Trust, Trustworthiness, and Assurance of AI and Autonomy

Abstract

The importance of trust in artificial intelligence and autonomous systems (AIAS) is widely touted, but not necessarily in productive ways. This paper argues that discourse about the importance of trust can obscure more than it illuminates, misleadingly suggesting similarities (and common solutions) across fundamentally unrelated challenges. In particular, it is important to distinguish whose trust is required, and whether that trust is subjective or objective. The paper also pushes back against the idea that trust can be built in as a property of the AIAS, and argues that assurance cases, as originally developed by the safety and security communities, provide a framework for understanding trustworthiness, disambiguating different concepts of trust, and enabling test, evaluation, verification, and validation (TEV and V) approaches to AIAS development and employment.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2021
Accession Number
AD1150274

Entities

People

  • David M. Tate

Organizations

  • Institute for Defense Analyses

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Autonomous Systems
  • Autonomy
  • Employment
  • Engineering
  • Human-Machine Interaction
  • Human-Machine Systems
  • Personnel Management
  • Security
  • Standards
  • Systems Engineering
  • Test And Evaluation
  • Transport Aircraft
  • Unmanned Vehicles
  • Validation
  • Verification

Fields of Study

  • Computer science

Readers

  • Cybersecurity.
  • Educational Psychology
  • Joint Military Operations and Doctrine.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Autonomy
  • Autonomy - Human-Robot Interaction