ML24319A056
| ML24319A056 | |
| Person / Time | |
|---|---|
| Issue date: | 11/20/2024 |
| From: | Taylor Lamb NRC/RES/DSA/AAB |
| To: | |
| References | |
| Download: ML24319A056 (1) | |
Text
1 Taylor Lamb Senior Reactor Systems Engineer (Data Scientist)
November 20, 2024 Artificial Intelligence Project Plan ACRS Subcommittee Meeting
2 Artificial Intelligence Strategic Plan Overview The AI Strategic Plan consists of five strategic goals:
- Goal 1: Ensure NRC Readiness for Regulatory Decisionmaking
- Goal 2: Establish an Organizational Framework to Review AI Applications
- Goal 3: Strengthen and Expand AI Partnerships
- Goal 4: Cultivate an AI-Proficient Workforce
- Goal 5: Pursue Use Cases to Build an AI Foundation Across the NRC Vision and Outcomes
- Continue to keep pace with technological innovations to ensure the safe and secure use of AI in NRC-regulated activities
- Establish an AI framework and cultivate a skilled workforce to review and evaluate the use of AI in NRC-regulated activities Available at ML23132A305
3 Artificial Intelligence Project Plan Overview
- The AI Project Plan describes how the agency will execute the five strategic goals from the AI Strategic Plan
- Provides estimated timelines for various task completions within each Strategic Goal
- Communicates NRC priorities to internal and external stakeholders Available at ML23236A279 Project Plan for the U.S.
Nuclear Regulatory Commission Artificial Intelligence Strategic Plan Fiscal Years 2023-2027, Revision 1 Available at ML24194A2116
4 GOAL 1: Ensure NRC Readiness for Regulatory Decisionmaking Outcome: Develop an AI framework to review the use of AI in NRC-regulated activities Collect industry AI plans Develop scheduling for resource allocation on AI applications Goal 1 Planning for AI Submittals IEC/
SC45A/
WGA12 Regulatory Gap Analysis Interdisciplinary team for the development of AI standards at nuclear facilities Applicable to entire nuclear fuel cycle Provides life cycle guidance on AI Assess regulations and guidance as it applies to gaps Identify usable standards and gaps
5 Goal 1: Ensure NRC Readiness for Regulatory Decision-Making
6 GOAL 2: Establish an Organizational Framework Outcome: An organization that facilitates effective coordination and collaboration across the NRC to ensure readiness for reviewing the use of AI in NRC-regulated activities Cross-agency strategic alignment and direction Centralized coordination of resources, priorities, and use case analyses NRC AI Community of Practice NRC AI Steering Committee Lead best practices for reviewing requests that use AI technologies Provide agencywide awareness on active and potential use cases Facilitate the sharing of knowledge and lessons learned AI
7 Goal 2: Establish an Organizational Framework to Review AI Applications
8 GOAL 3: Strengthening and Expanding AI Partnerships
- Enhancing and leveraging existing Memoranda of Understanding (MOU)
- Participating in federal AI working groups and maintaining awareness of other AI regulatory activities
- Engaging with international counterparts interested in AI for nuclear
- Maintaining continual engagement on state-of-the-art research GAINING VALUABLE INFORMATION TO BENCHMARK AI ACTIVITIES Enhancing and Leveraging MOUs Data Science and AI Addendum Operating Experience and Data Analytics International Collaboration Maintaining Federal Awareness
Goal 3: Strengthen and Expand AI Partnerships 9
10 GOAL 4: Cultivate an AI Proficient Workforce
- Focused on developing the critical skills for the AI workforce of tomorrow
- Staffing
- Targeted staffing to review AI applications
- Provide opportunities for all to learn
- Training
- Develop qualifications and up-skilling opportunities
- Workforce Planning
- Perform gap analysis, assess position descriptions and review hiring needs
11 Goal 4: Cultivate an AI Proficient Workforce
12 GOAL 5: Pursue Use Cases to Build AI Foundation Across the NRC Pilot Studies Learn, measure, and evaluate readiness to implement regulatory framework Public workshops have shown industry interest to pursue pilot studies and proofs of concepts AI Safety Insights Survey industrial safety evaluation methods and tools Utilize AI partnerships and engagement strategies AI Ecosystem Establish integrated development environments and provide training Acquire common data science tools Develop regulatory sandboxes for supporting use-cases AI R&D Research Continue supporting University grants and research into AI systems Building AI foundation through the NRCs Future Focused Research initiative
13 Goal 5: Pursue Use Cases to Build an AI Foundation Across the NRC
14 Accomplishments Completed one-third of the actions in the Project Plan.
Created an AI Steering Committee to promote cross-office coordination and direction to prepare the agency for the future use of AI in NRC-regulated activities.
Issued SECY-24-0035, Advancing the Use of Artificial Intelligence at the U.S. Nuclear Regulatory Commission on April 25, 2024 (ML24086A001).
Published an AI principles paper with Canada and the United Kingdom on September 5, 2024 (ML24241A252), outlining guiding principles for the safe and secure use of AI in nuclear applications.
Issued the final report from SwRI supporting Task 1.1, Regulatory Framework Gap Assessment for the Use of Artificial Intelligence in Nuclear Applications (ML24290A059).
15 Focus for FY25
- Publish a strategy for identifying and removing barriers to the responsible use of AI and achieving enterprisewide improvements in AI maturity.
- Broaden the scope of generative AI training to agency staff and contractors and the development of the rules of behavior for the use of generative AI tools.
- Continue to engage with the inspection community across all regions and inspection types in Future Focused Research outreach sessions.
- Further develop the regulatory framework to support the use of AI in NRC-regulated activities.
- Continue communication with industry stakeholders to ensure preparedness for future uses of AI in NRC-regulated activities.
AI DATA SCIENCE DATA SCIENCE & AI REGULATORY APPLICATIONS PUBLIC WORKSHOP Matthew Homiack Reactor Systems Engineer (Data Scientist)
GOAL 01 Establish consistent AI terminology and understand the availability and development of both NRC and industry datasets GOAL 02 Explore and leverage collaborative activities with other U.S. government agencies, national laboratories, and industry organizations GOAL 03 Identify and develop future use cases and collaborations where early intervention can provide high-yield resource investment GOAL 04 Engage with industry and public stakeholders on areas where NRC could enhance information sharing, data access, or AI development that could improve agency efficiency and effectiveness GOAL 05 Share industry and NRC lessons learned from ongoing data science and AI-related projects The NRC staff hosts public workshops to provide a forum to discuss the state of knowledge and research activities related to data science and AI applications in the nuclear industry.
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300+ participants from 6 countries June 2021 Introduction to AI WORKSHOP 1 August 2021 Current topics WORKSHOP 2 November 2021 Future focused initiatives WORKSHOP 3 September 2023 AI characteristics for regulatory consideration WORKSHOP 4 September 2024 AI regulatory framework applicability considerations WORKSHOP 5 The focus of the latest public workshop was on AI regulatory framework applicability considerations.
200+ participants from 6 countries 100+ participants from 6 countries 350+ participants from 12 countries 430+ participants from 12 countries 18
The 3 workshop panel sessions highlighted AI uses in NRC research projects, the industry, and the areas of nuclear materials, waste, and permitting.
AGENCY AI RESEARCH ACTIVITIES 01 NUCLEAR INDUSTRY AI 02 AI INNOVATIONS FOR NUCLEAR MATERIALS, RADIOACTIVE WASTE, AND PERMITTING 03 19
NRC AI RESEARCH ACTIVITIES Characterizing Nuclear Cybersecurity States with AI/ML Autonomous Control Algorithms to Simulate Boiling Water Reactor Cycle Depletion Using the Boiling Water Reactor Autonomous Learning Tasks Optimizer (BALTO)
Engagement with the Regions 20
NUCLEAR INDUSTRY AI Nuclear Energy Industry (NEI) View on AI Pressurized Water Reactor Owners Group (PWROG) Insights on AI Blue Wave AI Labs Applications X-energy Applications 21
MATERIALS, WASTE, & PERMITTING Florida International University (FIU)
Advanced Technologies for Characterization, Decommissioning, and Remediation of Contaminated Sites Microsoft AI for Nuclear Licensing Pacific Northwest National Laboratories (PNNL) PolicyAI Commonwealth Fusion AI/ML in Fusion Energy 22
There are 5 key takeaways from the 5th public workshop.
The workshop confirmed that the NRC remains well informed on the status of international AI regulation and domestic projects in the nuclear industry.
Industry representatives encouraged continued collaboration to pursue pilot studies and proofs of concept as a foundation for reviewing the use of AI in NRC-regulated activities.
AI regulatory sandboxes provide a unique opportunity for industry and the NRC to collaboratively explore the potential hurdles and benefits from using AI in safety-related nuclear applications.
The NRC plans to establish a working group to address the 8 potential AI regulatory gap categories and develop recommendations for the next steps based on feedback from the AI workshop and this subcommittee meeting.
Stakeholders are interested in leveraging NRCs public data set. Other Federal agencies are faced with similar challenges in implementing AI tools and features while ensuring privacy and responsible use.
01 02 03 04 05 23
CAN-UK-US Trilateral Collaboration:
Considerations for Developing Artificial Intelligence Systems in Nuclear Applications ACRS Briefing November 20, 2024 Matt Dennis Senior Data Scientist Office of Nuclear Regulatory Research U.S. Nuclear Regulatory Commission
OUTLINE
- Background on Individual Agency AI Activities
- Canada-UK-US (CANUKUS) AI Trilateral Background
- CANUKUS AI Paper Overview
- Path Forward 25
US NRC AI Activities 26 ML23132A305 AI Organizational Framework
- Internal NRC AI Steering Committee
- Internal NRC AI Community of Practice AI Research Priorities
- Regulatory framework applicability assessment
- Survey AI tools and methods for safety evaluation
- AI use cases for regulatory framework
- AI standards identification
- AI partnerships AI Regulatory Workshops*
- Scoping AI characteristics and regulatory considerations (2023.09.19)
- Regulatory gaps and considerations (2024.09.17)
NRC AI Webpage
CANUKUS Trilateral Background
- CNSC/UK ONR/US NRC established a trilateral relationship in March 2022 to share knowledge and discuss disruptive, innovative and emerging technology (DIET)
- Three regulators agreed to work together to develop and publish a trilateral AI considerations paper
- Working group organized in November 2022
- CNSC: Kevin Lee, Senior Regulatory Policy Officer
- UK ONR: Andy White, Superintending Nuclear Inspector, Electrical and Control & Instrumentation
- US NRC: Matt Dennis, Data Scientist 27
CANUKUS AI Paper Overview
Purpose:
Collaborate on a joint AI paper to establish a common set of overarching considerations for the use of AI technologies in nuclear activities
- Outcome: The AI considerations paper describes important topics that should be considered when deploying AI to ensure continued safe and secure operation of nuclear activities 28
Considerations for Developing AI Systems in Nuclear Applications
- 1. Introduction
- 2. Country-specific regulatory philosophies and perspectives
- 3. High level categories for AI use cases in nuclear applications
- 4. Use of existing safety and security systems engineering principles
- 5. Human and organisational factors
- 6. AI architecture in nuclear applications
- 7. AI lifecycle management
- 8. Documenting AI safety and security
- 9. Conclusion
- 10. Further reading (links to useful documents, etc.)
- 11. Annex (relevant standards and guidance across regulatory areas) 29 ML24241A252
Trilateral AI Publication
- Trilateral social media announcement occurred on September 5, 2024 (ML24241A252)
- Trilateral AI principles paper published on respective agency websites:
- CNSC
- ONR
- NRC 30
Proposed Future CANUKUS AI Activities
- Trilateral working group determined the preferred future collaboration would focus on AI assurance and a literature review of near-term AI use cases across the three countries.
- The group plans to collaborate on another discussion paper with a target publication date of Fall 2025.
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Conclusion
- Observations from fruitful and productive trilateral engagement
- Recognition that AI is similar to other previous innovations
- We have faced innovative technologies in the past and integrated those into suitable engineered systems to manage risks
- Recognition that we are grappling with areas of uncertainty
- Maintaining adequate safety and security is fundamental
- Global cooperation among entities is paramount to ensure efficient, safe, and secure adoption of this emerging technology 32
BACKUP SLIDES 33
CNSC AI Activities Disruptive, Innovative, Emerging Technologies (DIET)
- Canadian Nuclear Safety Commission (CNSC) DIET Working Group and its Innovation Hub enables greater sharing of innovation, internally and externally
- Topics include AI, fusion, digital twins, drones, robotics, additive manufacturing Collaboration
- Seminars from external presenters, internal communication, and DIET sub-groups
- Engagement with IAEA, NEA, Canadian Standards Association, labs and Canadian Nuclear Society (CNS DIET2024 Conference, Oct. 28-30, 2024)
Research on AI regulation
- R760.1 A Study for the CNSC on AI Applications and Implications for the Nuclear Industry (April 2023)
Future activities
- Gathering intelligence, assessing DIET readiness, build innovative culture, ensure knowledge management, and continue external engagement (IAEA, NEA, regulatory counterparts, etc.)
- Developing an AI roadmap for licensees as they consider deployment of AI 34
UK ONR AI Activities
- UK government - National AI Strategy 2022
- Public sector leadership in safe and ethical deployment of AI
- Incubator for Artificial Intelligence (i.AI) - Government team of technical specialists
- UK Office for Nuclear Regulation (ONR) research on AI regulation
- ONR-RRR-121 (June 2021)
- New research (commencing October 2024)
- Collaboration
- Other UK regulators, licensees, internal specialisms, academia
- Chair IAEA AI Safety Working Group, Alan Turing AI Standards
- Innovation Hub and Sandboxing
- Test realistic applications against ONR regulation, test ability of AI to be used in nuclear safety applications, and pilot use of regulatory sandbox
- Outcomes of nuclear AI regulatory sandbox pilot (November 2023)
- ONR future activities
- IAEA participation, sandboxing, guidance, and growing skilled inspectors
- Modernisation of ONR - data rich/data driven organisation, create an ecosystem of safe experimentation, think big/start small/learn fast, Thought Group 35
Regulatory Framework Applicability Assessment of Artificial Intelligence in Nuclear Applications (AIRGA: AI Regulatory Gap Analysis)
O. Pensado, P. LaPlante, M. Hartnett, K. Holladay Advisory Committee on Reactor Safeguards (ACRS)
Joint Human Factors Reliability & PRA, and Digital I&C Subcommittee Meeting Information Briefing:
Implementing the NRCs Artificial Intelligence (AI) Strategic Plan Fiscal Years 2023-2027 November 20, 2024 36
Acknowledgments This project benefitted from discussions with M. Dennis, L. Betancourt, A.
Hathaway, N. Tehrani, A. Valiaveedu, and S. Haq of the NRC Office of Nuclear Regulatory Research This project was sponsored by the NRC Office of Nuclear Regulatory Research, Division of Systems Analysis The work is an independent product of SwRI and it does not necessarily reflect the views of the NRC Report: O. Pensado, P. LaPlante, M. Hartnett, K. Holladay. Regulatory Framework Gap Assessment for the Use of Artificial Intelligence in Nuclear Applications. San Antonio, TX: Southwest Research Institute, October 2024.
ADAMS Accession Number ML24290A059 37
Project Objective Support NRCs readiness to evaluate uses of AI technologies in NRC-regulated activities Main task: conduct an AI regulatory gap analysis (AIRGA)
- Identify types of AI technologies to be potentially used in the nuclear industry
- Identify potential AI uses in NRC-regulated activities
- Examine whether the existing regulatory framework is appropriate for AI technologies 38
Project Scope Regulatory framework considered
- NRC's regulations, Title 10, Chapter I, of the Code of Federal Regulations, Parts 1 - 171
- 517 regulatory guides (RGs)
Out of scope guidance documents:
- NUREGs, Interim Staff Guidance (ISG)
- Standard Review Plans (SRPs)
- Inspection Procedures
- Standards cited in regulations 39
Main Project Tasks 40 Task 1: Identify examples of AI uses Task 2: Analyze RGs to identify potential gaps Task 3: Analyze regulations applicable to the subset of RGs with potential gaps Task 4: Examine AI standards by professional communities Considered examples of AI uses based on known applications and R&D activities Examples discussed in past Data Science and AI Regulatory Applications Workshops Examples in NUREG/CR-7294
Analysis Approach for Regulatory Guides 41 Q0: Could AI technologies be used within the scope of the RG?
NO RG is excluded from further analysis.
No gap in the RG YES Q1: Is the regulatory guide flexible to allow use of AI?
Q2: Does the regulatory guide provide adequate guidance to evaluate the use of AI?
Answer to Q1 or Q2 is NO The RG has a potential gap: detailed notes are recorded in report appendices Answer to both Q1 and Q2 is YES No gap in the RG YES
Regulatory Guides: 372 active RGs 42 Power Reactors Division Division 9, Antitrust and Financial Review, has no active RGs
Regulatory Guides with Potential Gaps 71 RGs with potential gaps after applying the process from previous slide
- Questions Q0, Q1, and Q2 43 Division Power Reactors The RGs of divisions missing in the chart do not have potential gaps
Categories of Potential Gaps Gap 1: Implied Manual Actions Gap 2: Special Computations Gap 3: Preoperational and Initial Testing Programs May Omit AI Gap 4: Habitability Conditions under Autonomous Operations Gap 5: Periodic Testing, Monitoring, and Reporting Gap 6: Software for Safety Critical Applications Gap 7: Radiation Safety Support Gap 8: Miscellaneous: Training and Human Factors Engineering 44
Gap 1: Guides call for human manual actions; AI systems may offer different alternatives to execute those actions 45 Table 3-1. Regulatory guides related to Gap 1: Implied Manual Actions 1.7 Control of Combustible Gas Concentrations in Containment 1.114 Guidance to Operators at the Controls and to Senior Operators in the Control Room of a Nuclear Power Unit 1.141 Containment Isolation Provisions for Fluid Systems 1.147 Inservice Inspection Code Case Acceptability, ASME Section XI, Division 1 1.149 Nuclear Power Plant Simulation Facilities for Use in Operator Training, License Examinations, and Applicant Experience Requirements 1.189 Fire Protection for Nuclear Power Plants 1.205 Risk-Informed, Performance-Based Fire Protection for Existing Light-Water Nuclear Power Plants 5.7 Entry/Exit Control for Protected Areas, Vital Areas, and Material Access Areas 5.44 Perimeter Intrusion Alarm Systems
Gap 2: AI techniques may be used in special computations; guidance may be needed on documentation and verification 46 Table 3-2. Regulatory guides related to Gap 2: Special Computations 1.59 Design Basis Floods for Nuclear Power Plants 1.60 Design Response Spectra for Seismic Design of Nuclear Power Plants 1.76 Design-Basis Tornado and Tornado Missiles for Nuclear Power Plants 1.157 Best-Estimate Calculations of Emergency Core Cooling System Performance 1.198 Procedures and Criteria for Assessing Seismic Soil Liquefaction at Nuclear Power Plant Sites 1.200 Acceptability of Probabilistic Risk Assessment Results for Risk-Informed Activities 1.203 Transient and Accident Analysis Methods 1.245 Preparing Probabilistic Fracture Mechanics (PFM) Submittals 1.247 TRIAL - Acceptability of Probabilistic Risk Assessment Results for Non-Light Water Reactor Risk-Informed Activities 3.27 Nondestructive Examination of Welds in the Liners of Concrete Barriers in Fuel Reprocessing Plants 3.76 Implementation of Aging Management Requirements for Spent Fuel Storage Renewals 5.11 Nondestructive Assay of Special Nuclear Material Contained in Scrap and Waste 5.21 Nondestructive Uranium-235 Enrichment Assay by Gamma Ray Spectrometry 5.23 In Situ Assay of Plutonium Residual Holdup 5.37 In Situ Assay of Enriched Uranium Residual Holdup 5.38 Nondestructive Assay of High-Enrichment Uranium Fuel Plates by Gamma Ray Spectrometry 10.4 Guide for the Preparation of Applications for Licenses to Process Source Material
Gap 3: Critical AI systems may need to be explicitly included in preoperational and initial testing programs 47 Table 3-3.
Regulatory guides related to Gap 3: Preoperational and Initial Testing Programs May Omit AI 1.68 Initial Test Programs for Water-Cooled Nuclear Power Plants 1.68.2 Initial Startup Test Program to Demonstrate Remote Shutdown Capability for Water-Cooled Nuclear Power Plants 1.79 Preoperational Testing of Emergency Core Cooling Systems for Pressurized Water Reactors 1.79.1 Initial Test Program of Emergency Core Cooling Systems for New Boiling-Water Reactors
Gap 4: Habitability conditions under autonomous operations; variable role of operators 48 Table 3-4. Regulatory guides related to Gap 4: Habitability Conditions Under Autonomous Operations 1.78 Evaluating the Habitability of a Nuclear Power Plant Control Room During a Postulated Hazardous Chemical Release 1.189 Fire Protection for Nuclear Power Plants 1.196 Control Room Habitability at Light-Water Nuclear Power Reactors
Gap 5: Periodic testing, monitoring, surveillance, and reporting; AI systems may offer different strategies for those activities RGs related to testing and monitoring deemed with potential gaps:
RG 5.71 Cyber Security Programs for Nuclear Power Plants: AI may be used as monitoring tool to detect anomalies as indicators of cyber attacks
- Cybersecurity of AI and AI for cybersecurity 49 1.7 1.205 5.11 5.71 8.22 8.38 1.9 1.246 5.21 8.8 8.25 10.2 1.21 3.27 5.23 8.1 8.26 10.3 1.9 3.76 5.27 8.11 8.31 10.4 1.118 4.1 5.37 8.15 8.32 1.129 4.14 5.38 8.18 8.34 1.141 4.16 5.41 8.19 8.36 1.147 5.7 5.44 8.2 8.37
Gap 6: Software guides may need to be updated to address special features and risks of AI systems 50 Table 3-6. Regulatory guides related to Gap 6: Software for Critical Applications 1.168 Verification, Validation, Reviews, and Audits for Digital Computer Software Used in Safety Systems of Nuclear Power Plants 1.169 Configuration Management Plans for Digital Computer Software Used in Safety Systems of Nuclear Power Plants 1.171 Software Unit Testing for Digital Computer Software Used in Safety Systems of Nuclear Power Plants 1.172 Software Requirement Specifications for Digital Computer Software and Complex Electronics Used in Safety Systems of Nuclear Power Plants 1.173 Developing Software Life Cycle Processes for Digital Computer Software Used in Safety Systems of Nuclear Power Plants 1.231 Acceptance of Commercial-Grade Design and Analysis Computer Programs Used in Safety-Related Applications for Nuclear Power Plants 5.71 Cyber Security Programs for Nuclear Power Reactors
Gap 7: Radiation safety support; AI may be used for tasks and functions of radiation safety professionals 51 Table 3-7.
Regulatory guides related to Gap 7: Radiation Safety Support 8.8 Information Relevant to Ensuring that Occupational Radiation Exposures at Nuclear Power Stations Will Be as Low as Is Reasonably Achievable 8.10 Operating Philosophy for Maintaining Occupational Radiation Exposures as Low as Is Reasonably Achievable 8.11 Applications of Bioassay for Uranium 8.15 Acceptable Programs for Respiratory Protection 8.18 Information Relevant to Ensuring that Occupational Radiation Exposures at Medical Institutions Will Be as Low as Reasonably Achievable 8.20 Applications of Bioassay for Radioiodine 8.22 Bioassay at Uranium Mills 8.25 Air Sampling in the Workplace 8.26 Applications of Bioassay for Fission and Activation Products 8.31 Information Relevant to Ensuring that Occupational Radiation Exposures at Uranium Recovery Facilities Will Be as Low as Is Reasonably Achievable 8.32 Criteria for Establishing a Tritium Bioassay Program 8.34 Monitoring Criteria and Methods to Calculate Occupational Radiation Doses 8.35 Planned Special Exposures 8.36 Radiation Dose to the Embryo/Fetus 8.38 Control of Access to High and Very High Radiation Areas of Nuclear Plants 10.4 Guide for the Preparation of Applications for Licenses to Process Source Material
Gap 8: Miscellaneous: Training and Human Factors Engineering 52 Table 3-8. Regulatory guides related to Gap 8: Miscellaneous: Training, Human Factors Engineering, and AI Introduced as Changes 1.149 Nuclear Power Plant Simulation Facilities for Use in Operator Training, License Examinations, and Applicant Experience Requirements 1.206 Applications for Nuclear Power Plants
The analysis identified only few gaps in applicable regulations Regulations applicable to the RGs deemed with potential gaps were examined
- Not all the regulations were examined in detail In general, the applicable regulations (10 CFR 1 to 171) were high level and adequate for AI technologies, with a few exceptions The exceptions are related to regulatory statements calling for actions by humans when those actions could also be executed by AI systems
- Surveillance using computer vision
- Searches of people and vehicles
- Escorting people in facilities Some regulations should be examined in light of questions related to autonomous operation and control room habitability
- Role of operators, protection of equipment in the control room, constraints on autonomous operation 53
Recommendations Develop few general guides addressing cross-cutting issues associated with potential gaps, such as software development with AI systems and use of AI in special computations
- More efficient to develop few guides rather than inserting explicit AI considerations in many RGs Existing AI standards by professional societies do not readily address the identified potential gaps 54
Potential cross-cutting guides
- 1. Data quality for machine learning (ML) purposes, including aspects of accuracy, context, data management, data variety, and data quantity.
- 2. Type of systematic testing and documentation needed to enhance confidence in outputs by AI systems, with special attention on responses to rare and extreme inputs.
- 3. Systematic fail-safe design, including active detection of inputs different than in the ML database, active detection of anomalous responses by AI systems, and mitigation of errors by AI systems.
- 4. Types of testing and documentation needed to enhance confidence in computations and predictions that use AI techniques.
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Backup Slides 56
Abbreviations and acronyms 57 AI Artificial intelligence AIRGA AI regulatory gap analysis ASME American Society of Mechanical Engineers CFR Code of Federal Regulations FAA U.S. Federal Aviation Administration FDA U.S. Food and Drug Administration HFE Human factors engineering IEEE Institute of Electical and Electronics Engineers ISG Interim Staff Guidance LLM Large language model ML machine learning NRC U.S. Nuclear Regulatory Commission PFM Probabilistic fracture mechanics R&D Research and development RG Regulatory Guide SRP Standard Review Plan SwRI Southwest Research Institute
Terminology AI includes a range of technologies Deep neural networks and machine learning methods are notable because of their broad range of applicability 58 Artificial Intelligence Machine Learning Deep Learning Computer Vision Natural Language Processing Generative AI &
Large Language Models