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Algorithmic decision-making lacks transparency. Full public transparency is inadvisable due to privacy and gaming risks, but oversight bodies require it. Machine learning models should be interpretable.

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Area of Science:

  • Computer Science
  • Ethics
  • Law

Background:

  • Machine learning algorithms increasingly influence critical decisions.
  • Current algorithmic systems often lack transparency, hindering accountability.
  • The need for transparency in artificial intelligence (AI) decision-making is a growing concern.

Purpose of the Study:

  • To investigate whether transparency can restore accountability in algorithmic decision-making systems.
  • To examine the objections and feasibility of full transparency for AI systems.
  • To propose alternative approaches for enhancing understanding of algorithmic processes.

Main Methods:

  • Analysis of objections to full transparency, including privacy, gaming the system, and competitive edge.
  • Evaluation of the effectiveness of transparency in achieving accountability.
  • Exploration of interpretability methods for machine learning models.

Main Results:

  • Full transparency for the public is generally not advisable due to significant risks.
  • Oversight bodies may require full transparency as a feasible option for accountability.
  • Sophisticated algorithms are often inherently opaque, limiting answerability.

Conclusions:

  • Complete public transparency for algorithmic systems is currently not feasible or advisable.
  • Transparency for specialized oversight bodies is a more practical approach.
  • Prioritizing the interpretability of machine learning models is crucial for understanding and accountability.