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Ensuring Reliable Model Calibration Across Subpopulations

Many machine learning models, despite being globally calibrated, exhibit significant miscalibration within identifiable subgroups. This issue can lead to unreliable performance in critical applications. The problem affects various stakeholders, including model developers and end-users. Subgroup calibration errors can have serious consequences, such as unfair outcomes or decreased trust in AI systems.

Computing & Technology, Computer Science, Machine Learning