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Related Concept Videos

Decision Making01:20

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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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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The (Im)perfect Automation Schema: Who Is Trusted More, Automated or Human Decision Support?

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Human experts foster greater trust than artificial intelligence (AI) or novices in support roles. Understanding agent expertise is crucial for effective human-agent interaction and managing trust dynamics.

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Area of Science:

  • Human-computer interaction
  • Cognitive psychology
  • Artificial intelligence

Background:

  • Previous research suggested a perfect automation schema in human-agent interaction.
  • More recent studies presented conflicting evidence regarding automation trust.
  • This ambiguity necessitates further investigation into trust dynamics.

Purpose of the Study:

  • To investigate the dynamics of trust attitude and behavior in human-agent interactions.
  • To examine the influence of agent type (AI, expert, novice) and failure experience on trust.
  • To clarify conflicting findings on automation trust schemas.

Main Methods:

  • An online experiment was conducted using a simulated medical X-ray task.
  • Agent framing (AI, expert, novice) was manipulated between subjects.
  • Failure experience (perfect, imperfect, back-to-perfect support) was manipulated within subjects.
  • Trust attitude, trust behavior, and perceived reliability were measured as dependent variables.

Main Results:

  • Trust attitude and perceived reliability were highest for human experts, followed by AI, then human novices.
  • Observed patterns of trust formation, dissolution, and restoration were consistent across agents.
  • No significant differences in forgiveness after failure experiences were found between agent types.

Conclusions:

  • The findings support the existence of an imperfect automation schema.
  • Agent expertise significantly influences trust attitude and perceived reliability.
  • Consideration of agent expertise is vital for successful human-agent interaction, especially when AI replaces human experts.