{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T09:50:57Z","timestamp":1791366657557,"version":"4.3.1"},"reference-count":85,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Risk Analysis"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The growing integration of artificial intelligence (AI)\u2010driven technologies into nuclear power plant (NPP) operations and maintenance holds significant potential to enhance safety, operational efficiency, and reliability. However, deploying such AI\u2010driven automation technologies in high\u2010consequence settings requires a rigorous, traceable, and risk\u2010informed evaluation of their trustworthiness. Although researchers across various domains have proposed definitions and metrics for AI\u2010driven automation trustworthiness, no agreed\u2010upon formal or quantitative method exists that is suitable for the highly regulated nuclear sector. To address this gap, this paper, the first in a two\u2010part series, presents three contributions: (I) a cross\u2010disciplinary literature review that categorizes existing definitions and evaluation methodologies for AI\u2010driven automation trustworthiness and identifies their inherent limitations with respect to applicability in the nuclear domain; (II) develops a new definition of AI\u2010driven automation trustworthiness as \u201cthe degree of confidence that the AI\u2010driven automation system will function as expected across its conditions of use\u201d to address the limitations identified in Contribution I; and (III) building on the new definition, conceptualizes the Socio\u2010Technical Risk\u2010informed AI\u2010driven Automation Trustworthiness Evaluation (ST\u2010RATE) methodology, grounded in three foundational pillars: (a) risk\u2010informed performance expectations, (b) socio\u2010technical context across operational conditions, and (c) uncertainty\u2010based trustworthiness evaluation. While developed primarily for the nuclear domain, the definition and ST\u2010RATE methodology has the potential to be adapted for other safety\u2010critical domains with appropriate modifications. The ST\u2010RATE methodological framework is operationalized and demonstrated through an AI\u2010driven automated firewatch case study in a companion paper (Companion paper Part 2, 2025). ST\u2010RATE is the first\u2010of\u2010its\u2010kind methodology that enables risk\u2010informed and uncertainty\u2010based quantification of trustworthiness despite limited empirical data and without over\u2010reliance on subjective expert judgment, addressing the key limitations of existing approaches identified in the literature review.<\/jats:p>","DOI":"10.1111\/risa.70367","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T09:03:00Z","timestamp":1791363780000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Socio\u2010Technical Risk\u2010Informed AI\u2010Driven Automation Trustworthiness Evaluation (ST\u2010RATE): Part 1\u2010Conceptualization"],"prefix":"10.1111","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2027-6659","authenticated-orcid":false,"given":"Muhammad Hammad","family":"Khalid","sequence":"first","affiliation":[{"name":"Socio\u2010Technical Risk Analysis (SoTeRiA) Laboratory  Urbana Illinois USA"},{"name":"Department of Nuclear, Plasma, and Radiological Engineering University of Illinois at Urbana\u2010 Champaign  Urbana Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ha","family":"Bui","sequence":"additional","affiliation":[{"name":"Socio\u2010Technical Risk Analysis (SoTeRiA) Laboratory  Urbana Illinois USA"},{"name":"Department of Nuclear, Plasma, and Radiological Engineering University of Illinois at Urbana\u2010 Champaign  Urbana Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad Al","family":"Rashdan","sequence":"additional","affiliation":[{"name":"Idaho National Laboratory (INL)  Idaho Falls Idaho USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Mohaghegh","sequence":"additional","affiliation":[{"name":"Socio\u2010Technical Risk Analysis (SoTeRiA) Laboratory  Urbana Illinois USA"},{"name":"Department of Nuclear, Plasma, and Radiological Engineering University of Illinois at Urbana\u2010 Champaign  Urbana Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"Agarwal V. C. M.Walker K. A.Manjunatha et\u00a0al.2022. \u201cTechnical Basis for Advanced Artificial Intelligence and Machine Learning Adoption in Nuclear Power Plants.\u201d INL\/RPT\u201022\u201068942\u2010Rev000.Idaho Falls ID:Idaho National Laboratory."},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"Ahmed H. andG.Lofstead.2021. \u201cQuantifying the Crisis in Reproducible Machine Learning.\u201d SAND2021\u201010258C.Albuquerque NM:Sandia National Laboratories.","DOI":"10.2172\/1883039"},{"key":"e_1_2_9_4_1","unstructured":"Al Rashdan A. M.Griffel andL.Powell.2019. \u201cAutomating Fire Watch in Industrial Environments Through Machine Learning\u2010Enabled Visual Monitoring.\u201d INL\/EXT\u201019\u20105570.Idaho Falls ID:Idaho National Laboratory."},{"key":"e_1_2_9_5_1","unstructured":"Al Rashdan A. Y. R.Shaffer E. L.Quinn M.Bailey R.Jarrett andK. M.Giraud.2021. \u201cConsiderations Regarding the Use of Computer Vision Machine Learning in Safety\u2010Related or Risk\u2010Significant Applications in Nuclear Power Plants.\u201d INL\/RPT\u201023\u201074920\u2010Rev000.Idaho Falls ID:Idaho National Laboratory."},{"key":"e_1_2_9_6_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.pnucene.2022.104559","article-title":"Automation Levels for Nuclear Reactor Operations: A Revised Perspective","volume":"157","author":"Alberti A. L.","year":"2023","journal-title":"Progress in Nuclear Energy"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101805","article-title":"Explainable Artificial Intelligence (XAI): What We Know and What Is Left to Attain Trustworthy Artificial Intelligence","volume":"99","author":"Ali S.","year":"2023","journal-title":"Information fusion"},{"key":"e_1_2_9_8_1","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1177\/21695067231192575","article-title":"Levels of Digitization, Digitalization, and Automation for Advanced Reactors","volume":"67","author":"Boring R. L.","year":"2023","journal-title":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting"},{"issue":"4","key":"e_1_2_9_9_1","doi-asserted-by":"crossref","first-page":"500","DOI":"10.2967\/jnumed.121.262567","article-title":"Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development","volume":"63","author":"Bradshaw T. J.","year":"2022","journal-title":"Journal of Nuclear Medicine"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"Buluc A. T. G.Kolda S. M.Wild et\u00a0al.2021. \u201cRandomized Algorithms for Scientific Computing (RASC).\u201d arXiv:2104.11079.","DOI":"10.2172\/1807223"},{"key":"e_1_2_9_11_1","unstructured":"Cetiner S. M. andP.Ramuhalli.2019. \u201cTransformational Challenge Reactor Autonomous Control System Framework and Key Enabling Technologies.\u201d ORNL\/SPR\u20102019\/1178.Oak Ridge TN:Oak Ridge National Laboratory."},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","unstructured":"Chen Y. C. M.Poskitt andJ.Sun.2021. \u201cCode Integrity Attestation for PLCs Using Black Box Neural Network Predictions.\u201d InProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering 32\u201344.Association for Computing Machinery.","DOI":"10.1145\/3468264.3468617"},{"key":"e_1_2_9_13_1","unstructured":"CNSC (Canadian Nuclear Safety Commission) ONR (UK Office for Nuclear Regulation) and U.S. NRC (U.S. Nuclear Regulatory Commission).2024. \u201cConsiderations for Developing Artificial Intelligence Systems in Nuclear Applications.\u201d September."},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"Dave A. J. T. N.Nguyen andR. B.Vilim.2024. \u201cIntegrating LLMs for Explainable Fault Diagnosis in Complex Systems.\u201d arXiv:2402.06695.","DOI":"10.13182\/T130-44845"},{"key":"e_1_2_9_15_1","unstructured":"Elks C. R. A.Tantawy R.Hite S.Gautham A.Jayakumar andC.Deloglos.2019.Realizing Verifiable I&C and Embedded Digital Devices for Nuclear Power.U.S. Department of Energy."},{"key":"e_1_2_9_16_1","unstructured":"EPRI (Electric Power Research Institute).2022. \u201cProgram on Technology Innovation: Landscape of Automation in Nuclear Power Plants.\u201d 3002025693.Palo Alto CA:Electric Power Research Institute."},{"issue":"4","key":"e_1_2_9_17_1","first-page":"136","article-title":"Methods of Explainable Artificial Intelligence (XAI), Trustworthy Artificial Intelligence (TAI) and Interpretable Machine Learning (IML) in Renewable Energy","volume":"6","author":"Ers\u00f6z B.","year":"2022","journal-title":"International Journal of Smart Grid\u2010ijSmartGrid"},{"key":"e_1_2_9_18_1","unstructured":"Findrik M. I.Friedberg E.Piatkowska P.Smith andJ.Song.2017. \u201cTrustworthy Computer Security Incident Response for Nuclear Facilities.\u201d InProceedings of the 4th International Symposium on Future Instrumentation and Control for Nuclear Power Plants (ISOFIC'17) 1\u201310.Gyeongju South Korea."},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/robotics10020067"},{"key":"e_1_2_9_20_1","unstructured":"Franco D.2024. \u201cTowards Trustworthiness in Artificial Intelligence: Pushing for Explainable Fair Robust and Private Supervised Machine Learning.\u201d."},{"key":"e_1_2_9_21_1","unstructured":"Gallear G. S.2019. \u201cImproving Nuclear Mass Predictions Using Machine Learning Algorithms.\u201d Master's thesis San Diego State University."},{"key":"e_1_2_9_22_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.pnucene.2024.105295","article-title":"Barriers to Adopting Artificial Intelligence and Machine Learning Technologies in Nuclear Power","volume":"175","author":"Hall A.","year":"2024","journal-title":"Progress in Nuclear Energy"},{"key":"e_1_2_9_23_1","doi-asserted-by":"crossref","unstructured":"Hanna B. N. L.Lin P.Athe T. C.Son andN. T.Dinh.2021. \u201cTrusting Machine Learning in Nuclear Plant Control: a Reasoning\u2010Based Discrepancy Checker.\u201d InProceedings of the 12th Nuclear Plant Instrumentation Control and Human\u2010Machine Interface Technologies (NPIC&HMIT 2021) 272\u2013284.La Grange Park IL:American Nuclear Society.","DOI":"10.13182\/T124-34310"},{"issue":"6","key":"e_1_2_9_24_1","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1017\/S1471068420000241","article-title":"An Application of ASP in Nuclear Engineering: Explaining the Three Mile Island Nuclear Accident Scenario","volume":"20","author":"Hanna B. N.","year":"2020","journal-title":"Theory and Practice of Logic Programming"},{"key":"e_1_2_9_25_1","doi-asserted-by":"crossref","unstructured":"Henderson A. S.Harbour andK.Cohen.2022. \u201cToward Airworthiness Certification for Artificial Intelligence (AI) in Aerospace Systems.\u201d Paper presented at the 2022 IEEE\/AIAA 41st Digital Avionics Systems Conference (DASC) Portsmouth VA September 18\u201322.","DOI":"10.1109\/DASC55683.2022.9925740"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1177\/0018720814547570"},{"key":"e_1_2_9_27_1","unstructured":"Holt M.2023. \u201cAdvanced Nuclear Reactors: Technology Overview and Current Issues.\u201d R45706.Washington DC:Congressional Research Service."},{"issue":"11","key":"e_1_2_9_28_1","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1002\/ajim.23037","article-title":"Artificial Intelligence: Implications for the Future of Work","volume":"62","author":"Howard J.","year":"2019","journal-title":"American Journal of Industrial Medicine"},{"key":"e_1_2_9_29_1","unstructured":"IAEA (International Atomic Energy Agency).2025. \u201cConsiderations for Deploying Artificial Intelligence Applications in the Nuclear Power Industry.\u201d IAEA Nuclear Energy Series No. NR\u2010T\u20101.26.Vienna:International Atomic Energy Agency."},{"key":"e_1_2_9_30_1","unstructured":"Karra S. B.Ahmmed andM. K.Mudunuru.2021. \u201cAdjointNet: Constraining Machine Learning Models With Physics\u2010Based Codes.\u201d Preprint arXiv September 8."},{"key":"e_1_2_9_31_1","unstructured":"Khalid M. H. M. I.Ahmed H.Bui S.Reihani A.Al Rashdan andZ.Mohaghegh.2026. \u201cSocio\u2010Technical Risk\u2010Informed Automation Trustworthiness Evaluation (ST\u2010RATE): Part 2: Methodological and Computational Framework and Application to an AI\u2010Driven Automated Firewatch System in Nuclear Power Plants.\u201d Manuscript under revision Risk Analysis."},{"issue":"9","key":"e_1_2_9_32_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3648608","article-title":"Resilient Machine Learning: Advancement, Barriers, and Opportunities in the Nuclear Industry","volume":"56","author":"Khadka A.","year":"2024","journal-title":"ACM Computing Surveys"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1177\/1748006X261440735"},{"key":"e_1_2_9_34_1","doi-asserted-by":"crossref","unstructured":"Kim H. andJ.Kim.2025. \u201cTaxonomies of Levels of Automation for Nuclear Power Plants Based on Operational Conditions.\u201d InProceedings of the Nuclear Plant Instrumentation and Control & Human\u2010Machine Interface Technology (NPIC&HMIT 2025) 582\u2013591.La Grange Park IL:American Nuclear Society.","DOI":"10.13182\/xyz-46958"},{"key":"e_1_2_9_35_1","doi-asserted-by":"crossref","first-page":"55777","DOI":"10.1109\/ACCESS.2021.3071552","article-title":"Trustworthy Building Fire Detection Framework With Simulation\u2010Based Learning","volume":"9","author":"Kim Y.\u2010J.","year":"2021","journal-title":"IEEE Access"},{"issue":"1","key":"e_1_2_9_36_1","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/S0029-5493(00)00430-1","article-title":"Development of Advanced I&C in Nuclear Power Plants: ADIOS and ASICS","volume":"207","author":"Kim J.\u2010T.","year":"2001","journal-title":"Nuclear Engineering and Design"},{"key":"e_1_2_9_37_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.pnucene.2025.105605","article-title":"Quantitative Comparison of Explainable Artificial Intelligence Methods for Nuclear Power Plant Accident Diagnosis Models","volume":"180","author":"Kim S. G.","year":"2025","journal-title":"Progress in Nuclear Energy"},{"key":"e_1_2_9_38_1","unstructured":"Kima Y. andJ.Park.2018. \u201cEnvisioning Human\u2010Automation Interactions for Responding Emergency Situations of NPPs: a Viewpoint From Human Computer Interaction.\u201d InProceedings of the Transactions of the Korean Nuclear Society Autumn Meeting."},{"key":"e_1_2_9_39_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107620","article-title":"Explainable, Interpretable, and Trustworthy AI for an Intelligent Digital Twin: a Case Study on Remaining Useful Life","volume":"129","author":"Kobayashi K.","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"e_1_2_9_40_1","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyg.2021.604977","article-title":"Measurement of Trust in Automation: a Narrative Review and Reference Guide","volume":"12","author":"Kohn S. C.","year":"2021","journal-title":"Frontiers in Psychology"},{"key":"e_1_2_9_41_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.anucene.2022.109293","article-title":"Machine Learning for Analysis of Real Nuclear Plant Data in the Frequency Domain","volume":"177","author":"Kollias S.","year":"2022","journal-title":"Annals of Nuclear Energy"},{"key":"e_1_2_9_42_1","doi-asserted-by":"crossref","unstructured":"Kovesdi C. R. Z. A.Spielman J. D.Mohon T. M.Miyake R. A.Hill andC.Pederson.2021. \u201cDevelopment of an Assessment Methodology That Enables the Nuclear Industry to Evaluate Adoption of Advanced Automation.\u201d INL\/EXT\u201021\u201064320\u2010Rev000.Idaho Falls ID:Idaho National Laboratory.","DOI":"10.2172\/1822880"},{"issue":"1","key":"e_1_2_9_43_1","doi-asserted-by":"crossref","DOI":"10.1177\/20539517231153811","article-title":"European Artificial Intelligence Policy as Digital Single Market Making","volume":"10","author":"Krarup T.","year":"2023","journal-title":"Big Data & Society"},{"key":"e_1_2_9_44_1","doi-asserted-by":"publisher","DOI":"10.1518\/hfes.46.1.50_30392"},{"key":"e_1_2_9_45_1","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.anucene.2018.05.020","article-title":"Autonomous Operation Algorithm for Safety Systems of Nuclear Power Plants by Using Long\u2010Short Term Memory and Function\u2010Based Hierarchical Framework","volume":"119","author":"Lee D.","year":"2018","journal-title":"Annals of Nuclear Energy"},{"key":"e_1_2_9_46_1","doi-asserted-by":"publisher","DOI":"10.1177\/1071181319631532"},{"key":"e_1_2_9_47_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.anucene.2020.107861","article-title":"Development and Assessment of a Nearly Autonomous Management and Control System for Advanced Reactors","volume":"150","author":"Lin L.","year":"2021","journal-title":"Annals of Nuclear Energy"},{"key":"e_1_2_9_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.anucene.2021.108362"},{"issue":"9","key":"e_1_2_9_49_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.net.2025.103588","article-title":"Explainable Machine\u2010Learning Tools for Predictive Maintenance of Circulating Water Systems in Nuclear Power Plants","volume":"57","author":"Lin L.","year":"2025","journal-title":"Nuclear Engineering and Technology"},{"key":"e_1_2_9_50_1","unstructured":"Mahdavifar S.2021. \u201cExplainable Deep Learning for Detecting Cyber Threats.\u201d PhD diss. University of New Brunswick."},{"key":"e_1_2_9_51_1","first-page":"33","volume-title":"Proceedings of the International Conference on Data Management, Analytics & Innovation","author":"Manchanda S.","year":"2022"},{"key":"e_1_2_9_52_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.envc.2021.100396","article-title":"Delineation of Groundwater Potential Zones Using the Integration of Geospatial and MIF Techniques: A Case Study on Rarh Region of West Bengal, India","volume":"5","author":"Mandal P.","year":"2021","journal-title":"Environmental Challenges"},{"key":"e_1_2_9_53_1","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1080\/00295450.2024.2411153","article-title":"Human Factors Considerations in Artificial Intelligence Applications for Nuclear Power Plants","volume":"211","author":"Mortenson T.","year":"2024","journal-title":"Nuclear Technology"},{"key":"e_1_2_9_54_1","doi-asserted-by":"crossref","unstructured":"Muhlheim M. D. P.Ramuhalli A.Huning A.Guler Yigitoglu andA.Saxena.2023.Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML).Oak Ridge National Laboratory.","DOI":"10.2172\/1883838"},{"key":"e_1_2_9_55_1","doi-asserted-by":"crossref","unstructured":"Muhlheim M. P.Ramuhalli A.Huning A. G.Yigitoglu A.Saxena andR. T.Wood.2022. \u201cHow Digital Twins May Be Used in Design and Operations.\u201dOak Ridge TN:Oak Ridge National Laboratory.","DOI":"10.13182\/T130-39790"},{"key":"e_1_2_9_56_1","doi-asserted-by":"crossref","unstructured":"Naranjo de Candido I. A.Al Rashdan A. A.Jaoude andJ.Buongiorno.2024. \u201cAssessment of Technoeconomic Opportunities in Automation for Nuclear Microreactors.\u201d Nuclear Science and Engineering: 1\u201320.","DOI":"10.1080\/00295639.2024.2372511"},{"key":"e_1_2_9_57_1","unstructured":"Naser J.2005.Guidance for the Design and Use of Automation in Nuclear Power Plants. 2005.Electric Power Research Institute."},{"issue":"3","key":"e_1_2_9_58_1","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/3468.844354","article-title":"A Model for Types and Levels of human Interaction With Automation","volume":"30","author":"Parasuraman R.","year":"2000","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics\u2014Part A: Systems and Humans"},{"key":"e_1_2_9_59_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.triboint.2023.108337","article-title":"A New Empirical Model for Accurate Investigation of Rheological Behavior of MWCNT\/Oxide Nanoparticles\u2010Engine Oil Hybrid Nano\u2010lubricants","volume":"182","author":"Rahmati M.","year":"2023","journal-title":"Tribology International"},{"key":"e_1_2_9_60_1","unstructured":"Roszel M.2024. \u201cTowards Trustworthy Artificial Intelligence in Privacy\u2010Preserving Collaborative Machine Learning.\u201d PhD diss. Universit\u00e9 du Luxembourg."},{"issue":"2","key":"e_1_2_9_61_1","doi-asserted-by":"crossref","first-page":"188","DOI":"10.2967\/jnumed.121.263703","article-title":"Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem","volume":"64","author":"Saboury B.","year":"2023","journal-title":"Journal of Nuclear Medicine"},{"key":"e_1_2_9_62_1","unstructured":"Salunkhe A.2023.Uncertainty Aware Deep Neural Architectures for Applications in Turbulent Combustion. PhD diss. State University of New York at Buffalo."},{"issue":"3","key":"e_1_2_9_63_1","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1080\/00295450.2022.2067461","article-title":"Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review","volume":"209","author":"Sethu M.","year":"2023","journal-title":"Nuclear Technology"},{"key":"e_1_2_9_64_1","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/B978-0-08-036226-7.50076-4","volume-title":"Analysis, Design and Evaluation of Man\u2013Machine Systems 1988","author":"Sheridan T.","year":"1989"},{"key":"e_1_2_9_65_1","unstructured":"Sheridan T. B.2002.Humans and Automation: System Design and Research Issues: John Wiley & Sons. 280 pp."},{"key":"e_1_2_9_66_1","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.3389\/fpsyg.2019.01117","article-title":"Individual Differences in Attributes of Trust in Automation: Measurement and Application to System Design","volume":"10","author":"Sheridan T.","year":"2019","journal-title":"Frontiers in Psychology"},{"issue":"6","key":"e_1_2_9_67_1","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1080\/10447318.2020.1741118","article-title":"Human\u2010Centered Artificial Intelligence: Reliable, Safe & Trustworthy","volume":"36","author":"Shneiderman B.","year":"2020","journal-title":"International Journal of Human\u2013Computer Interaction"},{"issue":"11","key":"e_1_2_9_68_1","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MC.2021.3090018","article-title":"Zero Trust: The What, How, Why, and When","volume":"54","author":"Shore M.","year":"2021","journal-title":"Computer"},{"key":"e_1_2_9_69_1","doi-asserted-by":"crossref","unstructured":"Sifakis J.2019. \u201cCan We Trust Autonomous Systems? Boundaries and Risks.\u201d InInternational Symposium on Automated Technology for Verification and Analysis 65\u201378.Cham:Springer International Publishing.","DOI":"10.1007\/978-3-030-31784-3_4"},{"key":"e_1_2_9_70_1","unstructured":"SkraaningJr G. G. A.Jamieson andJ. C.Joe.2020. \u201cTowards a Deeper Understanding of Automation Transparency in the Operation of Nuclear Plants.\u201d INL\/EXT\u201020\u201059469\u2010Rev000.Idaho Falls ID:Idaho National Laboratory."},{"key":"e_1_2_9_71_1","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/978-3-030-18214-4_10","volume-title":"Industrial Control Systems Security and Resiliency: Practice and Theory","author":"Smidts C.","year":"2019"},{"key":"e_1_2_9_72_1","doi-asserted-by":"crossref","unstructured":"Spayne P. L.Lacey M.Cahillane andA.Saddington.2024. \u201cAutonomy Is the Answer What Was the Question Again?\u201dIn Conference Proceedings of INEC.","DOI":"10.24868\/11162"},{"key":"e_1_2_9_73_1","doi-asserted-by":"crossref","unstructured":"Sterz S. K.Baum S.Biewer et\u00a0al.2024. \u201cOn the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives.\u201d InProceedings of the 2024 ACM Conference on Fairness Accountability and Transparency 2495\u20132507.Association for Computing Machinery.","DOI":"10.1145\/3630106.3659051"},{"issue":"3","key":"e_1_2_9_74_1","doi-asserted-by":"crossref","DOI":"10.1177\/1687814016638807","article-title":"A Novel Concept and Assessment Method for Trustworthiness of Prognostics","volume":"8","author":"Sun B.","year":"2016","journal-title":"Advances in Mechanical Engineering"},{"key":"e_1_2_9_75_1","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s11023-010-9201-3","article-title":"Modelling Trust in Artificial Agents, a First Step Toward the Analysis of E\u2010Trust","volume":"20","author":"Taddeo M.","year":"2010","journal-title":"Minds and Machines"},{"issue":"2","key":"e_1_2_9_76_1","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1007\/s43681-024-00489-4","article-title":"Human Control of AI Systems: From Supervision to Teaming","volume":"5","author":"Tsamados A.","year":"2025","journal-title":"AI and Ethics"},{"issue":"2","key":"e_1_2_9_77_1","doi-asserted-by":"crossref","first-page":"898","DOI":"10.3390\/app14020898","article-title":"Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends","volume":"14","author":"Ucar A.","year":"2024","journal-title":"Applied Sciences"},{"key":"e_1_2_9_78_1","unstructured":"U.S. NRC (U.S. Nuclear Regulatory Commission).2023. \u201cArtificial Intelligence Strategic Plan: Fiscal Years 2023\u20132027.\u201d NUREG\u20102261.Washington DC:U.S. Nuclear Regulatory Commission."},{"key":"e_1_2_9_79_1","doi-asserted-by":"publisher","DOI":"10.1177\/00187208221077804"},{"key":"e_1_2_9_80_1","doi-asserted-by":"crossref","unstructured":"Volkova A. A. D.Patil S. A.Javadi andH.De Meer.2022. \u201cAccountability Challenges of AI in Smart Grid Services.\u201d InProceedings of the Thirteenth ACM International Conference on Future Energy Systems 597\u2013600.","DOI":"10.1145\/3538637.3539636"},{"key":"e_1_2_9_81_1","unstructured":"Walker C. M. V.Agarwal L.Lin et\u00a0al.2023. \u201cExplainable Artificial Intelligence Technology for Predictive Maintenance.\u201d INL\/RPT\u201023\u201074159\u2010Rev000.Idaho Falls ID:Idaho National Laboratory."},{"key":"e_1_2_9_82_1","unstructured":"Wang L.2022. \u201cDevelopment and Application of Data Coverage Assessment for NAMAC Trustworthiness.\u201d PhD diss. North Carolina State University."},{"key":"e_1_2_9_83_1","doi-asserted-by":"crossref","unstructured":"Wu X. L. E.Moloko P. M.Bokov G. K.Delipei J.Kaizer andK. N.Ivanov.2025.Uncertainty Quantification for Data\u2010Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need? Preprint arXiv March 16.","DOI":"10.1080\/00295639.2025.2552500"},{"key":"e_1_2_9_84_1","doi-asserted-by":"crossref","unstructured":"Yadav V. andJ. M.Biersdorf.2019. \u201cUtilizing FLEX Equipment for O&M Cost Reduction in Nuclear Power Plants.\u201d INL\/EXT\u201019\u201055445\u2010Rev000.Idaho Falls ID:Idaho National Laboratory.","DOI":"10.2172\/1559041"},{"key":"e_1_2_9_85_1","unstructured":"Zafer M. andM.Sani.2024. \u201cTrustworthy Artificial Intelligence: Fine\u2010Tuning Machine Learning Models with Ethics and Safety.\u201d."},{"issue":"11","key":"e_1_2_9_86_1","doi-asserted-by":"crossref","first-page":"10842","DOI":"10.1109\/TII.2023.3241587","article-title":"Trustworthy Fault Diagnosis With Uncertainty Estimation Through Evidential Convolutional Neural Networks","volume":"19","author":"Zhou H.","year":"2023","journal-title":"IEEE Transactions on Industrial Informatics"}],"container-title":["Risk Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/risa.70367","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/risa.70367","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/risa.70367","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T09:03:38Z","timestamp":1791363818000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/risa.70367"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,7]]},"references-count":85,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["10.1111\/risa.70367"],"URL":"https:\/\/doi.org\/10.1111\/risa.70367","archive":["Portico"],"relation":{},"ISSN":["0272-4332","1539-6924"],"issn-type":[{"value":"0272-4332","type":"print"},{"value":"1539-6924","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,7]]},"assertion":[{"value":"2025-12-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-08-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-10-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70367"}}