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The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 1901
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Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Debnath, A. K., Lopez de Compadre, R. L., Debnath, G., Shusterman, A. J., and Hansch, C · 1991
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The mnist database of handwritten digits
LeCun, Y · 1998
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The impact of the mit-bih arrhythmia database
Moody, G. B., and Mark, R. G · 2001
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Learning question classifiers
Li, X., and Roth, D · 2002
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Recognizing human actions: a local svm approach
Schuldt, C., Laptev, I., and Caputo, B · 2004
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Protein function prediction via graph kernels
Borgwardt, K. M., Ong, C. S., Schönauer, S., Vishwanathan, S., Smola, A. J., and Kriegel, H.-P · 2005
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What is a concept?
Goguen, J · 2005
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Pang, B., and Lee, L · 2005
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Approximable concepts, chu spaces, and information systems
Zhang, G.-Q., and Shen, G · 2006
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Visualizing higher-layer features of a deep network
Erhan, D., Bengio, Y., Courville, A., and Vincent, P · 2009
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Multiparameter intelligent monitoring in intensive care ii (mimic-ii): a public-access intensive care unit database
Saeed, M., Villarroel, M., Reisner, A. T., Clifford, G., Lehman, L.-W., Moody, G., Heldt, T., Kyaw, T. H., Moody, B., and Mark, R. G · 2011
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Formal concept analysis: mathematical foundations
Ganter, B., and Wille, R · 2012
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., and Potts, C · 2013
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Analyzing the performance of multilayer neural networks for object recognition
Agrawal, P., Girshick, R., and Malik, J · 2014
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Deep inside convolutional networks: visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D., and Fergus, R · 2014
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Deep graph kernels
Yanardag, P., and Vishwanathan, S · 2015
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Object detectors emerge in deep scene cnns
Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., and Torralba, A · 2015
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Semi-supervised classification with graph convolutional networks
Kipf, T. N., and Welling, M · 2016
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" why should i trust you?" explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2016
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Network dissection: Quantifying interpretability of deep visual representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A · 2017
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Deformable convolutional networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y · 2017
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European union regulations on algorithmic decision-making and a “right to explanation”
Goodman, B., and Flaxman, S · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M., and Lee, S.-I · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Speer, R., Chin, J., and Havasi, C · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
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Peeking inside the black-box: a survey on explainable artificial intelligence (xai)
Adadi, A., and Berrada, M · 2018
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
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Towards robust interpretability with self-explaining neural networks
Alvarez Melis, D., and Jaakkola, T · 2018
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Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Fong, R., and Vedaldi, A · 2018
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A simple and effective model-based variable importance measure
Greenwell, B. M., Boehmke, B. C., and McCarthy, A. J · 2018
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Local rule-based explanations of black box decision systems
Guidotti, R., Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., and Giannotti, F · 2018
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A survey of methods for explaining black box models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., et al · 2018
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Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions
Li, O., Liu, H., Chen, C., and Rudin, C · 2018
Cited alongside, same era.
The v-dem measurement model: latent variable analysis for cross-national and cross-temporal expert-coded data
Pemstein, D., Marquardt, K. L., Tzelgov, E., Wang, Y.-t., Krusell, J., and Miri, F · 2018
Cited alongside, same era.
The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Tschandl, P., Rosendahl, C., and Kittler, H · 2018
Cited alongside, same era.
Interpretable convolutional neural networks
Zhang, Q., Wu, Y. N., and Zhu, S.-C · 2018
Cited alongside, same era.
Interpretable basis decomposition for visual explanation
Zhou, B., Sun, Y., Bau, D., and Torralba, A · 2018
Cited alongside, same era.
Concept activation regions: A generalized framework for concept-based explanations
Crabbé, J., and van der Schaar, M · 2022
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Deformable protopnet: An interpretable image classifier using deformable prototypes
Donnelly, J., Barnett, A. J., and Chen, C · 2022
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Concept embedding models: Beyond the accuracy-explainability trade-off
Espinosa Zarlenga, M., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., Lió, P., and Jamnik, M · 2022
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Addressing leakage in concept bottleneck models
Havasi, M., Parbhoo, S., and Doshi-Velez, F · 2022
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Extending logic explained networks to text classification
Jain, R., Ciravegna, G., Barbiero, P., Giannini, F., Buffelli, D., and Lio, P · 2022
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Glancenets: Interpretable, leak-proof concept-based models
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Carreira, J., Noland, E., Hillier, C., and Zisserman, A · 2019
Cited alongside, same era.
This looks like that: deep learning for interpretable image recognition
Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., and Su, J. K · 2019
Cited alongside, same era.
Interpretation of neural networks is fragile
Ghorbani, A., Abid, A., and Zou, J · 2019
Cited alongside, same era.
Towards automatic concept-based explanations
Ghorbani, A., Wexler, J., Zou, J. Y., and Kim, B · 2019
Cited alongside, same era.
On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Gondal, M. W., Wuthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Schölkopf, B., and Bauer, S · 2019
Cited alongside, same era.
Explaining classifiers with causal concept effect (cace)
Goyal, Y., Feder, A., Shalit, U., and Kim, B · 2019
Cited alongside, same era.
Interpretable image recognition with hierarchical prototypes
Hase, P., Chen, C., Li, O., and Rudin, C · 2019
Cited alongside, same era.
Marconato, E., Passerini, A., and Teso, S · 2022
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Vael: Bridging variational autoencoders and probabilistic logic programming
Misino, E., Marra, G., and Sansone, E · 2022
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Clip-dissect: Automatic description of neuron representations in deep vision networks
Oikarinen, T., and Weng, T.-W · 2022
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Attention-based interpretability with concept transformers
Rigotti, M., Miksovic, C., Giurgiu, I., Gschwind, T., and Scotton, P · 2022
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Interpretable image classification with differentiable prototypes assignment
Rymarczyk, D., Struski, Ł., Górszczak, M., Lewandowska, K., Tabor, J., and Zieliński, B · 2022
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A framework for learning ante-hoc explainable models via concepts
Sarkar, A., Vijaykeerthy, D., Sarkar, A., and Balasubramanian, V. N · 2022
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Concept bottleneck model with additional unsupervised concepts
Sawada, Y., and Nakamura, K · 2022
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Post-hoc concept bottleneck models
Yuksekgonul, M., Wang, M., and Zou, J · 2022
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From attribution maps to human-understandable explanations through concept relevance propagation
Achtibat, R., Dreyer, M., Eisenbraun, I., Bosse, S., Wiegand, T., Samek, W., and Lapuschkin, S · 2023
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Ripple: concept-based interpretation for raw time series models in education
Asadi, M., Swamy, V., Frej, J., Vignoud, J., Marras, M., and Käser, T · 2023
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Interpretable neural-symbolic concept reasoning
Barbiero, P., Ciravegna, G., Giannini, F., Espinosa Zarlenga, M., Magister, L. C., Tonda, A., Lio, P., Precioso, F., Jamnik, M., and Marra, G · 2023
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Benchmarking and survey of explanation methods for black box models
Bodria, F., Giannotti, F., Guidotti, R., Naretto, F., Pedreschi, D., and Rinzivillo, S · 2023
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Concept-level debugging of part-prototype networks
Bontempelli, A., Teso, S., Tentori, K., Giunchiglia, F., and Passerini, A · 2023
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Making corgis important for honeycomb classification: Adversarial attacks on concept-based explainability tools
Brown, D., and Kvinge, H · 2023
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Concept-based explanations for out-of-distribution detectors
Choi, J., Raghuram, J., Feng, R., Chen, J., Jha, S., and Prakash, A · 2023
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Logic explained networks
Ciravegna, G., Barbiero, P., Giannini, F., Gori, M., Lió, P., Maggini, M., and Melacci, S · 2023
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Explainable ai (xai): Core ideas, techniques, and solutions
Dwivedi, R., Dave, D., Naik, H., Singhal, S., Omer, R., Patel, P., Qian, B., Wen, Z., Shah, T., Morgan, G., et al · 2023
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Craft: Concept recursive activation factorization for explainability
Fel, T., Picard, A., Bethune, L., Boissin, T., Vigouroux, D., Colin, J., Cadène, R., and Serre, T · 2023
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Spatial-temporal concept based explanation of 3d convnets
Ji, Y., Wang, Y., and Kato, J · 2023
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Probabilistic concept bottleneck models
Kim, E., Jung, D., Park, S., Kim, S., and Yoon, S · 2023
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" help me help the ai": Understanding how explainability can support human-ai interaction
Kim, S. S., Watkins, E. A., Russakovsky, O., Fong, R., and Monroy-Hernández, A · 2023
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When are post-hoc conceptual explanations identifiable?
Leemann, T., Kirchhof, M., Rong, Y., Kasneci, E., and Kasneci, G · 2023
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Zebra: Explaining rare cases through outlying interpretable concepts
Madeira, P., Carreiro, A., Gaudio, A., Rosado, L., Soares, F., and Smailagic, A · 2023
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Label-free concept bottleneck models
Oikarinen, T., Das, S., Nguyen, L. M., and Weng, T.-W · 2023
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Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability
Ramaswamy, V. V., Kim, S. S., Fong, R., and Russakovsky, O · 2023
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A closer look at the intervention procedure of concept bottleneck models
Shin, S., Jo, Y., Ahn, S., and Lee, N · 2023
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Multi-dimensional concept discovery (mcd): A unifying framework with completeness guarantees
Vielhaben, J., Bluecher, S., and Strodthoff, N · 2023
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Learning bottleneck concepts in image classification
Wang, B., Li, L., Nakashima, Y., and Nagahara, H · 2023
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Causal proxy models for concept-based model explanations
Wu, Z., D’Oosterlinck, K., Geiger, A., Zur, A., and Potts, C · 2023
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Global concept-based interpretability for graph neural networks via neuron analysis
Xuanyuan, H., Barbiero, P., Georgiev, D., Magister, L. C., and Liò, P · 2023
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Language in a bottle: Language model guided concept bottlenecks for interpretable image classification
Yang, Y., Panagopoulou, A., Zhou, S., Jin, D., Callison-Burch, C., and Yatskar, M · 2023
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IMDb Non-Commercial Datasets
IMDb · 2026
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ISIC Challenge 2020
International Skin Imaging Collaboration (ISIC) · 2026
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AwA2: An Attribute Database for Animal Behavior Analysis
IST Austria · 2026
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Osteoarthritis Initiative (OAI) Data
Osteoarthritis Initiative · 2026
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COMPAS Analysis Repository
ProPublica · 2026
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