Dealing with high-risk or safety-critical applications calls for the
development of trustworthy AI systems. Beyond prediction, such systems must
offer a number of additional facilities, including explanation and
verification. The case when the prediction made is deemed wrong by an expert
calls for still another operation, called rectification.
Rectifying a classifier aims to guarantee that the predictions made by the
classifier (once rectified) comply with the expert knowledge. Here, the
expert is supposed more reliable than the predictor, but their knowledge is
Focusing on Boolean classifiers, I will present rectification as a change
operation. Following an axiomatic approach, I will give some postulates that
must be satisfied by rectification operators. I will show that the family of
rectification operators is disjoint from the family of revision operators
and from the family of update operators. I will also present a few results
about the computation of a rectification operation.
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