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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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Population preferences for AI system features across eight different decision-making contexts.

Søren Holm1,2, Thomas Ploug3

  • 1Centre for Social Ethics and Policy, School of Law, University of Manchester, Manchester, United Kingdom.

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Public preferences for artificial intelligence (AI) systems emphasize protective features like explainability and accuracy. These preferences hold across various AI applications, though importance slightly decreases with less significant decision outcomes.

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence Ethics
  • Public Opinion Research

Background:

  • Deep learning-based artificial intelligence (AI) systems are increasingly used as decision-support tools.
  • Existing research on public preferences for AI, particularly in medicine, highlights the importance of features like explainability, accuracy, and human oversight.
  • These 'protective' features are crucial for safeguarding user interests, but their context-specific relevance remains under-explored beyond medical applications.

Purpose of the Study:

  • To investigate public preferences for five specific protective features of AI systems and their implementation.
  • To examine how these preferences vary across eight diverse use cases in public and commercial sectors.
  • To understand the influence of decision impact significance on the perceived importance of AI system features.

Main Methods:

  • A cross-sectional survey study was conducted.
  • The study involved the adult Danish population.
  • Participants' preferences for five protective AI features were assessed across eight different use cases, from medical diagnostics to parking ticket issuance.

Main Results:

  • All five protective features were consistently deemed important across all eight evaluated contexts.
  • The perceived importance of these features slightly decreased when the implications of the AI-driven decision were less significant to respondents.
  • This indicates a general demand for trustworthy AI features regardless of the application domain.

Conclusions:

  • Public preferences for protective AI features are robust and extend beyond the medical domain.
  • Contextual factors, particularly the significance of decision outcomes, influence the perceived importance of these features.
  • Findings underscore the need for developing AI systems that incorporate user-centric protective features across all sectors.