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Perspectives on incorporating expert feedback into model updates.

Valerie Chen1, Umang Bhatt2,3, Hoda Heidari1

  • 1Carnegie Mellon University, Pittsburgh, PA, USA.

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Machine learning (ML) practitioners need better ways to use expert feedback for model development. This review proposes a taxonomy to systematically translate domain expertise into ML updates, improving human-AI collaboration.

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Machine learning (ML) models increasingly require alignment with non-technical expert values and goals.
  • Translating domain expertise into effective ML model updates remains a challenge for practitioners.
  • Existing methods lack systematic approaches for incorporating expert feedback into ML development.

Purpose of the Study:

  • To systematically capture and categorize interactions between ML practitioners and domain experts.
  • To develop a taxonomy for matching expert feedback types with specific ML update strategies.
  • To highlight the need for improved methods in integrating non-technical expert insights into ML.

Main Methods:

  • Reviewing existing literature in machine learning and human-computer interaction.
  • Proposing a novel taxonomy to classify expert feedback and corresponding ML updates.
  • Analyzing how feedback at observation or domain levels can inform dataset, loss function, or parameter space adjustments.

Main Results:

  • A structured taxonomy is presented, categorizing expert feedback (observation vs. domain level) and practitioner updates (dataset, loss function, parameter space).
  • The review identifies a gap in current research regarding the systematic incorporation of non-technical expert feedback.
  • The proposed framework facilitates a clearer understanding of the feedback-update loop in ML.

Conclusions:

  • A systematic approach, like the proposed taxonomy, is crucial for effective human-AI collaboration in ML.
  • Further research is needed to address the identified gaps in integrating non-technical expert feedback into ML workflows.
  • Open questions are posed to guide future research in this interdisciplinary area.