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Artificial intelligence explainability: the technical and ethical dimensions.

John A McDermid1, Yan Jia1, Zoe Porter1

  • 1Department of Computer Science, University of York, Deramore Lane, York YO10 5GH, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|August 16, 2021
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Summary
This summary is machine-generated.

This paper reviews AI explainability (XAI) methods, connecting technical approaches to ethical stakeholder needs. It emphasizes linking XAI to human decisions in AI development for practical application by various professionals.

Keywords:
assuranceexplainabilitymachine learning

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Ethics in AI

Background:

  • Recent advancements in AI have spurred the development of technical methods for enhancing model transparency and interpretability.
  • These techniques are collectively known as AI explainability (XAI) and are crucial for understanding complex AI systems.

Purpose of the Study:

  • To provide a comprehensive overview of existing XAI methods.
  • To connect these technical methods with the underlying purposes of stakeholders seeking explanations.
  • To bridge the technical and ethical dimensions of XAI by highlighting the ethical nature of stakeholder motivations.

Main Methods:

  • Review and categorization of various AI explainability (XAI) techniques.
  • Analysis of stakeholder motivations for requiring AI explanations.
  • Linking specific XAI methods to identified stakeholder purposes and ethical considerations.

Main Results:

  • Identified a strong connection between technical XAI methods and the ethical goals of stakeholders.
  • Emphasized the necessity of integrating XAI with human decision-making processes throughout the AI development lifecycle.
  • Proposed XAI as a framework to support practical judgments for designers, engineers, and regulators.

Conclusions:

  • XAI methods must be contextualized within a broader accountability framework that includes human oversight.
  • The analysis offers a foundational understanding for professionals to select and implement appropriate XAI techniques.
  • This work contributes to the development of more transparent, interpretable, and ethically aligned AI systems.