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The increasing deployment of machine learning as well as legal regulations such as EU's GDPR cause a need for user-friendly explanations of decisions proposed by machine learning models.
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Dhurandhar, A., Chen, P., Luss, R., Tu, C., Ting, P., Shanmugam, K., Das, P.: Explanations based on the missing: Towards contrastive explanations with pertinent negatives. In: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada. pp. 590–601 (2018)
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Kim, B., Koyejo, O., Khanna, R.: Examples are not enough, learn to criticize! criticism for interpretability. In: Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. pp. 2280–2288 (2016), http://papers.nips.cc/paper/6300-examples-are-not-enough-learn-to-criticize-criticism-for-interpretability
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Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: An overview of interpretability of machine learning. In: 5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018, Turin, Italy, October 1-3, 2018. pp. 80–89 (2018). https://doi.org/10.1109/DSAA.2018.00018, https://doi.org/10.1109/DSAA.2018.00018
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Artelt, A., Hammer, B.: Efficient computation of counterfactual explanations of LVQ models. In: Verleysen, M. (ed.) Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020) (2020), accepted
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