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1. Comparative Explanations of Recommendations

   https://doi.org/10.1145/3485447.3512031  

   Yang, Aobo; Wang, Nan; Cai, Renqin; Deng, Hongbo; Wang, Hongning (April 2022, Proceedings of the ACM Web Conference 2022)

   As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i.e., comparative explanations about the recommended items. Ideally, after reading the explanations, a user should reach the same ranking of items as the system’s. Unfortunately, little research attention has yet been paid on such comparative explanations. In this work, we develop an extract-and-refine architecture to explain the relative comparisons among a set of ranked items from a recommender system. For each recommended item, we first extract one sentence from its associated reviews that best suits the desired comparison against a set of reference items. Then this extracted sentence is further articulated with respect to the target user through a generative model to better explain why the item is recommended. We design a new explanation quality metric based on BLEU to guide the end-to-end training of the extraction and refinement components, which avoids generation of generic content. Extensive offline evaluations on two large recommendation benchmark datasets and serious user studies against an array of state-of-the-art explainable recommendation algorithms demonstrate the necessity of comparative explanations and the effectiveness of our solution. 

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2. Selective Explanations

   Paes, L; Wei, D; Calmon, F (December 2024, Advances in Neural Information Processing Systems 37 (NeurIPS 2024))
3. Cartoon Explanations of Image Classifiers

   Kolec, Stefan; Nguyen, Duc; Levie, Ron; Kutyniok, Gitta; Bruna, Joan (October 2022, European Conference on Computer Vision)
4. Probabilistic Sufficient Explanations

   Wang, E.; Khosravi, P.; Van den Broeck, G. (January 2021, Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI))

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5. Robust Ranking Explanations

   Chen, Chao; Guo, Chenghua; Ma, Guixiang; Zeng, Ming; Zhang, Xi; Xie, Sihong (July 2023, Workshop on Interpretable Machine Learning in Healthcare at ICML 2023)

   Robust explanations of machine learning models are critical to establish human trust in the models. Due to limited cognition capability, most humans can only interpret the top few salient features. It is critical to make top salient features robust to adversarial attacks, especially those against the more vulnerable gradient-based explanations. Existing defense measures robustness using lp norms, which have weaker protection power. We define explanation thickness for measuring salient features ranking stability, and derive tractable surrogate bounds of the thickness to design the R2ET algorithm to efficiently maximize the thickness and anchor top salient features. Theoretically, we prove a connection between R2ET and adversarial training. Experiments with a wide spectrum of network architectures and data modalities, including brain networks, demonstrate that R2ET attains higher explanation robustness under stealthy attacks while retaining accuracy. 

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