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Structured Verification of Machine Learning Models in Industrial Settings.

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Summary

Democratizing artificial intelligence (AI) requires more than automated machine learning (ML); it necessitates structured verification. This study proposes guidelines for verifying ML models, code, and data to ensure reliable AI systems.

Keywords:
AutoMLvalidationverification

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning Engineering

Background:

  • Machine learning (ML) enables automation and scaling of decision-making, driving the democratization of artificial intelligence (AI).
  • Current ML verification processes are unstructured, relying heavily on experience and domain knowledge, hindering AI democratization.
  • Existing methods like cross-validation and explainable AI are insufficient for robust ML system verification.

Purpose of the Study:

  • To highlight the critical challenge of democratizing ML system verification for true AI democratization.
  • To propose structured approaches for verifying ML models, code, and data throughout the ML lifecycle.
  • To provide guidelines for reliable measurement, optimal solution selection, and risk mitigation of bugs and edge-case behaviors.

Main Methods:

  • Critical analysis of current ML verification practices.
  • Discussion of limitations in automated ML (AutoML) and existing verification techniques.
  • Development of a set of guidelines for structured verification across the ML lifecycle.

Main Results:

  • Current ML verification is largely experience-based and not easily automated.
  • Cross-validation and explainable AI do not fully address the challenges of ensuring ML system reliability.
  • Structured verification guidelines can improve the reliability and safety of ML systems.

Conclusions:

  • Democratizing AI hinges on democratizing the verification of ML systems, not just their development.
  • Structured verification of models, code, and data is essential for reliable and safe AI deployment.
  • Implementing proposed guidelines can minimize risks and enhance the performance of ML solutions.