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After Harm: A Plea for Moral Repair after Algorithms Have Failed.

Pak-Hang Wong1, Gernot Rieder2

  • 1Academy of Chinese, History, Philosophy and Religion, Faculty of Arts and Social Sciences, Hong Kong Baptist University, Kowloon Tong, Hong Kong. pakhangwong@hkbu.edu.hk.

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Current AI ethics focuses on prevention, neglecting post-harm scenarios. This study introduces "algorithmic imprint" to understand harm

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AI ethicsAI governanceAlgorithmic harmAlgorithmic imprintMoral repairPost-harm scenarios

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Area of Science:

  • AI Ethics and Governance
  • Societal Impacts of AI
  • Algorithmic Decision-Making

Background:

  • Growing societal concerns regarding AI and algorithmic decision-making.
  • Current regulatory and scholarly efforts prioritize risk identification and preventative safeguards.
  • A focus on prevention overlooks post-harm scenarios and their long-term consequences.

Purpose of the Study:

  • To highlight the inattention to post-harm scenarios in AI ethics and governance.
  • To introduce the concept of 'algorithmic imprint' for understanding algorithmic harm.
  • To explore the necessity of moral repair beyond system decommissioning or decision reversal.

Main Methods:

  • Conceptual analysis of AI ethics and governance frameworks.
  • Introduction and definition of the 'algorithmic imprint' concept.
  • Argumentation for the inadequacy of current responses to algorithmic harm.

Main Results:

  • Algorithmic harm is inadequately addressed by current preventative measures.
  • The concept of 'algorithmic imprint' offers a new lens for analyzing harm.
  • Decommissioning systems or reversing decisions is insufficient for full redress.

Conclusions:

  • A significant blind spot exists in AI ethics regarding post-harm scenarios.
  • Addressing algorithmic harm requires acknowledging its lasting effects through 'algorithmic imprint'.
  • Moral repair is essential for a comprehensive response to algorithmic harm.