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Reasons underdetermination in meaningful human control.

Atay Kozlovski1

  • 1Delft University of Technology, Delft, Netherlands.

Ethics and Information Technology
|October 20, 2025
PubMed
Summary
This summary is machine-generated.

Meaningful Human Control (MHC) for AI needs refinement. This study identifies "reasons underdetermination" as a gap, where conflicting reasons prevent clear AI decision-making, impacting AI safety and evaluation.

Keywords:
Decision Support SystemsIncommensurabilityMeaningful Human Control (MHC)Moral ResponsibilityReasonsTracking and TracingUnderdetermination

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Area of Science:

  • Artificial Intelligence Ethics
  • Philosophy of Technology
  • AI Safety Research

Background:

  • AI proliferation raises safety and responsibility concerns.
  • Meaningful Human Control (MHC) is a framework for AI design and evaluation.
  • Existing MHC frameworks require theoretical and practical advancements.

Purpose of the Study:

  • To identify a critical gap in the Meaningful Human Control (MHC) framework.
  • To explore the implications of reasons underdetermination for MHC.
  • To propose refinements for the MHC framework based on axiology and decision theory.

Main Methods:

  • Drawing on research in axiology and rational decision theory.
  • Analyzing the role of 'reasons' within the MHC framework's Tracking and Tracing conditions.
  • Examining the phenomenon of reasons underdetermination.

Main Results:

  • Identified a gap in MHC concerning situations where reasons do not yield a single preferable action.
  • Demonstrated that reasons underdetermination challenges the Tracking condition of MHC.
  • Showed that reasons underdetermination challenges the Tracing condition of MHC.

Conclusions:

  • The MHC framework must address reasons underdetermination for robust AI safety.
  • Refinements are needed to handle conflicting or inconclusive reasons in AI decision-making.
  • Integrating insights from axiology and decision theory can enhance MHC's practical applicability.