What-If Tool (Google)

The What-If Tool, built by Google Research's PAIR (People + AI Research) team, is an open-source tool for visually probing how a trained machine learning model behaves, without writing custom analysis code. It lets you test model predictions across hypothetical input changes, inspect which features most influence a prediction, and compare different fairness metrics side by side. Who it's for: ML practitioners and researchers who need to understand and communicate a model's real behavior — not just its aggregate accuracy — particularly for fairness analysis (comparing how a model performs across different subgroups) or debugging surprising predictions on specific examples. Strengths: it integrates directly into workflows people already use — Colaboratory, Jupyter notebooks, and TensorBoard — rather than requiring a separate standalone application, and it works with TensorFlow models, cloud-hosted models, and arbitrary custom Python functions, so it isn't locked to one modeling framework. Being built by Google's own PAIR research team gives it real credibility as a fairness/interpretability tool, not a hobby project. Weaknesses and limitations: it's a model-inspection and visualization tool, not an automated fairness auditor — it surfaces the data needed for a human to reason about fairness and behavior, but doesn't itself decide whether a model is "fair enough" for a given use case. Effective use assumes real familiarity with the ML concepts (feature importance, fairness metrics) being visualized. Real-world scenario: a team needs to check whether a classification model performs consistently across different demographic subgroups before deploying it, and wants to interactively explore specific edge cases rather than only looking at an aggregate accuracy number.