In this section
@inproceedings{Leofante:2023,
author = {Leofante, F and Lomuscio, A},
pages = {2343--2345},
publisher = {ACM},
title = {Towards robust contrastive explanations for human-neural multi-agent systems},
url = {https://dl.acm.org/doi/10.5555/3545946.3598928},
year = {2023}
}
TY - CPAPER
AB - Generating explanations of high quality is fundamental to the development of trustworthy human-AI interactions. We here study the problem of generating contrastive explanations with formal robustness guarantees. We formalise a new notion of robustness and introduce two novel verification-based algorithms to (i) identify non-robust explanations generated by other methods and (ii) generate contrastive explanations augmented with provablerobustness certificates. We present an implementation and evaluate the utility of the approach on two case studies concerning neural agents trainedon credit scoring and image classification tasks.
AU - Leofante,F
AU - Lomuscio,A
EP - 2345
PB - ACM
PY - 2023///
SP - 2343
TI - Towards robust contrastive explanations for human-neural multi-agent systems
UR - https://dl.acm.org/doi/10.5555/3545946.3598928
UR - http://hdl.handle.net/10044/1/102850
ER -
Artificial Intelligence Network
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Imperial College London
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