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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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One Dimensional Turing-Like Handshake Test for Motor Intelligence
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In human-machine trust, humans rely on a simple averaging strategy.

Jonathon Love1, Quentin F Gronau2, Gemma Palmer2

  • 1Psychological Sciences, University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia. jonathon.love@uon.edu.au.

Cognitive Research: Principles and Implications
|September 1, 2024
PubMed
Summary
This summary is machine-generated.

Human-AI collaboration trust is complex. Participants in a study often averaged their judgment with AI recommendations, rather than fully trusting or distrusting the machine agent.

Keywords:
AutomationHuman–machine teamJudge–advisor systemTrust

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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is increasingly integrated into daily life, necessitating research into effective human-AI collaboration.
  • Understanding how humans integrate machine recommendations, especially when they conflict with personal judgment, is crucial for trust in human-machine teaming.

Purpose of the Study:

  • To investigate trust dynamics in human-machine teaming using a perceptual judgment task.
  • To analyze how the 'advice distance' (discrepancy between human and machine judgment) influences trust and behavioral adjustments.

Main Methods:

  • Participants performed a perceptual estimation task and received a recommendation from a machine agent.
  • Trust was measured by the degree participants shifted their second response towards the machine's recommendation.
  • The study analyzed how participants' judgments changed in response to varying distances between their initial estimate and the AI's advice.

Main Results:

  • While some participants either increased or decreased trust based on advice distance, the most common behavior was not extreme.
  • A simple averaging model best explained participants' trust behavior, indicating a nuanced integration of AI recommendations.
  • Human trust in machine agents did not follow simple distrust or over-reliance patterns when recommendations differed significantly.

Conclusions:

  • Human trust in AI is not binary; it involves complex integration strategies, often leaning towards averaging judgments.
  • Findings suggest that simple models of trust, like averaging, are more accurate in predicting human-AI collaboration behavior.
  • Implications for developing more effective human-machine teaming systems and refining theories of trust in AI are discussed.