Deep machine unlearning is the problem of'removing' from a trained neural network a subset of its training set.

Machine learning models are often personalized by usinggroup attributesthat encodepersonalcharacteristics(e.g.,sex,agegroup,HIVstatus). Insuchsettings, individuals expect to receive more accurate predictions in return for disclosing group attributes to the personalized model.

Based on this analysis, our second contribution is the discovering of a type of unfairness that arises from theoretically predictable accuracy disparity across some subgroups of test nodes.

Explainability in artificial intelligence is crucial for restoring trust, particularly in areas like face forgery detection, where viewers often struggle to distinguish between real and fabricated content.