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Supporting Trustworthy AI Through Machine Unlearning.

Emmie Hine1,2,3, Claudio Novelli4,5, Mariarosaria Taddeo6,7

  • 1Department of Legal Studies, University of Bologna, Via Zamboni, 27/29, 40121, Bologna, Italy. emmie.hine@yale.edu.

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Summary
This summary is machine-generated.

Machine unlearning (MU) supports trustworthy AI principles and the right to be forgotten. However, ethical risks necessitate policy recommendations for responsible research and AI development.

Keywords:
GELSIMachine learningMachine unlearningTechnology policyTrustworthy AI

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • AI Ethics

Background:

  • Machine unlearning (MU) is frequently discussed concerning data privacy and the 'right to be forgotten'.
  • The Organisation for Economic Co-operation and Development (OECD) principles for trustworthy AI are increasingly influential in global AI governance.
  • There is a need to bridge the gap between theoretical AI principles and practical implementation.

Purpose of the Study:

  • To demonstrate how machine unlearning can operationalize the OECD's trustworthy AI principles.
  • To identify and analyze the ethical risks associated with implementing machine unlearning.
  • To propose policy recommendations for fostering responsible MU research and adoption.

Main Methods:

  • Conceptual analysis linking machine unlearning capabilities to OECD AI principles.
  • Ethical risk assessment of machine unlearning implementation.
  • Policy analysis and formulation based on identified risks and benefits.

Main Results:

  • Machine unlearning directly supports key OECD principles for trustworthy AI, including fairness, transparency, and accountability.
  • The study identifies potential ethical challenges such as unintended data leakage and the complexity of verification.
  • A framework of six policy recommendation categories is proposed to guide future MU development.

Conclusions:

  • Machine unlearning serves as a practical mechanism for implementing trustworthy AI principles.
  • Addressing ethical risks through proactive policy is crucial for maximizing the benefits of machine unlearning.
  • The findings provide a foundation for developing regulatory and research agendas for machine unlearning.