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Neural networks are increasingly being deployed in contexts where safety is a critical concern. In this work, we propose a way to construct neural network classifiers that dynamically repair violations of non-relational safety constraints called safe ordering properties. Safe ordering properties relate requirements on the ordering of a network's output indices to conditions on their input, and are sufficient to express most useful notions of non-relational safety for classifiers. Our approacharXiv:2107.11445v1 fatcat:6ppligqyqfh6fjnmw6gzl3245e