% Key papers for Post-hoc explanation analysis (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2016_examples_55716,
  title = {{Examples are not enough, learn to criticize! Criticism for Interpretability}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7428},
}

@inproceedings{cvpr2017_from_12290,
  title = {{From Red Wine to Red Tomato: Composition With Context}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{neurips2018_model_57384,
  title = {{Model Agnostic Supervised Local Explanations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11260},
}

@inproceedings{cvpr2023_pip_21811,
  title = {{PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21105},
}

@inproceedings{aaai2021_on_1087,
  title = {{On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/11575-on-generating-plausible-counterfactual-and-semi-factual-explanations-for-deep-learning/},
}

@inproceedings{neurips2020_how_60019,
  title = {{How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18228},
}

@inproceedings{neurips2019_towards_59101,
  title = {{Towards Automatic Concept-based Explanations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13967},
}

@inproceedings{neurips2022_openxai_64709,
  title = {{OpenXAI: Towards a Transparent Evaluation of Model Explanations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55636},
}

@inproceedings{aaai2022_explainable_2062,
  title = {{Explainable Metaphor Identification Inspired by Conceptual Metaphor Theory}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/10681-explainable-metaphor-identification-inspired-by-conceptual-metaphor-theory/},
}

@inproceedings{aaai2022_entropy_2046,
  title = {{Entropy-Based Logic Explanations of Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/06046-entropy-based-logic-explanations-of-neural-networks/},
}

@inproceedings{cvpr2018_teaching_13635,
  title = {{Teaching Categories to Human Learners With Visual Explanations}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Aodha_Teaching_Categories_to_CVPR_2018_paper.html},
}

@inproceedings{neurips2020_regularizing_60687,
  title = {{Regularizing Black-box Models for Improved Interpretability}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17724},
}

@inproceedings{cvpr2023_overlooked_21796,
  title = {{Overlooked Factors in Concept-Based Explanations: Dataset Choice, Concept Learnability, and Human Capability}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/23045},
}

@inproceedings{aaai2023_pen_4330,
  title = {{PEN: Prediction-Explanation Network to Forecast Stock Price Movement with Better Explainability}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25648},
}

@inproceedings{aaai2022_explain_2061,
  title = {{Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/05277-explain-edit-and-understand-rethinking-user-study-design-for-evaluating-model-explanations/},
}

@inproceedings{aaai2021_learning_910,
  title = {{Learning from the Best: Rationalizing Predictions by Adversarial Information Calibration}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/13771-learning-from-the-best-rationalizing-predictions-by-adversarial-information-calibration/},
}

@inproceedings{aaai2022_feature_2100,
  title = {{Feature Importance Explanations for Temporal Black-Box Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08351-feature-importance-explanations-for-temporal-black-box-models/},
}

@inproceedings{iclr2022_post_31840,
  title = {{Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6565},
}

@inproceedings{cvpr2024_slice_24713,
  title = {{SLICE: Stabilized LIME for Consistent Explanations for Image Classification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31155},
}

@inproceedings{iclr2021_evaluation_30073,
  title = {{Evaluation of Similarity-based Explanations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2619},
}

@inproceedings{aaai2024_dissenting_5717,
  title = {{Dissenting Explanations: Leveraging Disagreement to Reduce Model Overreliance}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30151},
}

@inproceedings{cvpr2025_interactive_26646,
  title = {{Interactive Medical Image Analysis with Concept-based Similarity Reasoning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33732},
}

@inproceedings{aaai2024_pantypes_6917,
  title = {{Pantypes: Diverse Representatives for Self-Explainable Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29223},
}

@inproceedings{aaai2024_semi_7310,
  title = {{Semi-factual Explanations in AI}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30390},
}

@inproceedings{aaai2024_why_7858,
  title = {{‘Why Didn’t You Allocate This Task to Them?’ Negotiation-Aware Task Allocation and Contrastive Explanation Generation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28890},
}

@inproceedings{aaai2025_human_9464,
  title = {{Human and AI Perceptual Differences in Image Classification Errors}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33568},
}

@inproceedings{aaai2024_i_6247,
  title = {{I-CEE: Tailoring Explanations of Image Classification Models to User Expertise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30152},
}

@inproceedings{aaai2024_evaluating_5879,
  title = {{Evaluating the Effectiveness of Explainable Artificial Intelligence Approaches (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30458},
}

@inproceedings{aaai2025_bee_8209,
  title = {{BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32178},
}

@inproceedings{aaai2025_cohex_8451,
  title = {{CohEx: A Generalized Framework for Cohort Explanation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34140},
}

@inproceedings{aaai2025_nomatterxai_10086,
  title = {{NOMATTERXAI: Generating “No Matter What” Alterfactual Examples for Explaining Black-Box Text Classification Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34677},
}

@inproceedings{iclr2025_intrinsic_83960,
  title = {{Intrinsic User-Centric Interpretability through Global Mixture of Experts}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27864},
}

@inproceedings{iclr2025_feature_84010,
  title = {{Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27815},
}

@inproceedings{aaai2026_beyond_108700,
  title = {{Beyond Patches: Mining Interpretable Part-Prototypes for Explainable AI}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/41052},
}

@inproceedings{aaai2024_counterfactual_5516,
  title = {{Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ (Abstract Reprint)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30596},
}

@inproceedings{aaai2025_multi_10047,
  title = {{Multi-concept Model Immunization through Differentiable Model Merging}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33145},
}

@inproceedings{icml2024_understanding_44056,
  title = {{Understanding Inter-Concept Relationships in Concept-Based Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34396},
}

@inproceedings{icml2025_enhancing_47529,
  title = {{Enhancing Performance of Explainable AI Models with Constrained Concept Refinement}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45067},
}

@inproceedings{icml2026_on_51087,
  title = {{On the Variability of Concept Activation Vectors}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Ih5NfDUiOz},
}