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Human-AI Teaming State-of-the-Art and Research Needs (2022) / Chapter Skim
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8 Identification and Mitigation of Bias in Human-AI Teams
Pages 57-62

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From page 57...
... While there are important evolutionary reasons for many of these human biases, most notably their ability to reduce cognitive load and allow rapid decision making, these same benefits are not necessarily applicable to AI systems that do not suffer from the same significant attention or processing limitations as humans. AI BIASES AI also suffers from biases, which occur when a computer algorithm makes prejudiced decisions based on limited training data (West, Whittaker, and Crawford, 2019)
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From page 58...
... , the impact of AI biases may be large and significant, given the variety of new and novel situations that may be encountered.
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From page 59...
... AI biases can lead to human decision-making biases when the AI system is incorrect or uncertain, and decision-making biases can negatively affect human performance. The committee finds that the interactive effects of bias in the human-AI team may often be subtle, occurring below conscious awareness, but can lead to poor decision outcomes with potential ill effects, such as increased collateral damage, fratricide, or damage from adversarial attacks.
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From page 60...
... Research is greatly needed for cyber defense, to prevent enemies from gaining advantage via human-AI biases, and to determine how defenders can exploit such biases for cyber defense (Gonzalez et al., 2020)
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From page 61...
... Research is required to address resultant human-AI biases that emerge from AI learning based on small and sparse datasets. Research Objective 8-4: Inductive and Emerging Human Biases.
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From page 62...
... 62 HUMAN-AI TEAMING with them, and which can negatively affect an AI system's performance and the performance of the combined human-AI team. Research is needed to better understand the interdependencies between human and AI biases, and to detect and prevent biases that impede effective performance in human-AI teams in multi-domain operations, particularly in the face of adversarial actions that may try to exploit them.
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Key Terms

  • human decision
  • team biases
  • data curation
  • research objective
  • negatively affect

  • preventative detection
  • emergent human
  • predict human
  • expected utility
  • cyber criminals

  • decision bias
  • team bias
  • utility theory
  • subjective expected
  • adversarial attacks

  • affect performance
  • learning based
  • sparse datasets
  • cyber defense
  • affect human

  • human preferences
  • trace human
  • human decisions
  • decision biases
  • biases affect

  • human performance
  • mine human
  • implicit human
  • affect data
  • eliminate human


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