92(8), 997–1013 (2007)

    Chang, Y., Mosleh, A.: Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 1: Overview of the idac model. Reliability Engineering & System Safety 92(8), 997–1013 (2007)

    2007

  66. [69]

    Reliability Engineering & System Safety 83(2), 241–253 (2004)

    Mosleh, A., Chang, Y.: Model-based human reliability analysis: prospects and requirements. Reliability Engineering & System Safety 83(2), 241–253 (2004)

    2004

  67. [70]

    In: Probabilistic Safety Assessment and Management: PSAM 7—ESREL’04 June 14–18, 2004, Berlin, Germany, Volume 6, pp

    Hallbert, B.P., Gertman, D.I., Marble, J., Lois, E., Siu, N.: Using information from operating experience to inform human reliability analysis. In: Probabilistic Safety Assessment and Management: PSAM 7—ESREL’04 June 14–18, 2004, Berlin, Germany, Volume 6, pp. 977–982 (2004). Springer

    2004

  68. [71]

    Ocean Engineering 253, 111339 (2022)

    Maya, B.N., Komianos, A., Wood, B., Wolff, L., Kurt, R.E., Turan, O.: A practi- cal application of the hierarchical task analysis (hta) and human error assessment and reduction technique (heart) to identify the major errors with mitigating actions taken after fire detection on...

    2022

  69. [72]

    Nuclear Engineering and Design 439, 114119 (2025)

    Li, Z., Feng, W., Wang, B., Yu, Y.: An improved hra method based on loop accident for multi-unit by spar-h combined with system dynamics. Nuclear Engineering and Design 439, 114119 (2025)

    2025

  70. [73]

    Reliability Engineering & System Safety, 111260 (2025)

    Park, J., Boring, R.L.: Dynamic human reliability analysis using the emrald dynamic risk assessment tool. Reliability Engineering & System Safety, 111260 (2025)

    2025

  71. [74]

    Nuclear Engineering and Technology 40(5), 349–364 (2008)

    Siu, N., Collins, D.: Pra research and the development of risk-informed regulation at the us nuclear regulatory commission. Nuclear Engineering and Technology 40(5), 349–364 (2008)

    2008

  72. [75]

    Procedia Manufacturing 3, 1305–1311 (2015)

    Joe, J.C., Shirley, R.B., Mandelli, D., Boring, R.L., Smith, C.L.: The devel- opment of dynamic human reliability analysis simulations for inclusion in risk 29 informed safety margin characterization frameworks. Procedia Manufacturing 3, 1305–1311 (2015)

    2015

  73. [76]

    In: Proceedings of the PSAM Topical Conference on Human Reliability, Quantitative Human Factors, and Risk Management, Munich, Germany (2017)

    Taylor, C.: Integrating human reliability analysis and human factors engineering for risk-informed plant design and improvement. In: Proceedings of the PSAM Topical Conference on Human Reliability, Quantitative Human Factors, and Risk Management, Munich, Germany (2017)

    2017

  74. [77]

    In: 2022 4th International Conference on System Reliability and Safety Engineering (SRSE), pp

    Shiguang, D., Yunlong, X., Wanting, L.: Research of risk-informed human factors engineering implementation methodology. In: 2022 4th International Conference on System Reliability and Safety Engineering (SRSE), pp. 322–326 (2022). IEEE

    2022

  75. [78]

    In: PSAM Topical Conference, pp

    Petkov, G., Petkov, I.: Team performance comparison in core-melt units of fukushima daiichi nps based on dynamic context quantification of accident. In: PSAM Topical Conference, pp. 7–9 (2017)

    2017

  76. [79]

    Safety science 158, 105962 (2023)

    Bye, A.: Future needs of human reliability analysis: The interaction between new technology, crew roles and performance. Safety science 158, 105962 (2023)

    2023

  77. [80]

    Progress in Nuclear Energy 117, 103050 (2019)

    Alvarenga, M., Melo, P.F.: A review of the cognitive basis for human reliability analysis. Progress in Nuclear Energy 117, 103050 (2019)

    2019

  78. [81]

    Soft Computing 24(4), 2851–2871 (2020)

    Li, N., Huang, J., Feng, Y.: Human performance modeling and its uncertainty factors affecting decision making: a survery. Soft Computing 24(4), 2851–2871 (2020)

    2020

  79. [82]

    Blanchonette, P.: Jack human modelling tool: A review. (2010)

    2010

  80. [83]

    Human Factors and Ergonomics in Manufacturing & Service Industries 20(4), 287–299 (2010)

    Fritzsche, L.: Ergonomics risk assessment with digital human models in car assembly: Simulation versus real life. Human Factors and Ergonomics in Manufacturing & Service Industries 20(4), 287–299 (2010)

    2010

  81. [84]

    Technical report, SAE Technical Paper (2006)

    Reed, M.P., Faraway, J., Chaffin, D.B., Martin, B.J.: The humosim ergonomics framework: A new approach to digital human simulation for ergonomic analysis. Technical report, SAE Technical Paper (2006)

    2006

  82. [85]

    CRC press, ??? (2018)

    Jagacinski, R.J., Flach, J.M.: Control Theory for Humans: Quantitative Approaches to Modeling Performance. CRC press, ??? (2018)

    2018

  83. [86]

    Psychological review 111(4), 1036 (2004)

    Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychological review 111(4), 1036 (2004)

    2004

  84. [87]

    Maxerll AFB

    Boyd, J.: A discourse on winning and losing. Maxerll AFB. Air University Press, Alabama, Curtis E. LeMay Center for Doctrine . . . (2018)

    2018

  85. [88]

    Trends in cognitive sciences 12(4), 136–143 (2008) 30

    Anderson, J.R., Fincham, J.M., Qin, Y., Stocco, A.: A central circuit of the mind. Trends in cognitive sciences 12(4), 136–143 (2008) 30

    2008

  86. [89]

    PhD thesis (2013)

    Cao, S.: Queueing network modeling of human performance in complex cognitive multi-task scenarios. PhD thesis (2013)

    2013

  87. [90]

    Artificial Intelligence for Human Computer Interaction: A Modern Approach, 3–31 (2021)

    Yuan, A., Pfeuffer, K., Li, Y.: Human performance modeling with deep learning. Artificial Intelligence for Human Computer Interaction: A Modern Approach, 3–31 (2021)

    2021

  88. [91]

    Earthscan, ??? (2009)

    Deneulin, S., Shahani, L.: An Introduction to the Human Development and Capability Approach: Freedom and Agency. Earthscan, ??? (2009)

    2009

  89. [92]

    Human Factors 50(3), 489–496 (2008)

    Pew, R.W.: More than 50 years of history and accomplishments in human performance model development. Human Factors 50(3), 489–496 (2008)

    2008

  90. [93]

    Journal of experimental psychology 47(6), 381 (1954)

    Fitts, P.M.: The information capacity of the human motor system in control- ling the amplitude of movement. Journal of experimental psychology 47(6), 381 (1954)

    1954

  91. [94]

    Quarterly Journal of experimen- tal psychology 4(1), 11–26 (1952)

    Hick, W.E.: On the rate of gain of information. Quarterly Journal of experimen- tal psychology 4(1), 11–26 (1952)

    1952

  92. [95]

    Artificial intelligence 33(1), 1–64 (1987)

    Laird, J.E., Newell, A., Rosenbloom, P.S.: Soar: An architecture for general intelligence. Artificial intelligence 33(1), 1–64 (1987)

    1987

  93. [96]

    Cognition 55(2), 115–149 (1995)

    Cooper, R., Shallice, T.: Soar and the case for unified theories of cognition. Cognition 55(2), 115–149 (1995)

    1995

  94. [97]

    Unpub- lished manuscript from ftp://ftp

    Kieras, D.E., Meyer, D.E.: The epic architecture: Principles of operation. Unpub- lished manuscript from ftp://ftp. eecs. umich. edu/people/kieras/EPICarch. ps (1996)

    1996

  95. [98]

    Human– Computer Interaction 12(4), 391–438 (1997)

    Kieras, D.E., Meyer, D.E.: An overview of the epic architecture for cognition and performance with application to human-computer interaction. Human– Computer Interaction 12(4), 391–438 (1997)

    1997

  96. [99]

    Wiley Interdisciplinary Reviews: Cognitive Science 10(3), 1488 (2019)

    Ritter, F.E., Tehranchi, F., Oury, J.D.: Act-r: A cognitive architecture for mod- eling cognition. Wiley Interdisciplinary Reviews: Cognitive Science 10(3), 1488 (2019)

    2019

  97. [100]

    ACM Transactions on Computer-Human Interaction (TOCHI) 13(1), 37–70 (2006)

    Liu, Y., Feyen, R., Tsimhoni, O.: Queueing network-model human processor (qn- mhp) a computational architecture for multitask performance in human-machine systems. ACM Transactions on Computer-Human Interaction (TOCHI) 13(1), 37–70 (2006)

    2006

  98. [101]

    IEEE transactions on intelligent transportation systems 23(9), 14790–14805 (2022) 31

    Zhang, Y., Wu, C., Qiao, C., Sadek, A., Hulme, K.F.: A cognitive computational model of driver warning response performance in connected vehicle systems. IEEE transactions on intelligent transportation systems 23(9), 14790–14805 (2022) 31

    2022

  99. [102]

    a global perspective

    Wahr, J.A.: Human error–cognitive processes and interventions to improve safety. a global perspective. Education for anaesthesia providers worldwide, 7 (2024)

    2024

  100. [103]

    US Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, ??? (2016)

    Whaley, A.M.: Cognitive Basis for Human Reliability Analysis. US Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, ??? (2016)

    2016

  101. [104]

    Safety science 70, 19–28 (2014)

    Akyuz, E., Celik, M.: Utilisation of cognitive map in modelling human error in marine accident analysis and prevention. Safety science 70, 19–28 (2014)

    2014

  102. [105]

    Ergonomics 56(1), 1–15 (2013)

    Plant, K.L., Stanton, N.A.: The explanatory power of schema theory: theoreti- cal foundations and future applications in ergonomics. Ergonomics 56(1), 1–15 (2013)

    2013

  103. [106]

    Reliability Engineering & System Safety 75(2), 257–272 (2002)

    Isaac, A., Shorrock, S.T., Kirwan, B.: Human error in european air traffic management: the hera project. Reliability Engineering & System Safety 75(2), 257–272 (2002)

    2002

  104. [107]

    IEEE Communications Surveys & Tutorials 26(1), 706– 746 (2023)

    Chen, J., Yi, C., Okegbile, S.D., Cai, J., Shen, X.: Networking architecture and key supporting technologies for human digital twin in personalized healthcare: A comprehensive survey. IEEE Communications Surveys & Tutorials 26(1), 706– 746 (2023)

    2023

  105. [108]

    In: History of Programming Languages, pp

    McCarthy, J.: History of lisp. In: History of Programming Languages, pp. 173– 185 (1978)

    1978

  106. [109]

    Science Advances 6(16), 2631 (2020)

    Udrescu, S.-M., Tegmark, M.: Ai feynman: A physics-inspired method for symbolic regression. Science Advances 6(16), 2631 (2020)

    2020

  107. [110]

    Computer science review 15, 29–62 (2015)

    Ruijters, E., Stoelinga, M.: Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Computer science review 15, 29–62 (2015)

    2015

  108. [111]

    Nature Reviews Methods Primers 1(1), 1 (2021)

    Schoot, R., Depaoli, S., King, R., Kramer, B., M¨ artens, K., Tadesse, M.G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., et al.: Bayesian statistics and modelling. Nature Reviews Methods Primers 1(1), 1 (2021)

    2021

  109. [112]

    Review of scientific instruments 65(6), 1803–1832 (1994)

    Bishop, C.M.: Neural networks and their applications. Review of scientific instruments 65(6), 1803–1832 (1994)

    1994

  110. [113]

    In: Computational Intelligence: a Methodological Introduction, pp

    Kruse, R., Mostaghim, S., Borgelt, C., Braune, C., Steinbrecher, M.: Multi-layer perceptrons. In: Computational Intelligence: a Methodological Introduction, pp. 53–124. Springer, ??? (2022)

    2022

  111. [114]

    IEEE transactions on neural networks and learning systems 33(12), 6999–7019 (2021) 32

    Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems 33(12), 6999–7019 (2021) 32

    2021

  112. [115]

    Design and Applica- tions 5(64-67), 2 (2001)

    Medsker, L.R., Jain, L., et al.: Recurrent neural networks. Design and Applica- tions 5(64-67), 2 (2001)

    2001

  113. [116]

    Advances in Neural Information Processing Systems (2017)

    Vaswani, A.: Attention is all you need. Advances in Neural Information Processing Systems (2017)

    2017

  114. [117]

    Journal of artificial intelligence research 4, 237–285 (1996)

    Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: A survey. Journal of artificial intelligence research 4, 237–285 (1996)

    1996

  115. [118]

    Nature medicine29(8), 1930– 1940 (2023)

    Thirunavukarasu, A.J., Ting, D.S.J., Elangovan, K., Gutierrez, L., Tan, T.F., Ting, D.S.W.: Large language models in medicine. Nature medicine29(8), 1930– 1940 (2023)

    2023

  116. [119]

    arXiv preprint arXiv:2307.10169 (2023)

    Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., McHardy, R.: Challenges and applications of large language models. arXiv preprint arXiv:2307.10169 (2023)

    2023 arXiv

  117. [120]

    ACM Transactions on Intelligent Systems and Technology 15(3), 1–45 (2024)

    Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al.: A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology 15(3), 1–45 (2024)

    2024

  118. [121]

    Engineering Applications of Artificial Intelligence 25(4), 671–682 (2012)

    Weber, P., Medina-Oliva, G., Simon, C., Iung, B.: Overview on bayesian net- works applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence 25(4), 671–682 (2012)

    2012

  119. [126]

    Reliability Engineering & System Safety 33 208, 107392 (2021)

    Parhizkar, T., Utne, I.B., Vinnem, J.E., Mosleh, A.: Supervised dynamic proba- bilistic risk assessment of complex systems, part 2: Application to risk-informed decision making, practice and results. Reliability Engineering & System Safety 33 208, 107392 (2021)

    2021

  120. [128]

    International journal of human-computer studies 54(4), 509–540 (2001)

    Hoc, J.-M.: Towards a cognitive approach to human–machine cooperation in dynamic situations. International journal of human-computer studies 54(4), 509–540 (2001)

    2001

  121. [129]

    In: Proc

    Maanen, P.-P., Lindenberg, J., Neerincx, M.A.: Integrating human factors and artificial intelligence in the development of human-machine cooperation. In: Proc. of the 2005 International Conference on Artificial Intelligence (ICAI’05) (2005)

    2005

  122. [130]

    Proceedings of the ACM on Human-Computer Interaction 4(CSCW3), 1–25 (2021)

    Zhang, R., McNeese, N.J., Freeman, G., Musick, G.: ” an ideal human” expec- tations of ai teammates in human-ai teaming. Proceedings of the ACM on Human-Computer Interaction 4(CSCW3), 1–25 (2021)

    2021

  123. [131]

    arXiv preprint arXiv:1805.01109 (2018)

    Everitt, T., Lea, G., Hutter, M.: Agi safety literature review. arXiv preprint arXiv:1805.01109 (2018)

    2018 arXiv

  124. [132]

    Computer Law & Security Review 32(5), 749–758 (2016)

    Gurkaynak, G., Yilmaz, I., Haksever, G.: Stifling artificial intelligence: Human perils. Computer Law & Security Review 32(5), 749–758 (2016)

    2016

  125. [133]

    Business horizons 62(1), 15–25 (2019)

    Kaplan, A., Haenlein, M.: Siri, siri, in my hand: Who’s the fairest in the land? on the interpretations, illustrations, and implications of artificial intelligence. Business horizons 62(1), 15–25 (2019)

    2019

  126. [134]

    Human factors and ergonomics in manufacturing & service industries 31(2), 223–236 (2021)

    Salmon, P.M., Carden, T., Hancock, P.A.: Putting the humanity into inhuman systems: How human factors and ergonomics can be used to manage the risks associated with artificial general intelligence. Human factors and ergonomics in manufacturing & service industries 31(2), 223–2...

    2021

  127. [135]

    Proceedings of the VLDB Endowment 11(12), 1781–1794 (2018)

    Schelter, S., Lange, D., Schmidt, P., Celikel, M., Biessmann, F., Grafberger, A.: Automating large-scale data quality verification. Proceedings of the VLDB Endowment 11(12), 1781–1794 (2018)

    2018

  128. [136]

    Psychological science 4(6), 385–390 (1993)

    Gehring, W.J., Goss, B., Coles, M.G., Meyer, D.E., Donchin, E.: A neural sys- tem for error detection and compensation. Psychological science 4(6), 385–390 (1993)

    1993

  129. [137]

    Ai Magazine 35(4), 75–104 (2014)

    Robertson, G., Watson, I.: A review of real-time strategy game ai. Ai Magazine 35(4), 75–104 (2014)

    2014

  130. [138]

    Safety science 130, 104838 (2020)

    Zhang, M., Zhang, D., Yao, H., Zhang, K.: A probabilistic model of human 34 error assessment for autonomous cargo ships focusing on human–autonomy collaboration. Safety science 130, 104838 (2020)

    2020

  131. [139]

    Journal of Loss Prevention in the Process Industries 26(4), 639–649 (2013)

    Cai, B., Liu, Y., Zhang, Y., Fan, Q., Liu, Z., Tian, X.: A dynamic bayesian networks modeling of human factors on offshore blowouts. Journal of Loss Prevention in the Process Industries 26(4), 639–649 (2013)

    2013

  132. [140]

    Ocean engineering 58, 293–303 (2013)

    Yang, Z., Bonsall, S., Wall, A., Wang, J., Usman, M.: A modified cream to human reliability quantification in marine engineering. Ocean engineering 58, 293–303 (2013)

    2013

  133. [141]

    IEEE Transactions on Reliability 57(3), 517–528 (2008)

    Yang, Z., Bonsall, S., Wang, J.: Fuzzy rule-based bayesian reasoning approach for prioritization of failures in fmea. IEEE Transactions on Reliability 57(3), 517–528 (2008)

    2008

  134. [142]

    Annals of Operations Research, 1–15 (2022)

    Li, Y.-F., Huang, H.-Z., Mi, J., Peng, W., Han, X.: Reliability analysis of multi- state systems with common cause failures based on bayesian network and fuzzy probability. Annals of Operations Research, 1–15 (2022)

    2022

  135. [143]

    Journal of loss prevention in the process industries 57, 142–155 (2019)

    Zarei, E., Yazdi, M., Abbassi, R., Khan, F.: A hybrid model for human factor analysis in process accidents: Fbn-hfacs. Journal of loss prevention in the process industries 57, 142–155 (2019)

    2019

  136. [144]

    IEEE Access 8, 105484–105493 (2020)

    Chen, B., Liu, Y., Zhang, C., Wang, Z.: Time series data for equipment reliability analysis with deep learning. IEEE Access 8, 105484–105493 (2020)

    2020

  137. [145]

    Energies 17(1), 159 (2023)

    Xiao, X., Qi, B., Liang, J., Tong, J., Deng, Q., Chen, P.: Enhancing loca breach size diagnosis with fundamental deep learning models and optimized dataset construction. Energies 17(1), 159 (2023)

    2023

  138. [146]

    Progress in Nuclear Energy 177, 105421 (2024)

    Qi, B., Sun, J., Sui, Z., Xiao, X., Liang, J.: Multimodal learning using large language models to improve transient identification of nuclear power plants. Progress in Nuclear Energy 177, 105421 (2024)

    2024

  139. [147]

    Xie, C., Chen, C., Jia, F., Ye, Z., Shu, K., Bibi, A., Hu, Z., Torr, P., Ghanem, B., Li, G.: Can large language model agents simulate human trust behaviors? arXiv preprint arXiv:2402.04559 (2024)

    2024 arXiv

  140. [148]

    In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp

    Li, N., Gao, C., Li, M., Li, Y., Liao, Q.: Econagent: large language model- empowered agents for simulating macroeconomic activities. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 15523–15536 (2024)

    2024

  141. [149]

    arXiv preprint arXiv:2310.02124 (2023)

    Zhang, J., Xu, X., Deng, S.: Exploring collaboration mechanisms for llm agents: A social psychology view. arXiv preprint arXiv:2310.02124 (2023)

    2023 arXiv

  142. [150]

    In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp

    Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M.T., 35 Weld, D.: Does the whole exceed its parts? the effect of ai explanations on complementary team performance. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–16 (2021)

    2021

  143. [151]

    Science and Engineering Ethics 26(5), 2749–2767 (2020)

    Ryan, M.: In ai we trust: ethics, artificial intelligence, and reliability. Science and Engineering Ethics 26(5), 2749–2767 (2020)

    2020

  144. [152]

    Human Factors in Healthcare 2, 100021 (2022)

    Choudhury, A., Asan, O.: Impact of accountability, training, and human fac- tors on the use of artificial intelligence in healthcare: Exploring the perceptions of healthcare practitioners in the us. Human Factors in Healthcare 2, 100021 (2022)

    2022

  145. [153]

    In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pp

    Wang, D., Churchill, E., Maes, P., Fan, X., Shneiderman, B., Shi, Y., Wang, Q.: From human-human collaboration to human-ai collaboration: Designing ai systems that can work together with people. In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Sy...

    2020

  146. [154]

    Advanced Materials 34(16), 2107902 (2022)

    Heng, W., Solomon, S., Gao, W.: Flexible electronics and devices as human– machine interfaces for medical robotics. Advanced Materials 34(16), 2107902 (2022)

    2022

  147. [155]

    IEEE Intelligent transportation systems magazine 6(4), 6–22 (2014)

    Bengler, K., Dietmayer, K., Farber, B., Maurer, M., Stiller, C., Winner, H.: Three decades of driver assistance systems: Review and future perspectives. IEEE Intelligent transportation systems magazine 6(4), 6–22 (2014)

    2014

  148. [156]

    arXiv preprint arXiv:2311.16565 (2023)

    Chen, P., Wei, X., Lu, M., Zhu, Y., Yao, N., Xiao, X., Chen, H.: Diffu- siontalker: Personalization and acceleration for speech-driven 3d face diffuser. arXiv preprint arXiv:2311.16565 (2023)

    2023 arXiv

  149. [157]

    Journal of Neuroscience 30(46), 15643–15653 (2010)

    Steinhauser, M., Yeung, N.: Decision processes in human performance monitor- ing. Journal of Neuroscience 30(46), 15643–15653 (2010)

    2010

  150. [158]

    Radiology 264(2), 473–483 (2012)

    Morbi, A.H., Hamady, M.S., Riga, C.V., Kashef, E., Pearch, B.J., Vincent, C., Moorthy, K., Vats, A., Cheshire, N.J., Bicknell, C.D.: Reducing error and improving efficiency during vascular interventional radiology: implementation of a preprocedural team rehearsal. Radiology 26...

    2012

  151. [159]

    Journal of intelligent manufacturing 15, 491–503 (2004)

    Albayrak, E., Erensal, Y.C.: Using analytic hierarchy process (ahp) to improve human performance: An application of multiple criteria decision making prob- lem. Journal of intelligent manufacturing 15, 491–503 (2004)

    2004

  152. [160]

    International Security 46(3), 7–50 (2021)

    Goldfarb, A., Lindsay, J.R.: Prediction and judgment: Why artificial intelligence increases the importance of humans in war. International Security 46(3), 7–50 (2021)

    2021

  153. [161]

    Oxford University Press, ??? (2012) 36

    Oktay, J.S.: Grounded Theory. Oxford University Press, ??? (2012) 36

    2012

  154. [162]

    Lenskjold, A., Nybing, J.U., Trampedach, C., Galsgaard, A., Brejnebøl, M.W., Raaschou, H., Rose, M.H., Boesen, M.: Should artificial intelligence have lower acceptable error rates than humans? BJR— Open 5(1), 20220053 (2023)

    2023

  155. [163]

    Cultura e societ` a digitali (2021)

    Barassi, V.: The human error of artificial intelligence. Cultura e societ` a digitali (2021)

    2021

  156. [164]

    JMIR human factors 8(2), 28236 (2021)

    Asan, O., Choudhury, A.: Research trends in artificial intelligence applications in human factors health care: mapping review. JMIR human factors 8(2), 28236 (2021)

    2021

  157. [165]

    Cognition, Technology & Work 5, 272–282 (2003)

    Besnard, D., Greathead, D.: A cognitive approach to safe violations. Cognition, Technology & Work 5, 272–282 (2003)

    2003

  158. [166]

    Lye, A., Chang, J., Xiao, S., Chung, K.Y.: An overview of probabilistic safety assessment for nuclear safety: What has been done, and where do we go from here? Journal of Nuclear Engineering 5(4), 456–485 (2024) 37

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.