Decision Confidence in Human-Machine Teaming

Abstract

This project aimed to characterize and increase trust in human-machine teaming by leveraging insights from research on trust and influence in human social and group decision making. We conducted two complementary streams of research to investigate (1) the causes and consequences of algorithm aversion, whereby human operators systematically down weight advice from artificial systems after seeing them err; and (2) the features of algorithmic design that promote trust in advice from artificial systems.

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Document Details

Document Type
Technical Report
Publication Date
Dec 22, 2022
Accession Number
AD1194113

Entities

People

  • Nick Yeung

Organizations

  • University of Oxford

Tags

Communities of Interest

  • Autonomy
  • C4I

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Autonomous Systems
  • Contrast
  • Control Systems
  • Data Analysis
  • First Responders
  • Human-Machine Systems
  • Intelligent Systems
  • Judgment
  • Maximum Likelihood Estimation
  • Military Research
  • Psychology
  • Reliability
  • Space Force
  • Standards
  • Warfare

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.