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Opportunities for human factors in machine learning.

Jessica A Baweja1, Corey K Fallon1, Brett A Jefferson1

  • 1Pacific Northwest National Laboratory, Richland, WA, United States.

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Summary
This summary is machine-generated.

Human factors methods can help data scientists manage the complexity of rapidly advancing machine learning (ML) techniques. This study identifies ML workflow challenges and proposes human factors solutions for better tools and guidance.

Keywords:
artificial intelligencedata sciencehuman factorsmachine learningneural networks

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Machine learning (ML) and deep learning are advancing rapidly.
  • Data scientists face challenges maintaining expertise in cutting-edge ML techniques.
  • Human factors methods offer potential solutions to these challenges.

Purpose of the Study:

  • Apply human factors methods to the field of machine learning.
  • Understand data scientists' ML workflows and challenges.
  • Identify how human factors can address these difficulties.

Main Methods:

  • Conducted semi-structured interviews with data scientists at a National Laboratory.
  • Analyzed interview data to generalize ML model working processes.
  • Identified challenges encountered at each step of the ML process.

Main Results:

  • A generalized process model for working with machine learning models was developed.
  • Specific issues and challenges within each step of the ML workflow were identified.
  • The potential contribution of human factors to addressing these challenges was explored.

Conclusions:

  • Recommendations for collaboration between data scientists and human factors experts are provided.
  • Aims to improve tools, knowledge, and guidance for machine learning scientists.
  • Highlights the importance of human factors in the advancement of ML.