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Related Concept Videos

Introduction to Cognitive Psychology01:20

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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Human-centred explanations for artificial intelligence systems.

C Baber1, P Kandola1, I Apperly2

  • 1School of Computer Science, University of Birmingham, Birmingham, UK.

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|April 8, 2024
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Summary
This summary is machine-generated.

This study introduces human-centered Explainable AI (XAI) to ensure AI recommendations align with user preferences. A new interface helps users understand how their input influences AI decisions, improving trust and transparency in artificial intelligence.

Keywords:
Explainable artificial intelligenceuser interface designuser trial

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence Ethics
  • Explainable AI (XAI)

Background:

  • Growing capabilities of Artificial Intelligence (AI) systems raise concerns about their impact on daily decisions.
  • Existing Explainable AI (XAI) often overlooks the crucial human element in AI-driven recommendations.
  • There is a need for AI systems that are transparent and responsive to individual user needs and preferences.

Purpose of the Study:

  • To formally define human-centered Explainable AI (XAI).
  • To design and evaluate a user interface that facilitates user preference input and comparison with AI recommendations.
  • To enhance user understanding of AI decision-making processes and their influence.

Main Methods:

  • Formal definition of human-centered XAI principles.
  • Development of a user interface for preference indication and comparison.
  • Conducting a user trial to assess the interface's effectiveness.

Main Results:

  • Users could effectively indicate their preferences within the interface.
  • The user trial demonstrated that participants understood how their preferences influenced AI recommendations.
  • Users could also appreciate the contrast between their preferences and those of the AI system.

Conclusions:

  • The proposed human-centered XAI approach enhances user comprehension of AI recommendations.
  • The developed user interface effectively supports user engagement and transparency in AI systems.
  • Guidelines for implementing human-centered XAI are provided to foster more trustworthy AI.