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Session 4 - Development of Standards for Artificial Intelligence Systems
	ML23251A060
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Issue date:	09/13/2023
From:	Robert Roche-Rivera; NRC/RES/DE
To:	;
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Development of Standards for Artificial Intelligence Systems Opening Remarks by Victor Hall, Deputy Division Director, RES/DSA Session Chair: Luis Betancourt, Branch Chief, RES/DSA/AAB Panelists/Speakers:

Elham Tabassi (NIST)

Jonnie Bradley (DOE)

Ahmad Al Rashdan (INL)

Trey Hathaway (NRC)

Thiago Seuaciuc-Osorio (EPRI)

AI RISK MANAGEMENT PLAYBOOK DOE/NRC AI/ML Meeting September 13, 2023 Ms. Jonnie Bradley, Sr. Program Manager/

Responsible AI Official, Artificial Intelligence and Technology Office 2

AGENDA

Overview

AI RMP Features

Pathways

Future Developments

Walkthrough 3

1. From AI inventory data tracking to AI Portfolio and Program Optimization & Impacts DELIVERING

Strategic portfolio analysis and alignment of AI/ML ON AITO'S investments, ensuring alignment with national security priorities, facilitates department-wide responsible and trustworthy AI across program offices (ex. Federated MISSION AND Learning)

VISION 2. From management of AI projects to orchestration of AI Strategy and Partnership Development

Builds robust AI partnerships and customer excellence across internal, external, and international boundaries and addresses strategic communications for the Department.

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Visionaries and practitioners of Modern-Day Governance for AI and Autonomous Innovations DELIVERING ON AITO'S

Responsible AI Official

Convene MISSION AND

Coordinate VISION

Facilitate Orchestrate

Advocate

Integrate

Risk Management 5

OVERVIEW: AI Integrated Ecosystem 1 2 3 4 5 AIX System AI RMP AI Pathways AI Strategy AI Advancement Council Enables better Guidance for leaders of A space for the DOE Certificate upskilling A space to test and coordination across DOE new AI/ML projects and AI community to post program with two validate algorithms programs and alignment a reference of risks and questions, share tracks: Management and models using the with strategic priorities in mitigation techniques for knowledge, and and Technical latest techniques AI technical users connect with SMEs DOE AI Community Hub Active or underway In planning stages 6

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AI RISK MANAGEMENT PLAYBOOK 7

The AI Risk Management Playbook (AI RMP) is a comprehensive reference guide for AI risk AI RMP VALUE identification with recommended mitigations to support responsible and trustworthy (R&T) AI use and development.

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TIMELINE: AI RISK MANAGEMENT PLAYBOOK 2021 - DOE review, edit, comment, &

April 2020 - AI Asset September 2022 -

concurrence, and intergovernmental and Risk Management AI RMP Public Q&A industry review/comment Framework

1. April 2020 - AI Asset Risk Management Framework August 2020 - Internal AI Risk August 2022 - Public Release Management Playbook (AI Ethics, - energy.gov/ai/airmp Data Compliance, Asset Management, Secure Model Usage) 9

AI RMP: FEATURES Essential Guidance Intelligent Search Trustworthy AI Dynamic system featuring 141 Ability to filter according to Integration with EO 13960:

unique risks and mitigation lifecycle stage, assets, as well Promoting the Use of techniques with ability to easily as mapping to project roles and Trustworthy AI, including ability and continuously expand direct keyword searching to filter by principle 10

USING AI RMP AI Use Case Example Risk Using data for AI systems to determine peak charging hours for EV owners Using data for AI systems to determine Unintended biases in which neighborhoods to datasets used to build AI install EV supercharging systems; disadvantaged stations communities are not included in the datasets Using data for AI systems to predict power outages and which communities will be serviced after an outage 11

AI RMP: PATHWAYS MANAGEMENT TECHNICAL

Additional filters designed for project

Ability to directly search via keyword managers and AI novices according to issues encountered and mitigation techniques

Provides understanding of risks according to development stage

Quickest pathway for technical experts to find relevant risk or mitigation

Connects risks with Trustworthy AI recommendation principles 12

AI RMP: FUTURE DEVELOPMENT Additional Content Enhanced Searching Increased Engagement Continuously add new risks and mitigation techniques; Eventual New search capabilities Additional features to drive integration with NISTs AI Risk co-developed with playbook community engagement including Framework users including the ability to contributor badges and enhanced search by project role editing capabilities 13

Q&A/

Discussion 14

ADDITIONAL RESOURCES

Executive Order 13960 - Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government

NIST AI Technical Standards

Blueprint for an AI Bill of Rights

National Artificial Intelligence Initiative

Algorithmic Discrimination Protections

AI Now Institutes Annual Reports

Partnership on AI

Alan Turing Institutes Fairness, Transparency, Privacy group

Harvard's Embedded Ethics Modules

DAIR Institute

Fairness Tutorial 15

THANK YOU Artificial Intelligence and Technology Office U.S. Department of Energy September 13, 2023 Website: energy.gov/ai AIRMP: energy.gov/ai/airmp Inquiries, comments may be sent to:

doeaimailbox@hq.doe.gov 16

DOE DRAFT RESPONSIBLE AND TRUSTWORTHY AI PRINCIPLES DOE AI Principles Draft Equitable Traceable Reliable Governable Accountable 17

NRC Standards Forum Ahmad Al Rashdan, INL Considerations for the Development of a Standards-compatible AI Acknowledgment: Ted Quinn and Roman Shaffer September 13, 2023 18

Ahmad Al Rashdan, Ph.D.

Senior Research and Development Scientist Idaho National Laboratory, USA

- A senior scientist with 17 years of industrial and research experience in automation, artificial intelligence/machine learning (AI/ML), and instrumentation and controls (I&C).

- Lead of several efforts under the U.S. Department of Energy (DOE) focusing on plant modernization using AI methods and advanced analytics.

- Author of more than 80 reports and journal papers, 9 patent applications and awards, and developer/co-developer of 8 Copyrighted software packages.

- Recipient of several funding and recognition awards including a recent 2022 R&D 100 and the prestigious INLs Director Excellent Engineering Achievement Award.

- Holder of several professional leadership positions

- Standards involvement:

- Member of the IEC SC45A WG12 to create a standard for AI Application for Nuclear Installations.

- Member of the ANS Large Light Water Reactor Consensus Standards Committee.

E-mail = ahmad.alrashdan@inl.gov - Vice Chair to the ANS Simulators, Instrumentation, Control Systems,

Phone = +1 979 422 4264 Software & Testing standards Subcommittee.

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Computer Vision Machine Learning (CVML) in Fire Watch

Automatically identifying a fire in a video stream to eliminate/reduce the need for fire watches.

Sixteen different models were evaluated.

An ensemble of models is developed to improve accuracy

(>99% achieved)

Smoke is being integrated.

Temporal effects are being considered to eliminate false positives (mist, fog, steam, etc.)

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CVML in Gauge Reading

Automating manual logging of analog gauges (i.e., a method to recognize gauges in oblique angles and read their values)

Automated gauge reading impacts a wide spectrum of activities in a plant including operator rounds, gauges calibration, and peer verification, and improves data fidelity for online monitoring.

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The Need for Compatible AI and What Does that Mean?

Digital Instrumentation And Controls (DI&C) regulatory To evaluate how example AI requirements would need to technologies be satisfied for any and align with the every AI application that safety impacts safety related and framework, and risk-significant applications discusses how they could be

Regulations frequently cite analyzed, standards. modeled, tested, and validated in Can AI be customized to a manner similar meet those standards to typical DI&C requirements? technologies.

Do we need new AI-focused standards?

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Example of Considerations CVML models often utilize open-source datasets and feature extraction engines or models.

It is not always possible to determine the level of overlap among open-source datasets. Open-source models could use similar fundamental concepts. This impacts the independence of the developed CVML models:

Causes the CVML system to be susceptible to common cause failure

Overestimates the software verification results

Introduces a cybersecurity concern Methods to create independent Methods to quantify independence 23 datasets may be needed (e.g., GANs) may be needed

CVML Compatibility with the Current Requirements Defense in Depth Configuration Control Deterministic Nature Independence Cyber Security Maintainability Traceability Design Control Repeatability Explinability Justification Trustworthiness Characteristic/Consideration CGD Reliability FMEA Simplicity CCF V&V QA Open-source data and model Frequent updates to source Massive amounts of data Periodic training Probabilistic and stochastic Various performance metrics Incomprehensible to reviewers Inherited bias Non-systematic approach Robustness to new conditions Special skillset 24

Conclusions 25

lwrs.inl.gov 26

IEC/SC45A/WGA12 Artificial Intelligence for Nuclear Facilities Trey Hathaway, Ph.D.

Reactor Systems Engineer U.S. Nuclear Regulatory Commission RES/DSA/AAB 2023 NRC Standards Forum September 13, 2023 27

AI Standardization Efforts

ISO/IEC JTC 1/SC 42 - Artificial Intelligence (AI)

- Created in 2017

- Secretariat: ANSI

- Scope: Standardization encompassing Artificial Intelligence

Provide guidance to JTC 1, IEC, and ISO committees developing artificial intelligence applications

IEC - International Electrotechnical Commission

- IEC/SC 45A - Instrumentation, control and electric power systems of nuclear facilities

Scope:

Prepare standards applicable to electronic and electrical functions and associated systems and equipment used in nuclear energy generation facilities to improve efficiency, safety and security of nuclear energy generation.

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WGA 12

WGA 12 - AI for nuclear facilities

- First meeting held on August 22, 2023

Composed of a multi-disciplinary international team

- Currently 35 experts

NRC has four staff involved

- Ismael Garcia, Kim Lawson-Jenkins, Tanvir Siddiky - NSIR

- Trey Hathaway - RES 29

WGA 12

Tasks

- Develop and maintain standards and reports for AI applications in nuclear facilities

- Provide guidance to stakeholders developing, deploying, and overseeing AI applications for nuclear facilities

- Cover fundamental characteristics of AI of nuclear facility applications

- Applicable to the entire nuclear facility life cycle 30

WGA 12

Provide overview of AI from a nuclear perspective

- Discuss concepts, applications, and challenges

Explore definition of AI for nuclear facility applications

Discuss AI applications with focus on IEC/SC45A cross-cutting areas

- Instrumentation and control

- Annex to discuss applications of AI to other aspects of nuclear facility uses beyond IEC/SC45A scope 31

WGA 12 Cross-cutting Topics with other Working Groups in SC45A Working Working Group Titles Group WGA2 Sensors and measurement techniques WGA3 Instrumentation and control systems: architecture and system specific aspects WGA5 Special process measurements and radiation monitoring WGA7 Functional and safety fundamentals of instrumentations, control and electrical power systems WGA8 Control rooms WGA9 System performance and robustness toward external stress stems WGA10 Ageing management of instrumentation, control and electric power systems in NPP WGA11 Electrical power systems: architecture and system specific aspects 32

WGA 12

Three levels of documents to be produced

- Level 2 document on cross-cutting areas

General Requirements

Horizontal Standard

- e.g., Trustworthiness & Risk Management, Data Processing & Management, Testing and V&V, Performance Assessment

- Level 3 document on AI specific applications in nuclear facilities

Vertical Standard

Standards for specific applications

- e.g., Virtual sensors, anomaly detection

- Technical reports that support Level 2 and Level 3 standards

Documents created through IAEA Consultancy meetings will be leveraged in the IEC work 33

Status and Next Steps

Participants will notify working group convener of areas where they would like to offer input - by September 9, 2023

Meetings with other working groups at IEC General Meeting in October 2023 to discuss cross-cutting areas

Develop the Level 2 Standards first, then begin to explore application specific standards 34

AI-Assisted Ultrasonic Inspections in the Nuclear Power Industry Thiago Seuaciuc-Osorio Principal Technical Leader 2023 NRC Standards Forum September 13, 2023 35 www.epri.com © 2023 Electric Power Research Institute, Inc. All rights reserved.

AI-Assisted Analysis of UT Inspections UT inspections are an important part of the scope of an NDE program Some inspections are challenging or have large volumes of data Machine learning tools can potentially assist in the analysis of the data

- Increase reliability

- Decrease analysis time Assist means AI flags regions for review: final decisions still rest with the qualified inspector.

Goal: Develop auto-analysis tools to assist in UT inspections 36

How Would AI Assist in UT Inspections?

Current Inspection AI Assisted Inspection

- Examiners distribute their energy across a - Examiners focus their energy on the regions high volume of (mostly benign) data that require more careful review, while AI takes care of the more monotonous portion 37

Development Status Reactor Vessel Upper Head Penetrations Complete

Two successful field trials (2022-23)

Positive qualification assessment Dissimilar Metal Welds Work in

Field trial now (Sep 2023)

Progress

Qualification assessment underway Core Barrel + Core Shroud Coming Soon

Starting in 2024 38

Current Qualification Framework Blind Tests Detection False Calls Dual Qualification Procedure Personnel Separate Stages Data Collection Data Analysis Develop AI-Assisted Analysis that fits this overall framework 39

What initial qualification may look like Procedure would be updated to include a defined process for the AI evaluation AI algorithms for qualification would be developed and provided to the qualification body: model is frozen at this stage Qualification Body Only flagged regions are Determination presented to candidate, who reviews and makes calls Are all flawed Yes regions flagged?

AI model is applied No Pre-recorded AI flags areas data for review Fail Pass/Fail Based on typical detection

+ false call criteria 40

Elements that do NOT Change Same blind tests with same criteria Detection False Calls Procedure Personnel Data Collection Data Analysis Same data enables:

Ease of implementation

Parallel deployment Qualification framework is maintained 41

Elements that DO Change Procedure adds AI Detection False Calls Personnel analysis stage: only reviews flagged

Freezes model Procedure Personnel regions:

100%

Same test detection

More required Data Collection Data Analysis restrictive

Initial screening potentially on tool

Further analysis remain the same Modifications are typical of any new procedure 42

Going Forward Ready for Use Parallel & gradual deployment:

Utilities can use for oversight & planning AI-Assisted as one of the two

Vendors can seek qualification required independent reviews

Gain operational experience

Different failure modes Supporting Research (2024 onwards)

Assessment of reliability of AI-assisted analysis o UT, VT

Qualification protocol o Re-qualifications 43

TogetherShaping the Future of Energy 44

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