Trustworthy Autonomy: A Roadmap to Assurance Part 1: System Effectiveness

Abstract

The Department of Defense (DoD) has invested significant effort over the past decade considering the role of artificial intelligence and autonomy in national security (e.g., Defense Science Board, 2012, 2016; Deputy Secretary of Defense, 2012; Endsley, 2015; Executive Order No. 13859, 2019; US Department of Defense, 2011, 2019; Zacharias, 2019a). However, these efforts were broadly scoped and only partially touched on how the DoD will certify the safety and performance of these systems. More recent work has done this big-picture thinking for the test and evaluation (T and E) community (e.g., Ahner and Parson, 2016;Haugh, Sparrow, and Tate, 2018; Porter et al., 2018; Sparrow, Tate, Biddle, Kaminski, and Madhavan, 2018; Zacharias, 2019b). In parallel, individual programs have been generating their own working-level solutions for their own particular use-cases and challenges. The framework proposed in the current work bridges the gap between the big picture policy recommendations already made and individual program needs. It is meant to serve as a roadmap framework that the T and E community can follow in order to provide evidence that artificial intelligence (AI)-enabled and autonomous systems function as intended. At times we echo broad policy recommendations made by others as they will also enable T and E activities. In other places we make more specific recommendations relating to test planning and analysis.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2020
Accession Number
AD1131283

Entities

People

  • Chad Bieber
  • Daniel Porter
  • Michael O McAnally

Organizations

  • Institute for Defense Analyses

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Cyber
  • Electronic Warfare
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Autonomous Systems
  • Bayesian Networks
  • Cognitive Workload
  • Computational Science
  • Computer Languages
  • Computer Vision
  • Human Factors Engineering
  • Human Systems Integration
  • Human-Computer Interaction
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • National Security
  • Neural Networks
  • Psychology
  • Test And Evaluation
  • Test Methods
  • Unmanned Vehicles

Readers

  • Defense Acquisition Program Management
  • Distributed Systems and Data Platform Development
  • Educational Psychology

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Autonomy