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Omission and commission errors underlying AI failures.

Sasanka Sekhar Chanda1, Debarag Narayan Banerjee2

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This study examines Artificial Intelligence (AI) failures in machine learning and deep learning systems. It identifies 28 factors contributing to AI errors, aiding in the development of more reliable AI-ML systems.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Artificial Intelligence (AI) systems, particularly those using machine learning (ML) and deep learning (DL), are increasingly deployed across various domains.
  • Failures in these AI systems can have significant consequences, necessitating a thorough understanding of their root causes.

Purpose of the Study:

  • To investigate the origins of failures in AI systems that utilize machine learning and deep learning.
  • To develop a framework for analyzing errors in AI systems, focusing on inputs, processing logic, and outputs.

Main Methods:

  • Analysis of omission and commission errors within AI systems.
  • Categorization of errors across AI system inputs, processing logic, and outputs.
  • Development of a framework identifying 28 distinct factors contributing to AI failures.

Main Results:

  • Identified specific types of errors (omission and commission) in AI inputs, processing, and outputs.
  • Established a comprehensive framework with 28 factors for diagnosing AI failures.
  • Highlighted emerging areas for research to enhance AI robustness.

Conclusions:

  • The developed framework aids in reconstructing AI failure pathways and guiding corrective actions.
  • This research contributes to building more robust AI-ML systems by improving accuracy (true positives/negatives) and reducing errors (false positives/negatives).
  • Findings support strengthening the reliable application of AI in real-world scenarios.