Causal fuzzing with budgeted interventions can detect residual direct and indirect influence of unlearned data that standard attribution methods miss due to proxies, cancellations, and masking.
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Industry AI practitioners view model quality through nine attributes with context-dependent priorities, where data imbalance is a key challenge addressed by strategies like active learning, as confirmed by interviews and a follow-up survey.
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Towards Reliable Testing of Machine Unlearning
Causal fuzzing with budgeted interventions can detect residual direct and indirect influence of unlearned data that standard attribution methods miss due to proxies, cancellations, and masking.
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Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions
Industry AI practitioners view model quality through nine attributes with context-dependent priorities, where data imbalance is a key challenge addressed by strategies like active learning, as confirmed by interviews and a follow-up survey.