Act as a machine-learning evaluation specialist. Build The AI Response Calibration Dashboard, a tool for analyzing whether an AI system's stated confidence corresponds to its actual accuracy.
Allow users to import records containing predictions, confidence values, correct answers, categories, and optional model identifiers.
The application should:
Validate confidence ranges and label formats.
Calculate accuracy across configurable confidence bands.
Plot reliability diagrams and compare predicted confidence with observed accuracy.
Calculate metrics such as the Brier score and expected calibration error when their assumptions are satisfied.
Compare calibration across categories and model versions.
Highlight overconfidence and underconfidence patterns.
Allow users to simulate alternative decision thresholds.
Show the trade-off between accepted predictions, coverage, and error rate.
Export a report with formulas and data-quality warnings.
Include synthetic classification datasets and editable examples. Distinguish a model's reported confidence from a statistically calibrated probability. Explain how sample size and class imbalance affect interpretation. Do not claim that a single aggregate score establishes reliability in every context.
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