PAIR-code/what-if-tool

• highabout#1

  Update the repository description to be more functional

  Why:

  CURRENT

  Source code/webpage/demos for the What-If Tool

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  An interactive, no-code visual tool for understanding, debugging, and comparing black-box ML models, with a focus on fairness and 'what-if' scenarios.

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• highreadme#2

  Refine the README's opening paragraph to highlight core features and use cases

  Why:

  CURRENT

  The What-If Tool (WIT) provides an easy-to-use interface for expanding understanding of a black-box classification or regression ML model. With the plugin, you can perform inference on a large set of examples and immediately visualize the results in a variety of ways. Additionally, examples can be edited manually or programmatically and re-run through the model in order to see the results of the changes. It contains tooling for investigating model performance and fairness over subsets of a dataset.

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  The What-If Tool (WIT) is an interactive, no-code visual interface designed to help users understand, debug, and compare black-box classification and regression ML models. It allows you to perform inference on large datasets, visualize results, and explore model behavior by editing examples and running 'what-if' scenarios. WIT provides powerful tooling for investigating model performance and fairness across data subsets.

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• mediumtopics#3

  Expand repository topics with more specific keywords

  Why:

  CURRENT

  colaboratory, jupyterlab-extension, machine-learning, ml-fairness, tensorboard, visualization

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  colaboratory, jupyterlab-extension, machine-learning, ml-fairness, tensorboard, visualization, explainable-ai, xai, model-debugging, interpretability, interactive-tool, no-code-ml

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How to visually debug and understand machine learning model predictions and fairness?

  you: not recommended
  AI recommended (in order):

  1. What-If Tool (WIT)
  2. Microsoft Fairlearn (fairlearn/fairlearn)
  3. SHAP (SHapley Additive exPlanations) (shap/shap)
  4. LIME (Local Interpretable Model-agnostic Explanations) (marcotcr/lime)
  5. TensorFlow Lite Model Analyzer
  6. InterpretML (microsoft/interpret)
  7. Aequitas (dssg/aequitas)

  AI recommended 7 alternatives but never named PAIR-code/what-if-tool. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  Interactive no-code tool to explore ML model behavior with hypothetical data changes?

  you: not recommended
  AI recommended (in order):

  1. What-If Tool (WIT) (google/what-if-tool)
  2. Microsoft Fairlearn Dashboard (fairlearn/fairlearn)
  3. Lobe
  4. Google Cloud AI Platform Explainable AI (XAI)
  5. H2O.ai Wave Apps (h2oai/wave)
  6. DataRobot

  AI recommended 6 alternatives but never named PAIR-code/what-if-tool. This is the gap to close.

  Show full AI answer

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of PAIR-code/what-if-tool?

  pass
  ⚠AI did not name PAIR-code/what-if-tool — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts PAIR-code/what-if-tool in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named PAIR-code/what-if-tool explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo PAIR-code/what-if-tool solve, and who is the primary audience?

  pass
  ⚠AI did not name PAIR-code/what-if-tool — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?