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Instrumental Robots.

Sebastian Köhler1

  • 1Frankfurt School of Finance & Management, Frankfurt am Main, Germany. s.koehler@fs.de.

Science and Engineering Ethics
|August 20, 2020
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Summary
This summary is machine-generated.

This paper argues against viewing human-AI interactions as collaborative agency. Instead, it proposes that artificial intelligence (AI) agents should be considered instruments when determining responsibility for AI actions.

Keywords:
AgencyHuman–robot collaborationInstrumentsResponsibilityResponsibility-gaps

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Area of Science:

  • Philosophy of Technology
  • Artificial Intelligence Ethics
  • Legal Responsibility

Background:

  • Sophisticated artificial intelligence (AI) agents acting autonomously raise complex questions of responsibility.
  • Existing theories of individual agency may not adequately address AI-related harm.
  • Sven Nyholm proposed supervised agency in AI necessitates a shift to collaborative responsibility frameworks.

Purpose of the Study:

  • To critically evaluate the applicability of collaborative agency and responsibility theories to human-AI interactions.
  • To identify the most appropriate theoretical framework for assigning responsibility in cases of AI-induced harm.
  • To propose an alternative framework for understanding responsibility in human-AI relationships.

Main Methods:

  • Philosophical analysis of agency and responsibility concepts.
  • Critique of Sven Nyholm's "supervised agency" and "collaborative agency" arguments.
  • Conceptual exploration of AI as instrumental agents.

Main Results:

  • While acknowledging AI's supervised agency, the paper disputes its classification as collaborative agency.
  • The study concludes that collaborative responsibility is an unsuitable framework for human-AI interactions.
  • The paper posits that viewing AI agents as instruments offers a more fitting model for responsibility.

Conclusions:

  • The interaction between humans and supervised AI agents should not be framed as collaborative agency.
  • Responsibility for AI actions is more accurately grounded in the instrumental use of AI agents.
  • This instrumentalist approach provides a clearer path for assigning accountability in AI-related incidents.