Model Monitoring in the Absence of Labeled Data via Feature Attributions Distributions

• URL: http://arxiv.org/abs/2501.10774v2
• Date: Sat, 25 Jan 2025 11:50:05 GMT
• Title: Model Monitoring in the Absence of Labeled Data via Feature Attributions Distributions
• Authors: Carlos Mougan,
• Abstract summary: This thesis explores machine learning model monitoring ML before the predictions impact real-world decisions or users. The thesis is structured around two main themes: (i) AI alignment, measuring if AI models behave in a manner consistent with human values and (ii) performance monitoring, measuring if the models achieve specific accuracy goals or desires.
• Score: 5.167069404528051
• License:
• Abstract: Model monitoring involves analyzing AI algorithms once they have been deployed and detecting changes in their behaviour. This thesis explores machine learning model monitoring ML before the predictions impact real-world decisions or users. This step is characterized by one particular condition: the absence of labelled data at test time, which makes it challenging, even often impossible, to calculate performance metrics. The thesis is structured around two main themes: (i) AI alignment, measuring if AI models behave in a manner consistent with human values and (ii) performance monitoring, measuring if the models achieve specific accuracy goals or desires. The thesis uses a common methodology that unifies all its sections. It explores feature attribution distributions for both monitoring dimensions. Using these feature attribution explanations, we can exploit their theoretical properties to derive and establish certain guarantees and insights into model monitoring.

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