Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

Catalog evidence and recorded chart appearances. Inclusion is not an endorsement.

Source snapshot imported 2026-10-20. This is not a live feed. Source and contributions.

Evidence and limits

Finding: Practitioners used interpretability tools in varied, sometimes overconfident ways and could draw unwarranted conclusions from tool outputs.

Impact: Strengthen interpretation training, caveats, and misuse tests. Reject tool availability as proof of understanding.

Boundary: Data-science workflows differ from general agent use. Next: which caveats change behavior rather than become ignored boilerplate?

Flags: data-scientist sample; tool-specific misunderstandings

Record State: Catalog review recorded; not a performance endorsement

What was reviewed: Structure and relevance screened; not a full evaluation

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Recorded chart appearances

  • 2026-10-13: position 57. Relevant to the recorded review topic (see source); Structure and relevance screened; not a full evaluation evidence
  • 2026-10-12: position 56. Relevant to the recorded review topic (see source); Structure and relevance screened; not a full evaluation evidence