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A Framework for the Assurance of AI-Enabled Systems

Ariel S. Kapusta, David Jin, Peter M. Teague, Robert A. Houston, Jonathan B. Elliott, Grace Y. Park, Shelby S. Holdren

TL;DR

This work tackles the challenge of assuring AI-enabled DoD systems amid learning, data, and security uncertainties. It proposes a claims-based AI assurance framework that centers on explicit assurance claims and an assurance case to unify safety, security, ethics, and performance across the system lifecycle. The framework defines key terms, aligns with existing standards, and prescribes a three-phase process—Prepare for Assurance, Establish Assurance, Maintain Assurance—with iterative artifacts (assurance plan, assurance cases) to support fielding decisions. By enabling cross-domain coordination and providing a pathway for rapid yet rigorous evaluation, the approach aims to accelerate trustworthy AI deployment in defense contexts while maintaining accountability and risk control.

Abstract

The United States Department of Defense (DOD) looks to accelerate the development and deployment of AI capabilities across a wide spectrum of defense applications to maintain strategic advantages. However, many common features of AI algorithms that make them powerful, such as capacity for learning, large-scale data ingestion, and problem-solving, raise new technical, security, and ethical challenges. These challenges may hinder adoption due to uncertainty in development, testing, assurance, processes, and requirements. Trustworthiness through assurance is essential to achieve the expected value from AI. This paper proposes a claims-based framework for risk management and assurance of AI systems that addresses the competing needs for faster deployment, successful adoption, and rigorous evaluation. This framework supports programs across all acquisition pathways provide grounds for sufficient confidence that an AI-enabled system (AIES) meets its intended mission goals without introducing unacceptable risks throughout its lifecycle. The paper's contributions are a framework process for AI assurance, a set of relevant definitions to enable constructive conversations on the topic of AI assurance, and a discussion of important considerations in AI assurance. The framework aims to provide the DOD a robust yet efficient mechanism for swiftly fielding effective AI capabilities without overlooking critical risks or undermining stakeholder trust.

A Framework for the Assurance of AI-Enabled Systems

TL;DR

This work tackles the challenge of assuring AI-enabled DoD systems amid learning, data, and security uncertainties. It proposes a claims-based AI assurance framework that centers on explicit assurance claims and an assurance case to unify safety, security, ethics, and performance across the system lifecycle. The framework defines key terms, aligns with existing standards, and prescribes a three-phase process—Prepare for Assurance, Establish Assurance, Maintain Assurance—with iterative artifacts (assurance plan, assurance cases) to support fielding decisions. By enabling cross-domain coordination and providing a pathway for rapid yet rigorous evaluation, the approach aims to accelerate trustworthy AI deployment in defense contexts while maintaining accountability and risk control.

Abstract

The United States Department of Defense (DOD) looks to accelerate the development and deployment of AI capabilities across a wide spectrum of defense applications to maintain strategic advantages. However, many common features of AI algorithms that make them powerful, such as capacity for learning, large-scale data ingestion, and problem-solving, raise new technical, security, and ethical challenges. These challenges may hinder adoption due to uncertainty in development, testing, assurance, processes, and requirements. Trustworthiness through assurance is essential to achieve the expected value from AI. This paper proposes a claims-based framework for risk management and assurance of AI systems that addresses the competing needs for faster deployment, successful adoption, and rigorous evaluation. This framework supports programs across all acquisition pathways provide grounds for sufficient confidence that an AI-enabled system (AIES) meets its intended mission goals without introducing unacceptable risks throughout its lifecycle. The paper's contributions are a framework process for AI assurance, a set of relevant definitions to enable constructive conversations on the topic of AI assurance, and a discussion of important considerations in AI assurance. The framework aims to provide the DOD a robust yet efficient mechanism for swiftly fielding effective AI capabilities without overlooking critical risks or undermining stakeholder trust.

Paper Structure

This paper contains 18 sections, 2 figures.

Table of Contents

  1. INTRODUCTION
  2. Definitions
  3. Context
  4. Fundamental Concepts
  5. A Risk-informed Approach
  6. AI-Specific Assurance Challenges
  7. Data Bias and Quality
  8. Model Complexity and Unpredictability
  9. Lack of Explainability
  10. Dynamic Learning and Adaptation
  11. Adversarial Threats and Robustness
  12. Integration and Operational Constraints
  13. A Need for Consistency and a Unified Framework
  14. The AI Assurance Process
  15. Prepare for Assurance
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Phases of the AI assurance process
  • Figure 2: The phases and steps of the AI Assurance Process.