
@article{jacovi2021contrastive,
  title={Contrastive Explanations for Model Interpretability},
  author={Jacovi, Alon and Swayamdipta, Swabha and Ravfogel, Shauli and Elazar, Yanai and Choi, Yejin and Goldberg, Yoav},
  journal={arXiv preprint arXiv:2103.01378},
  year={2021}
}

@article{adadi2018peeking,
  title={Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)},
  author={Adadi, Amina and Berrada, Mohammed},
  journal={IEEE access},
  volume={6},
  pages={52138--52160},
  year={2018},
  publisher={IEEE}
}

@article{schwarz1978estimating,
  title={Estimating the dimension of a model},
  author={Schwarz, Gideon},
  journal={The annals of statistics},
  pages={461--464},
  year={1978},
  publisher={JSTOR}
}

@article{xue2022learn,
  title={Learn decision trees with deep visual primitives},
  author={Xue, Mengqi and Zhang, Haofei and Huang, Qihan and Song, Jie and Song, Mingli},
  journal={Journal of Visual Communication and Image Representation},
  volume={89},
  pages={103682},
  year={2022},
  publisher={Elsevier}
}

@inproceedings{nauta2021neural,
  title={Neural prototype trees for interpretable fine-grained image recognition},
  author={Nauta, Meike and Van Bree, Ron and Seifert, Christin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14933--14943},
  year={2021}
}


@article{shitole2021one,
  title={One explanation is not enough: structured attention graphs for image classification},
  author={Shitole, Vivswan and Li, Fuxin and Kahng, Minsuk and Tadepalli, Prasad and Fern, Alan},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={11352--11363},
  year={2021}
}

@inproceedings{szegedy2015going,
  title={Going deeper with convolutions},
  author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1--9},
  year={2015}
}

@article{kim2021machine,
  title={Machine learning techniques for accountability},
  author={Kim, Been and Doshi-Velez, Finale},
  journal={AI Magazine},
  volume={42},
  number={1},
  pages={47--52},
  year={2021}
}

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@inproceedings{downs2010your,
  title={Are your participants gaming the system? Screening Mechanical Turk workers},
  author={Downs, Julie S and Holbrook, Mandy B and Sheng, Steve and Cranor, Lorrie Faith},
  booktitle={Proceedings of the SIGCHI conference on human factors in computing systems},
  pages={2399--2402},
  year={2010}
}

@article{miller1995wordnet,
  title={WordNet: a lexical database for English},
  author={Miller, George A},
  journal={Communications of the ACM},
  volume={38},
  number={11},
  pages={39--41},
  year={1995},
  publisher={ACM New York, NY, USA}
}

@inproceedings{
wang2022why,
title={{\textquotedblleft}Why Not Other Classes?{\textquotedblright}: Towards Class-Contrastive Back-Propagation Explanations},
author={Yipei Wang and Xiaoqian Wang},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=X5eFS09r9hm}
}

@inproceedings{akula2020cocox,
  title={Cocox: Generating conceptual and counterfactual explanations via fault-lines},
  author={Akula, Arjun and Wang, Shuai and Zhu, Song-Chun},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={03},
  pages={2594--2601},
  year={2020}
}

@article{fel2021cannot,
  title={What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods},
  author={Fel, Thomas and Colin, Julien and Cad{\`e}ne, R{\'e}mi and Serre, Thomas},
  journal={arXiv preprint arXiv:2112.04417},
  year={2021}
}

@inproceedings{prabhushankar2020contrastive,
  title={Contrastive Explanations In Neural Networks},
  author={Prabhushankar, Mohit and Kwon, Gukyeong and Temel, Dogancan and AlRegib, Ghassan},
  booktitle={2020 IEEE International Conference on Image Processing (ICIP)},
  pages={3289--3293},
  year={2020},
  organization={IEEE}
}
@inproceedings{petsiuk2018rise,
  author    = {Vitali Petsiuk and
          Abir Das and
          Kate Saenko},
  title     = {RISE: Randomized Input Sampling for Explanation of Black-box Models},
  booktitle = {British Machine Vision Conference (BMVC)},
  year      = {2018}
}

@inproceedings{akula2020cocox,
  title={CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines.},
  author={Akula, Arjun R and Wang, Shuai and Zhu, Song-Chun},
  booktitle={AAAI},
  pages={2594--2601},
  year={2020}
}
@inproceedings{goyal2019counterfactual,
  title={Counterfactual visual explanations},
  author={Goyal, Yash and Wu, Ziyan and Ernst, Jan and Batra, Dhruv and Parikh, Devi and Lee, Stefan},
  booktitle={International Conference on Machine Learning},
  pages={2376--2384},
  year={2019},
  organization={PMLR}
}
@inproceedings{wang2020scout,
  title={SCOUT: Self-aware Discriminant Counterfactual Explanations},
  author={Wang, Pei and Vasconcelos, Nuno},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8981--8990},
  year={2020}
}

@book{samek2019explainable,
  title={Explainable AI: interpreting, explaining and visualizing deep learning},
  author={Samek, Wojciech and Montavon, Gr{\'e}goire and Vedaldi, Andrea and Hansen, Lars Kai and M{\"u}ller, Klaus-Robert},
  volume={11700},
  year={2019},
  publisher={Springer Nature}
}
@article{li2020survey,
  title={A Survey of Data-driven and Knowledge-aware e{X}plainable {AI}},
  author={Li, Xiao-Hui and Cao, Caleb Chen and Shi, Yuhan and Bai, Wei and Gao, Han and Qiu, Luyu and Wang, Cong and Gao, Yuanyuan and Zhang, Shenjia and Xue, Xun and others},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2020},
  publisher={IEEE}
}
@inproceedings{zhang2018interpretable,
  title={Interpretable convolutional neural networks},
  author={Zhang, Quanshi and Nian Wu, Ying and Zhu, Song-Chun},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={8827--8836},
  year={2018}
}

@article{pya2015shape,
  title={Shape constrained additive models},
  author={Pya, Natalya and Wood, Simon N},
  journal={Statistics and Computing},
  volume={25},
  number={3},
  pages={543--559},
  year={2015},
  publisher={Springer}
}

@article{lundberg2017unified,
  title={A unified approach to interpreting model predictions},
  author={Lundberg, Scott M and Lee, Su-In},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@inproceedings{sundararajan2017axiomatic,
  title={Axiomatic attribution for deep networks},
  author={Sundararajan, Mukund and Taly, Ankur and Yan, Qiqi},
  booktitle={International conference on machine learning},
  pages={3319--3328},
  year={2017},
  organization={PMLR}
}


@inproceedings{jacovi2021formalizing,
  title={Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in AI},
  author={Jacovi, Alon and Marasovi{\'c}, Ana and Miller, Tim and Goldberg, Yoav},
  booktitle={Proceedings of the 2021 ACM conference on fairness, accountability, and transparency},
  pages={624--635},
  year={2021}
}

@article{buccinca2021trust,
  title={To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making},
  author={Bu{\c{c}}inca, Zana and Malaya, Maja Barbara and Gajos, Krzysztof Z},
  journal={Proceedings of the ACM on Human-Computer Interaction},
  volume={5},
  number={CSCW1},
  pages={1--21},
  year={2021},
  publisher={ACM New York, NY, USA}
}

@article{adebayo2018sanity,
  title={Sanity checks for saliency maps},
  author={Adebayo, Julius and Gilmer, Justin and Muelly, Michael and Goodfellow, Ian and Hardt, Moritz and Kim, Been},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}


@article{miller2023explainable,
  title={Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support},
  author={Miller, Tim},
  journal={arXiv preprint arXiv:2302.12389},
  year={2023}
}

@inproceedings{mothilal2020explaining,
  title={Explaining machine learning classifiers through diverse counterfactual explanations},
  author={Mothilal, Ramaravind K and Sharma, Amit and Tan, Chenhao},
  booktitle={Proceedings of the 2020 conference on fairness, accountability, and transparency},
  pages={607--617},
  year={2020}
}

@article{arrieta2020explainable,
  title={Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI},
  author={Arrieta, Alejandro Barredo and D{\'\i}az-Rodr{\'\i}guez, Natalia and Del Ser, Javier and Bennetot, Adrien and Tabik, Siham and Barbado, Alberto and Garc{\'\i}a, Salvador and Gil-L{\'o}pez, Sergio and Molina, Daniel and Benjamins, Richard and others},
  journal={Information fusion},
  volume={58},
  pages={82--115},
  year={2020},
  publisher={Elsevier}
}

@article{zhang2016latent,
  title={Latent tree analysis},
  author={Zhang, Nevin L and Poon, Leonard KM},
  journal={AAAI}, 
  pages= {4891--4898},
  year={2017}
}

@article{chen2017latent,
  title={Latent tree models for hierarchical topic detection},
  author={Chen, Peixian and Zhang, Nevin L and Liu, Tengfei and Poon, Leonard KM and Chen, Zhourong and Khawar, Farhan},
  journal={Artificial Intelligence},
  volume={250},
  pages={105--124},
  year={2017},
  publisher={Elsevier}
}


@inproceedings{dhurandhar2018explanations,
  title={Explanations based on the missing: Towards contrastive explanations with pertinent negatives},
  author={Dhurandhar, Amit and Chen, Pin-Yu and Luss, Ronny and Tu, Chun-Chen and Ting, Paishun and Shanmugam, Karthikeyan and Das, Payel},
  booktitle={Advances in Neural Information Processing Systems},
  pages={592--603},
  year={2018}
}

@article{hase2020evaluating,
  title={Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?},
  author={Hase, Peter and Bansal, Mohit},
  journal={arXiv preprint arXiv:2005.01831},
  year={2020}
}


@article{samek2016evaluating,
  title={Evaluating the visualization of what a deep neural network has learned},
  author={Samek, Wojciech and Binder, Alexander and Montavon, Gr{\'e}goire and Lapuschkin, Sebastian and M{\"u}ller, Klaus-Robert},
  journal={IEEE transactions on neural networks and learning systems},
  volume={28},
  number={11},
  pages={2660--2673},
  year={2016},
  publisher={IEEE}
}


@inproceedings{ghorbani2019towards,
  title={Towards automatic concept-based explanations},
  author={Ghorbani, Amirata and Wexler, James and Zou, James Y and Kim, Been},
  booktitle={Advances in Neural Information Processing Systems},
  pages={9277--9286},
  year={2019}
}


@article{liu2019generative,
  title={Generative counterfactual introspection for explainable deep learning},
  author={Liu, Shusen and Kailkhura, Bhavya and Loveland, Donald and Han, Yong},
  journal={GlobalSIP},
  pages={1--5},
  year={2019}
}

@article{wachter2017counterfactual,
  title={Counterfactual explanations without opening the black box: Automated decisions and the {GDPR}},
  author={Wachter, Sandra and Mittelstadt, Brent and Russell, Chris},
  journal={Harv. JL \& Tech.},
  volume={31},
  pages={841},
  year={2017},
  publisher={HeinOnline}
}

@inproceedings{kim2016examples,
  title={Examples are not enough, learn to criticize! criticism for interpretability},
  author={Kim, Been and Khanna, Rajiv and Koyejo, Oluwasanmi O},
  booktitle={Advances in neural information processing systems},
  pages={2280--2288},
  year={2016}
}




@inproceedings{kim2018interpretability,
  title={Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)},
  author={Kim, Been and Wattenberg, Martin and Gilmer, Justin and Cai, Carrie and Wexler, James and Viegas, Fernanda and others},
  booktitle={International conference on machine learning},
  pages={2668--2677},
  year={2018}
}


@inproceedings{mohseni2021quantitative,
  title={Quantitative evaluation of machine learning explanations: A human-grounded benchmark},
  author={Mohseni, Sina and Block, Jeremy E and Ragan, Eric},
  booktitle={26th International Conference on Intelligent User Interfaces},
  pages={22--31},
  year={2021}
}

@inproceedings{lundberg2017unified,
  title={A unified approach to interpreting model predictions},
  author={Lundberg, Scott M and Lee, Su-In},
  booktitle={Advances in neural information processing systems},
  pages={4765--4774},
  year={2017}
}

@article{nunes2017systematic,
  title={A systematic review and taxonomy of explanations in decision support and recommender systems},
  author={Nunes, Ingrid and Jannach, Dietmar},
  journal={User Modeling and User-Adapted Interaction},
  volume={27},
  number={3},
  pages={393--444},
  year={2017},
  publisher={Springer}
}

@inproceedings{tintarev2007survey,
  title={A survey of explanations in recommender systems},
  author={Tintarev, Nava and Masthoff, Judith},
  booktitle={2007 IEEE 23rd international conference on data engineering workshop},
  pages={801--810},
  year={2007},
  organization={IEEE}
}

@article{tjoa2020survey,
  title={A survey on explainable artificial intelligence (xai): Toward medical xai},
  author={Tjoa, Erico and Guan, Cuntai},
  journal={IEEE transactions on neural networks and learning systems},
  volume={32},
  number={11},
  pages={4793--4813},
  year={2020},
  publisher={IEEE}
}

@inproceedings{nguyen2018comparing,
  title={Comparing automatic and human evaluation of local explanations for text classification},
  author={Nguyen, Dong},
  booktitle={Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  pages={1069--1078},
  year={2018}
}

@article{zhang2019dissonance,
  title={Dissonance between human and machine understanding},
  author={Zhang, Zijian and Singh, Jaspreet and Gadiraju, Ujwal and Anand, Avishek},
  journal={Proceedings of the ACM on Human-Computer Interaction},
  volume={3},
  number={CSCW},
  pages={1--23},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@article{chromik2020taxonomy,
  title={A Taxonomy for Human Subject Evaluation of Black-Box Explanations in XAI.},
  author={Chromik, Michael and Schuessler, Martin},
  journal={Exss-atec@ iui},
  volume={94},
  year={2020}
}

@article{yang2019evaluating,
  title={Evaluating explanation without ground truth in interpretable machine learning},
  author={Yang, Fan and Du, Mengnan and Hu, Xia},
  journal={arXiv preprint arXiv:1907.06831},
  year={2019}
}

@article{lage2019evaluation,
  title={An evaluation of the human-interpretability of explanation},
  author={Lage, Isaac and Chen, Emily and He, Jeffrey and Narayanan, Menaka and Kim, Been and Gershman, Sam and Doshi-Velez, Finale},
  journal={arXiv preprint arXiv:1902.00006},
  year={2019}
}




@inproceedings{selvaraju2017grad,
  title={Grad-cam: Visual explanations from deep networks via gradient-based localization},
  author={Selvaraju, Ramprasaath R and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={618--626},
  year={2017}
}


@InProceedings{shrikumar2017learning,
  title =    {Learning Important Features Through Propagating Activation Differences},
  author =       {Avanti Shrikumar and Peyton Greenside and Anshul Kundaje},
  booktitle =    {Proceedings of the 34th International Conference on Machine Learning},
  pages =    {3145--3153},
  year =   {2017}
  }


@article{bach2015pixel,
  title={On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation},
  author={Bach, Sebastian and Binder, Alexander and Montavon, Gr{\'e}goire and Klauschen, Frederick and M{\"u}ller, Klaus-Robert and Samek, Wojciech},
  journal={PloS one},
  volume={10},
  number={7},
  pages={e0130140},
  year={2015},
  publisher={Public Library of Science}
}



@InProceedings{sundararajan2017axiomatic,
  title={Axiomatic attribution for deep networks},
  author={Sundararajan, Mukund and Taly, Ankur and Yan, Qiqi},
  booktitle =    {Proceedings of the 34th International Conference on Machine Learning},
  pages =    {3319--3328},
  year =   {2017}
  }



@article{smilkov2017smoothgrad,
  title={SmoothGrad: removing noise by adding noise},
  author={Daniel Smilkov and Nikhil Thorat and Been Kim and F. Vi{\'e}gas and M. Wattenberg},
  journal={ArXiv},
  year={2017},
  volume={abs/1706.03825}
}



@InProceedings{springenberg2014striving,
  author       = "J.T. Springenberg and A. Dosovitskiy and T. Brox and M. Riedmiller",
  title        = "Striving for Simplicity: The All Convolutional Net",
  booktitle    = "ICLR (workshop track)",
  year         = "2015"
}


@article{simonyan2013deep,
  title={Deep inside convolutional networks: Visualising image classification models and saliency maps},
  author={Simonyan, Karen and Vedaldi, Andrea and Zisserman, Andrew},
  year={2014},
  journal={{ICLR}}
}


@inproceedings{zeiler2014visualizing,
  title={Visualizing and understanding convolutional networks},
  author={Zeiler, Matthew D and Fergus, Rob},
  booktitle={European conference on computer vision},
  pages={818--833},
  year={2014},
  organization={Springer}
}






@inproceedings{li2017not,
  title={Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade},
  author={Li, Xiaoxiao and Liu, Ziwei and Luo, Ping and Change Loy, Chen and Tang, Xiaoou},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3193--3202},
  year={2017}
}

@inproceedings{nie2019difficulty,
  title={Difficulty-aware attention network with confidence learning for medical image segmentation},
  author={Nie, Dong and Wang, Li and Xiang, Lei and Zhou, Sihang and Adeli, Ehsan and Shen, Dinggang},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  pages={1085--1092},
  year={2019}
}


@article{scheidegger2020efficient,
  title={Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy},
  author={Scheidegger, Florian and Istrate, Roxana and Mariani, Giovanni and Benini, Luca and Bekas, Costas and Malossi, Cristiano},
  journal={The Visual Computer},
  pages={1--18},
  year={2020},
  publisher={Springer}
}



@inproceedings{tudor2016hard,
  title={How hard can it be? Estimating the difficulty of visual search in an image},
  author={Tudor Ionescu, Radu and Alexe, Bogdan and Leordeanu, Marius and Popescu, Marius and Papadopoulos, Dim P and Ferrari, Vittorio},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2157--2166},
  year={2016}
}


@article{yu2020difficulty,
  title={Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling},
  author={Yu, Shuang and Zhou, Hong-Yu and Ma, Kai and Bian, Cheng and Chu, Chunyan and Liu, Hanruo and Zheng, Yefeng},
  journal={arXiv preprint arXiv:2007.14848},
  year={2020}
}


@article{beyer2020we,
  title={Are we done with ImageNet?},
  author={Beyer, Lucas and H{\'e}naff, Olivier J and Kolesnikov, Alexander and Zhai, Xiaohua and Oord, A{\"a}ron van den},
  journal={arXiv preprint arXiv:2006.07159},
  year={2020}
}


@article{tsipras2020imagenet,
  title={From ImageNet to Image Classification: Contextualizing Progress on Benchmarks},
  author={Tsipras, Dimitris and Santurkar, Shibani and Engstrom, Logan and Ilyas, Andrew and Madry, Aleksander},
  journal={arXiv preprint arXiv:2005.11295},
  year={2020}
}


@article{wan2020nbdt,
  title={NBDT: Neural-Backed Decision Trees},
  author={Wan, Alvin and Dunlap, Lisa and Ho, Daniel and Yin, Jihan and Lee, Scott and Jin, Henry and Petryk, Suzanne and Bargal, Sarah Adel and Gonzalez, Joseph E},
  journal={arXiv preprint arXiv:2004.00221},
  year={2020}
}


@inproceedings{murthy2016deep,
  title={Deep decision network for multi-class image classification},
  author={Murthy, Venkatesh N and Singh, Vivek and Chen, Terrence and Manmatha, R and Comaniciu, Dorin},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2240--2248},
  year={2016}
}


@inproceedings{ahmed2016network,
  title={Network of experts for large-scale image categorization},
  author={Ahmed, Karim and Baig, Mohammad Haris and Torresani, Lorenzo},
  booktitle={European Conference on Computer Vision},
  pages={516--532},
  year={2016},
  organization={Springer}
}




@article{shrikumar2016not,
  title={Not just a black box: Learning important features through propagating activation differences},
  author={Shrikumar, Avanti and Greenside, Peyton and Shcherbina, Anna and Kundaje, Anshul},
  journal={arXiv preprint arXiv:1605.01713},
  year={2016}
}

@article{miller2019explanation,
  title={Explanation in artificial intelligence: Insights from the social sciences},
  author={Miller, Tim},
  journal={Artificial Intelligence},
  volume={267},
  pages={1--38},
  year={2019},
  publisher={Elsevier}
}

@article{gunning2019darpa,
  title={{DARPA}'s explainable artificial intelligence program},
  author={Gunning, David and Aha, David W},
  journal={AI Magazine},
  volume={40},
  number={2},
  pages={44--58},
  year={2019},
  publisher={Association for the Advancement of Artificial Intelligence}
}




@article{lombrozo2006structure,
  title={The structure and function of explanations},
  author={Lombrozo, Tania},
  journal={Trends in cognitive sciences},
  volume={10},
  number={10},
  pages={464--470},
  year={2006},
  publisher={Elsevier}
}

@article{dzindolet2003role,
  title={The role of trust in automation reliance},
  author={Dzindolet, Mary T and Peterson, Scott A and Pomranky, Regina A and Pierce, Linda G and Beck, Hall P},
  journal={International journal of human-computer studies},
  volume={58},
  number={6},
  pages={697--718},
  year={2003},
  publisher={Elsevier}
}

@article{lombrozo2011instrumental,
  title={The instrumental value of explanations},
  author={Lombrozo, Tania},
  journal={Philosophy Compass},
  volume={6},
  number={8},
  pages={539--551},
  year={2011},
  publisher={Wiley Online Library}
}

@article{pya2015shape,
  title={Shape constrained additive models},
  author={Pya, Natalya and Wood, Simon N},
  journal={Statistics and Computing},
  volume={25},
  number={3},
  pages={543--559},
  year={2015},
  publisher={Springer}
}


@article{zhang2016latent,
  title={Latent tree analysis},
  author={Zhang, Nevin L and Poon, Leonard KM},
  journal={AAAI}, 
  pages= {4891--4898},
  year={2017}
}

@article{chen2017latent,
  title={Latent tree models for hierarchical topic detection},
  author={Chen, Peixian and Zhang, Nevin L and Liu, Tengfei and Poon, Leonard KM and Chen, Zhourong and Khawar, Farhan},
  journal={Artificial Intelligence},
  volume={250},
  pages={105--124},
  year={2017},
  publisher={Elsevier}
}


@inproceedings{dhurandhar2018explanations,
  title={Explanations based on the missing: Towards contrastive explanations with pertinent negatives},
  author={Dhurandhar, Amit and Chen, Pin-Yu and Luss, Ronny and Tu, Chun-Chen and Ting, Paishun and Shanmugam, Karthikeyan and Das, Payel},
  booktitle={Advances in Neural Information Processing Systems},
  pages={592--603},
  year={2018}
}

@article{hase2020evaluating,
  title={Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?},
  author={Hase, Peter and Bansal, Mohit},
  journal={arXiv preprint arXiv:2005.01831},
  year={2020}
}


@article{samek2016evaluating,
  title={Evaluating the visualization of what a deep neural network has learned},
  author={Samek, Wojciech and Binder, Alexander and Montavon, Gr{\'e}goire and Lapuschkin, Sebastian and M{\"u}ller, Klaus-Robert},
  journal={IEEE transactions on neural networks and learning systems},
  volume={28},
  number={11},
  pages={2660--2673},
  year={2016},
  publisher={IEEE}
}

@article{krizhevsky2012imagenet,
  title={Imagenet classification with deep convolutional neural networks},
  author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
  journal={Advances in neural information processing systems},
  volume={25},
  pages={1097--1105},
  year={2012}
}

@inproceedings{ghorbani2019towards,
  title={Towards automatic concept-based explanations},
  author={Ghorbani, Amirata and Wexler, James and Zou, James Y and Kim, Been},
  booktitle={Advances in Neural Information Processing Systems},
  pages={9277--9286},
  year={2019}
}


@article{liu2019generative,
  title={Generative counterfactual introspection for explainable deep learning},
  author={Liu, Shusen and Kailkhura, Bhavya and Loveland, Donald and Han, Yong},
  journal={arXiv preprint arXiv:1907.03077},
  year={2019}
}

@article{wachter2017counterfactual,
  title={Counterfactual explanations without opening the black box: Automated decisions and the GDPR},
  author={Wachter, Sandra and Mittelstadt, Brent and Russell, Chris},
  journal={Harv. JL \& Tech.},
  volume={31},
  pages={841},
  year={2017},
  publisher={HeinOnline}
}

@inproceedings{kim2016examples,
  title={Examples are not enough, learn to criticize! criticism for interpretability},
  author={Kim, Been and Khanna, Rajiv and Koyejo, Oluwasanmi O},
  booktitle={Advances in neural information processing systems},
  pages={2280--2288},
  year={2016}
}




@inproceedings{kim2018interpretability,
  title={Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)},
  author={Kim, Been and Wattenberg, Martin and Gilmer, Justin and Cai, Carrie and Wexler, James and Viegas, Fernanda and others},
  booktitle={International conference on machine learning},
  pages={2668--2677},
  year={2018}
}

@book{scheaffer2011elementary,
  title={Elementary survey sampling},
  author={Scheaffer, Richard L and Mendenhall III, William and Ott, R Lyman and Gerow, Kenneth G},
  year={2011},
  publisher={Cengage Learning}
}

@inproceedings{lundberg2017unified,
  title={A unified approach to interpreting model predictions},
  author={Lundberg, Scott M and Lee, Su-In},
  booktitle={Advances in neural information processing systems},
  pages={4765--4774},
  year={2017}
}

@inproceedings{wang2020score,
  title={Score-CAM: Score-weighted visual explanations for convolutional neural networks},
  author={Wang, Haofan and Wang, Zifan and Du, Mengnan and Yang, Fan and Zhang, Zijian and Ding, Sirui and Mardziel, Piotr and Hu, Xia},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops},
  pages={24--25},
  year={2020}
}

@inproceedings{nguyen2015deep,
  title={Deep neural networks are easily fooled: High confidence predictions for unrecognizable images},
  author={Nguyen, Anh and Yosinski, Jason and Clune, Jeff},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={427--436},
  year={2015}
}

@inproceedings{alcorn2019strike,
  title={Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects},
  author={Alcorn, Michael A and Li, Qi and Gong, Zhitao and Wang, Chengfei and Mai, Long and Ku, Wei-Shinn and Nguyen, Anh},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4845--4854},
  year={2019}
}

@inproceedings{zeiler2014visualizing,
  title={Visualizing and understanding convolutional networks},
  author={Zeiler, Matthew D and Fergus, Rob},
  booktitle={European conference on computer vision},
  pages={818--833},
  year={2014},
  organization={Springer}
}

@inproceedings{kapishnikov2019xrai,
  title={Xrai: Better attributions through regions},
  author={Kapishnikov, Andrei and Bolukbasi, Tolga and Vi{\'e}gas, Fernanda and Terry, Michael},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4948--4957},
  year={2019}
}

@inproceedings{girshick2014rich,
  title={Rich feature hierarchies for accurate object detection and semantic segmentation},
  author={Girshick, Ross and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={580--587},
  year={2014}
}

@inproceedings{fang2015captions,
  title={From captions to visual concepts and back},
  author={Fang, Hao and Gupta, Saurabh and Iandola, Forrest and Srivastava, Rupesh K and Deng, Li and Doll{\'a}r, Piotr and Gao, Jianfeng and He, Xiaodong and Mitchell, Margaret and Platt, John C and others},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1473--1482},
  year={2015}
}

@inproceedings{vinyals2015show,
  title={Show and tell: A neural image caption generator},
  author={Vinyals, Oriol and Toshev, Alexander and Bengio, Samy and Erhan, Dumitru},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3156--3164},
  year={2015}
}

@inproceedings{long2015fully,
  title={Fully convolutional networks for semantic segmentation},
  author={Long, Jonathan and Shelhamer, Evan and Darrell, Trevor},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3431--3440},
  year={2015}
}

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@article{kim2021machine,
  title={Machine Learning Techniques for Accountability},
  author={Kim, Been and Doshi-Velez, Finale},
  journal={AI Magazine},
  volume={42},
  number={1},
  pages={47--52},
  year={2021},
  publisher={Association for the Advancement of Artificial Intelligence}
}

@inproceedings{rebuffi2020there,
  title={There and back again: Revisiting backpropagation saliency methods},
  author={Rebuffi, Sylvestre-Alvise and Fong, Ruth and Ji, Xu and Vedaldi, Andrea},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8839--8848},
  year={2020}
}

@article{rudin2019stop,
  title={Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead},
  author={Rudin, Cynthia},
  journal={Nature Machine Intelligence},
  volume={1},
  number={5},
  pages={206--215},
  year={2019},
  publisher={Nature Publishing Group}
}

@article{petsiuk2018rise,
  title={Rise: Randomized input sampling for explanation of black-box models},
  author={Petsiuk, Vitali and Das, Abir and Saenko, Kate},
  journal={arXiv preprint arXiv:1806.07421},
  year={2018}
}


@article{doshi2017towards,
  title={Towards a rigorous science of interpretable machine learning},
  author={Doshi-Velez, Finale and Kim, Been},
  journal={arXiv preprint arXiv:1702.08608},
  year={2017}
}

@incollection{NIPS2017_7062,
title = {A Unified Approach to Interpreting Model Predictions},
author = {Lundberg, Scott M and Lee, Su-In},
booktitle = {Advances in Neural Information Processing Systems 30},
editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {4765--4774},
year = {2017},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf}
}

@inproceedings{ribeiro2016should,
  title={" Why should I trust you?" Explaining the predictions of any classifier},
  author={Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
  booktitle={Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining},
  pages={1135--1144},
  year={2016}
}

@inproceedings{chattopadhay2018grad,
  title={Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks},
  author={Chattopadhay, Aditya and Sarkar, Anirban and Howlader, Prantik and Balasubramanian, Vineeth N},
  booktitle={2018 IEEE winter conference on applications of computer vision (WACV)},
  pages={839--847},
  year={2018},
  organization={IEEE}
}

@inproceedings{selvaraju2017grad,
  title={Grad-cam: Visual explanations from deep networks via gradient-based localization},
  author={Selvaraju, Ramprasaath R and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={618--626},
  year={2017}
}

@article{shrikumar2017learning,
  title={Learning important features through propagating activation differences},
  author={Shrikumar, Avanti and Greenside, Peyton and Kundaje, Anshul},
  journal={arXiv preprint arXiv:1704.02685},
  year={2017}
}

@article{bach2015pixel,
  title={On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation},
  author={Bach, Sebastian and Binder, Alexander and Montavon, Gr{\'e}goire and Klauschen, Frederick and M{\"u}ller, Klaus-Robert and Samek, Wojciech},
  journal={PloS one},
  volume={10},
  number={7},
  pages={e0130140},
  year={2015},
  publisher={Public Library of Science}
}

@inproceedings{bussone2015role,
  title={The role of explanations on trust and reliance in clinical decision support systems},
  author={Bussone, Adrian and Stumpf, Simone and O'Sullivan, Dympna},

  booktitle={2015 international conference on healthcare informatics},
  pages={160--169},
  year={2015},
  organization={IEEE}
}

@inproceedings{adebayo2022post,
  title={Post hoc explanations may be ineffective for detecting unknown spurious correlation},
  author={Adebayo, Julius and Muelly, Michael and Abelson, Harold and Kim, Been},
  booktitle={International Conference on Learning Representations},
  year={2022}
}

@inproceedings{chromik2021think,
  title={I think i get your point, AI! the illusion of explanatory depth in explainable AI},
  author={Chromik, Michael and Eiband, Malin and Buchner, Felicitas and Kr{\"u}ger, Adrian and Butz, Andreas},
  booktitle={26th International Conference on Intelligent User Interfaces},
  pages={307--317},
  year={2021}
}

@article{sundararajan2017axiomatic,
  title={Axiomatic attribution for deep networks},
  author={Sundararajan, Mukund and Taly, Ankur and Yan, Qiqi},
  journal={arXiv preprint arXiv:1703.01365},
  year={2017}
}

@article{smilkov2017smoothgrad,
  title={Smoothgrad: removing noise by adding noise},
  author={Smilkov, Daniel and Thorat, Nikhil and Kim, Been and Vi{\'e}gas, Fernanda and Wattenberg, Martin},
  journal={arXiv preprint arXiv:1706.03825},
  year={2017}
}

@article{springenberg2014striving,
  title={Striving for simplicity: The all convolutional net},
  author={Springenberg, Jost Tobias and Dosovitskiy, Alexey and Brox, Thomas and Riedmiller, Martin},
  journal={arXiv preprint arXiv:1412.6806},
  year={2014}
}

@article{simonyan2013deep,
  title={Deep inside convolutional networks: Visualising image classification models and saliency maps},
  author={Simonyan, Karen and Vedaldi, Andrea and Zisserman, Andrew},
  journal={arXiv preprint arXiv:1312.6034},
  year={2013}
}

@inproceedings{zeiler2014visualizing,
  title={Visualizing and understanding convolutional networks},
  author={Zeiler, Matthew D and Fergus, Rob},
  booktitle={European conference on computer vision},
  pages={818--833},
  year={2014},
  organization={Springer}
}


@article{zhang2018top,
  title={Top-down neural attention by excitation backprop},
  author={Zhang, Jianming and Bargal, Sarah Adel and Lin, Zhe and Brandt, Jonathan and Shen, Xiaohui and Sclaroff, Stan},
  journal={International Journal of Computer Vision},
  volume={126},
  number={10},
  pages={1084--1102},
  year={2018},
  publisher={Springer}
}



@inproceedings{li2017not,
  title={Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade},
  author={Li, Xiaoxiao and Liu, Ziwei and Luo, Ping and Change Loy, Chen and Tang, Xiaoou},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3193--3202},
  year={2017}
}

@inproceedings{nie2019difficulty,
  title={Difficulty-aware attention network with confidence learning for medical image segmentation},
  author={Nie, Dong and Wang, Li and Xiang, Lei and Zhou, Sihang and Adeli, Ehsan and Shen, Dinggang},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  pages={1085--1092},
  year={2019}
}


@article{scheidegger2020efficient,
  title={Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy},
  author={Scheidegger, Florian and Istrate, Roxana and Mariani, Giovanni and Benini, Luca and Bekas, Costas and Malossi, Cristiano},
  journal={The Visual Computer},
  pages={1--18},
  year={2020},
  publisher={Springer}
}



@inproceedings{tudor2016hard,
  title={How hard can it be? Estimating the difficulty of visual search in an image},
  author={Tudor Ionescu, Radu and Alexe, Bogdan and Leordeanu, Marius and Popescu, Marius and Papadopoulos, Dim P and Ferrari, Vittorio},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2157--2166},
  year={2016}
}


@article{yu2020difficulty,
  title={Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling},
  author={Yu, Shuang and Zhou, Hong-Yu and Ma, Kai and Bian, Cheng and Chu, Chunyan and Liu, Hanruo and Zheng, Yefeng},
  journal={arXiv preprint arXiv:2007.14848},
  year={2020}
}


@article{beyer2020we,
  title={Are we done with ImageNet?},
  author={Beyer, Lucas and H{\'e}naff, Olivier J and Kolesnikov, Alexander and Zhai, Xiaohua and Oord, A{\"a}ron van den},
  journal={arXiv preprint arXiv:2006.07159},
  year={2020}
}

@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

@article{rudin2019stop,
  title={Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead},
  author={Rudin, Cynthia},
  journal={Nature Machine Intelligence},
  volume={1},
  number={5},
  pages={206--215},
  year={2019},
  publisher={Nature Publishing Group}
}

@inproceedings{deng2009imagenet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={2009 IEEE conference on computer vision and pattern recognition},
  pages={248--255},
  year={2009},
  organization={Ieee}
}

@article{tsipras2020imagenet,
  title={From ImageNet to Image Classification: Contextualizing Progress on Benchmarks},
  author={Tsipras, Dimitris and Santurkar, Shibani and Engstrom, Logan and Ilyas, Andrew and Madry, Aleksander},
  journal={arXiv preprint arXiv:2005.11295},
  year={2020}
}


@article{wan2020nbdt,
  title={NBDT: Neural-Backed Decision Trees},
  author={Wan, Alvin and Dunlap, Lisa and Ho, Daniel and Yin, Jihan and Lee, Scott and Jin, Henry and Petryk, Suzanne and Bargal, Sarah Adel and Gonzalez, Joseph E},
  journal={arXiv preprint arXiv:2004.00221},
  year={2020}
}


@inproceedings{murthy2016deep,
  title={Deep decision network for multi-class image classification},
  author={Murthy, Venkatesh N and Singh, Vivek and Chen, Terrence and Manmatha, R and Comaniciu, Dorin},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2240--2248},
  year={2016}
}


@inproceedings{ahmed2016network,
  title={Network of experts for large-scale image categorization},
  author={Ahmed, Karim and Baig, Mohammad Haris and Torresani, Lorenzo},
  booktitle={European Conference on Computer Vision},
  pages={516--532},
  year={2016},
  organization={Springer}
}




@article{shrikumar2016not,
  title={Not just a black box: Learning important features through propagating activation differences},
  author={Shrikumar, Avanti and Greenside, Peyton and Shcherbina, Anna and Kundaje, Anshul},
  journal={arXiv preprint arXiv:1605.01713},
  year={2016}
}

@article{miller2019explanation,
  title={Explanation in artificial intelligence: Insights from the social sciences},
  author={Miller, Tim},
  journal={Artificial Intelligence},
  volume={267},
  pages={1--38},
  year={2019},
  publisher={Elsevier}
}

@article{lipton2018mythos,
  title={The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.},
  author={Lipton, Zachary C},
  journal={Queue},
  volume={16},
  number={3},
  pages={31--57},
  year={2018},
  publisher={ACM New York, NY, USA}
}

@article{dzindolet2003role,
  title={The role of trust in automation reliance},
  author={Dzindolet, Mary T and Peterson, Scott A and Pomranky, Regina A and Pierce, Linda G and Beck, Hall P},
  journal={International journal of human-computer studies},
  volume={58},
  number={6},
  pages={697--718},
  year={2003},
  publisher={Elsevier}
}

@inproceedings{shrikumar2017learning,
  title={Learning important features through propagating activation differences},
  author={Shrikumar, Avanti and Greenside, Peyton and Kundaje, Anshul},
  booktitle={International Conference on Machine Learning},
  pages={3145--3153},
  year={2017},
  organization={PMLR}
}


@article{gunning2019darpa,
  title={{DARPA}'s explainable artificial intelligence program},
  author={Gunning, David and Aha, David W},
  journal={AI Magazine},
  volume={40},
  number={2},
  pages={44--58},
  year={2019},
  publisher={Association for the Advancement of Artificial Intelligence}
}


