@article{alpsancar2024explanation,
  title={Explanation needs and ethical demands: unpacking the instrumental value of XAI},
  author={Alpsancar, Suzana and Buhl, Heike M and Matzner, Tobias and Scharlau, Ingrid},
  journal={AI and Ethics},
  pages={1--19},
  year={2024},
  publisher={Springer}
}

@misc{iranian_churn_563,
  title        = {{Iranian Churn}},
  year         = {2020},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: https://doi.org/10.24432/C5JW3Z}
}

@misc{wine_quality_186,
  author       = {Cortez, Paulo and Cerdeira, A. and Almeida, F. and Matos, T. and Reis, J.},
  title        = {Wine Quality},
  year         = {2009},
  howpublished = {UCI Machine Learning Repository},
  note         = {DOI: https://doi.org/10.24432/C56S3T}
}

@misc{solar_flare_89,
  title        = {{Solar Flare}},
  year         = {1989},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: https://doi.org/10.24432/C5530G}
}

@inproceedings{bansal2020sam,
  title={Sam: The sensitivity of attribution methods to hyperparameters},
  author={Bansal, Naman and Agarwal, Chirag and Nguyen, Anh},
  booktitle={Proceedings of the ieee/cvf conference on computer vision and pattern recognition},
  pages={8673--8683},
  year={2020}
}

@article{alvarez2018towards,
  title={Towards robust interpretability with self-explaining neural networks},
  author={Alvarez Melis, David and Jaakkola, Tommi},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

@article{nguyen2020quantitative,
  title={On quantitative aspects of model interpretability},
  author={Nguyen, An-phi and Mart{\'\i}nez, Mar{\'\i}a Rodr{\'\i}guez},
  journal={arXiv preprint arXiv:2007.07584},
  year={2020}
}

@article{luss2019generating,
  title={Generating contrastive explanations with monotonic attribute functions},
  author={Luss, Ronny and Chen, Pin-Yu and Dhurandhar, Amit and Sattigeri, Prasanna and Shanmugam, Karthik and Tu, Chun-Chen},
  journal={arXiv preprint arXiv:1905.12698},
  volume={3},
  year={2019}
}

@article{deyoung2019eraser,
  title={ERASER: A benchmark to evaluate rationalized NLP models},
  author={DeYoung, Jay and Jain, Sarthak and Rajani, Nazneen Fatema and Lehman, Eric and Xiong, Caiming and Socher, Richard and Wallace, Byron C},
  journal={arXiv preprint arXiv:1911.03429},
  year={2019}
}

@article{yeh2019fidelity,
  title={On the (in) fidelity and sensitivity of explanations},
  author={Yeh, Chih-Kuan and Hsieh, Cheng-Yu and Suggala, Arun and Inouye, David I and Ravikumar, Pradeep K},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{balagopalan2022road,
  title={The road to explainability is paved with bias: Measuring the fairness of explanations},
  author={Balagopalan, Aparna and Zhang, Haoran and Hamidieh, Kimia and Hartvigsen, Thomas and Rudzicz, Frank and Ghassemi, Marzyeh},
  booktitle={Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency},
  pages={1194--1206},
  year={2022}
}

@inproceedings{tan2023robust,
  title={Robust explanation for free or at the cost of faithfulness},
  author={Tan, Zeren and Tian, Yang},
  booktitle={International Conference on Machine Learning},
  pages={33534--33562},
  year={2023},
}

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

@article{wang2020smoothed,
  title={Smoothed geometry for robust attribution},
  author={Wang, Zifan and Wang, Haofan and Ramkumar, Shakul and Mardziel, Piotr and Fredrikson, Matt and Datta, Anupam},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={13623--13634},
  year={2020}
}

@article{baehrens2010explain,
  title={How to explain individual classification decisions},
  author={Baehrens, David and Schroeter, Timon and Harmeling, Stefan and Kawanabe, Motoaki and Hansen, Katja and M{\"u}ller, Klaus-Robert},
  journal={The Journal of Machine Learning Research},
  volume={11},
  pages={1803--1831},
  year={2010},
  publisher={JMLR. org}
}

@article{schmidt2005least,
  title={Least squares optimization with L1-norm regularization},
  author={Schmidt, Mark},
  journal={CS542B Project Report},
  volume={504},
  number={2005},
  pages={195--221},
  year={2005},
  publisher={University of British Columbia}
}


@InProceedings{pmlr-v119-chalasani20a,
  title = 	 {Concise Explanations of Neural Networks using Adversarial Training},
  author =       {Chalasani, Prasad and Chen, Jiefeng and Chowdhury, Amrita Roy and Wu, Xi and Jha, Somesh},
  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
  pages = 	 {1383--1391},
  year = 	 {2020},
}

@article{rudin_stop_2019,
	title = {Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead},
	volume = {1},
	issn = {2522-5839},
	url = {https://www.nature.com/articles/s42256-019-0048-x},
	doi = {10.1038/s42256-019-0048-x},
	language = {en},
	number = {5},
	urldate = {2024-08-27},
	journal = {Nature Machine Intelligence},
	author = {Rudin, Cynthia},
	month = may,
	year = {2019},
	pages = {206--215},
}

@inproceedings{ijcai2020p417,
  title     = {Evaluating and Aggregating Feature-based Model Explanations},
  author    = {Bhatt, Umang and Weller, Adrian and Moura, José M. F.},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  pages     = {3016--3022},
  year      = {2020},

}
@misc{levine2019certifiablyrobustinterpretationdeep,
      title={Certifiably Robust Interpretation in Deep Learning}, 
      author={Alexander Levine and Sahil Singla and Soheil Feizi},
      year={2019},
      eprint={1905.12105},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1905.12105}, 
}


@misc{garreau2022lookingdeepertabularlime,
      title={Looking Deeper into Tabular LIME}, 
      author={Damien Garreau and Ulrike von Luxburg},
      year={2022},
      eprint={2008.11092},
      archivePrefix={arXiv},
      primaryClass={stat.ML},
      url={https://arxiv.org/abs/2008.11092}, 
}

@article{decker2024provably,
  title={Provably Better Explanations with Optimized Aggregation of Feature Attributions},
  author={Decker, Thomas and Bhattarai, Ananta R and Gu, Jindong and Tresp, Volker and Buettner, Florian},
  journal={arXiv preprint arXiv:2406.05090},
  year={2024}
}

@article{chen2022makes,
  title={What makes a good explanation?: A harmonized view of properties of explanations},
  author={Chen, Zixi and Subhash, Varshini and Havasi, Marton and Pan, Weiwei and Doshi-Velez, Finale},
  journal={arXiv preprint arXiv:2211.05667},
  year={2022}
}

@article{nofshin2024sim2real,
  title={A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning},
  author={Nofshin, Eura and Brown, Esther and Lim, Brian and Pan, Weiwei and Doshi-Velez, Finale},
  journal={arXiv preprint arXiv:2406.00116},
  year={2024}
}

@inproceedings{liao2022connecting,
  title={Connecting algorithmic research and usage contexts: a perspective of contextualized evaluation for explainable AI},
  author={Liao, Q Vera and Zhang, Yunfeng and Luss, Ronny and Doshi-Velez, Finale and Dhurandhar, Amit},
  booktitle={Proceedings of the AAAI Conference on Human Computation and Crowdsourcing},
  volume={10},
  pages={147--159},
  year={2022}
}

@inproceedings{10.1145/3411764.3445315,
author = {Poursabzi-Sangdeh, Forough and Goldstein, Daniel G and Hofman, Jake M and Wortman Vaughan, Jennifer Wortman and Wallach, Hanna},
title = {Manipulating and Measuring Model Interpretability},
year = {2021},
booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
numpages = {52},
}


@misc{nofshin_sim2real_2024-1,
	title = {A {Sim2Real} {Approach} for {Identifying} {Task}-{Relevant} {Properties} in {Interpretable} {Machine} {Learning}},
	publisher = {arXiv},
	author = {Nofshin, Eura and Brown, Esther and Lim, Brian and Pan, Weiwei and Doshi-Velez, Finale},
	year = {2024},

}

@inproceedings{pushkarna_data_2022,
	address = {Seoul Republic of Korea},
	title = {Data {Cards}: {Purposeful} and {Transparent} {Dataset} {Documentation} for {Responsible} {AI}},
	isbn = {978-1-4503-9352-2},
	shorttitle = {Data {Cards}},
	url = {https://dl.acm.org/doi/10.1145/3531146.3533231},
	doi = {10.1145/3531146.3533231},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {2022 {ACM} {Conference} on {Fairness}, {Accountability}, and {Transparency}},
	publisher = {ACM},
	author = {Pushkarna, Mahima and Zaldivar, Andrew and Kjartansson, Oddur},
	month = jun,
	year = {2022},
	pages = {1776--1826},
}

@inproceedings{sokol2020explainability,
  title={Explainability fact sheets: A framework for systematic assessment of explainable approaches},
  author={Sokol, Kacper and Flach, Peter},
  booktitle={Proceedings of the 2020 conference on fairness, accountability, and transparency},
  pages={56--67},
  year={2020}
}

@inproceedings{mitchell2019model,
  title={Model cards for model reporting},
  author={Mitchell, Margaret and Wu, Simone and Zaldivar, Andrew and Barnes, Parker and Vasserman, Lucy and Hutchinson, Ben and Spitzer, Elena and Raji, Inioluwa Deborah and Gebru, Timnit},
  booktitle={Proceedings of the conference on fairness, accountability, and transparency},
  pages={220--229},
  year={2019}
}

@inproceedings{nguyen2018comparing,
  title={Comparing automatic and human evaluation of local explanations for text classification},
  author={Nguyen, Dong},
  booktitle={16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  pages={1069--1078},
  year={2018},
  organization={Association for Computational Linguistics}
}

@misc{methnani_clash_2023,
	title = {Clash of the {Explainers}: {Argumentation} for {Context}-{Appropriate} {Explanations}},
	shorttitle = {Clash of the {Explainers}},
	url = {http://arxiv.org/abs/2312.07635},
	abstract = {Understanding when and why to apply any given eXplainable Artificial Intelligence (XAI) technique is not a straightforward task. There is no single approach that is best suited for a given context. This paper aims to address the challenge of selecting the most appropriate explainer given the context in which an explanation is required. For AI explainability to be effective, explanations and how they are presented needs to be oriented towards the stakeholder receiving the explanation. If—in general—no single explanation technique surpasses the rest, then reasoning over the available methods is required in order to select one that is context-appropriate. Due to the transparency they afford, we propose employing argumentation techniques to reach an agreement over the most suitable explainers from a given set of possible explainers.},
	language = {en},
	urldate = {2024-05-30},
	publisher = {arXiv},
	author = {Methnani, Leila and Dignum, Virginia and Theodorou, Andreas},
	month = dec,
	year = {2023},
	note = {arXiv:2312.07635 [cs]},
	keywords = {Computer Science - Artificial Intelligence},
}
@article{DBLP:journals/corr/abs-2312-07635,
  author       = {Leila Methnani and
                  Virginia Dignum and
                  Andreas Theodorou},
  title        = {Clash of the Explainers: Argumentation for Context-Appropriate Explanations},
  journal      = {CoRR},
  volume       = {abs/2312.07635},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2312.07635},
  doi          = {10.48550/ARXIV.2312.07635},
  eprinttype    = {arXiv},
  eprint       = {2312.07635},
  timestamp    = {Thu, 04 Jan 2024 17:55:51 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2312-07635.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@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}
}

@misc{garreau2022lookingdeepertabularlime,
      title={}, 
      author={Damien Garreau and Ulrike von Luxburg},
      year={2022},
      eprint={2008.11092},
      archivePrefix={arXiv},
      primaryClass={stat.ML},
      url={https://arxiv.org/abs/2008.11092}, 
}


@inproceedings{agarwal2021towards,
  title={Towards the unification and robustness of perturbation and gradient based explanations},
  author={Agarwal, Sushant and Jabbari, Shahin and Agarwal, Chirag and Upadhyay, Sohini and Wu, Steven and Lakkaraju, Himabindu},
  booktitle={International conference on machine learning},
  pages={110--119},
  year={2021},
}

@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}
}

@inproceedings{slack2020fooling,
  title={Fooling lime and shap: Adversarial attacks on post hoc explanation methods},
  author={Slack, Dylan and Hilgard, Sophie and Jia, Emily and Singh, Sameer and Lakkaraju, Himabindu},
  booktitle={Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
  pages={180--186},
  year={2020}
}

@inproceedings{ghorbani2019interpretation,
  title={Interpretation of neural networks is fragile},
  author={Ghorbani, Amirata and Abid, Abubakar and Zou, James},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={33},
  number={01},
  pages={3681--3688},
  year={2019}
}

@inproceedings{cugny2022autoxai,
  title={Autoxai: A framework to automatically select the most adapted xai solution},
  author={Cugny, Robin and Aligon, Julien and Chevalier, Max and Roman Jimenez, Geoffrey and Teste, Olivier},
  booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
  pages={315--324},
  year={2022}
}

@article{selvaraju2020grad,
  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},
  journal={International journal of computer vision},
  volume={128},
  pages={336--359},
  year={2020},
  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}
}@article{zhou2021evaluating,
  title={Evaluating the quality of machine learning explanations: A survey on methods and metrics},
  author={Zhou, Jianlong and Gandomi, Amir H and Chen, Fang and Holzinger, Andreas},
  journal={Electronics},
  volume={10},
  number={5},
  pages={593},
  year={2021},
  publisher={MDPI}
}
@article{zhou2021evaluating,
  title={Evaluating the quality of machine learning explanations: A survey on methods and metrics},
  author={Zhou, Jianlong and Gandomi, Amir H and Chen, Fang and Holzinger, Andreas},
  journal={Electronics},
  volume={10},
  number={5},
  pages={593},
  year={2021},
  publisher={MDPI}
}

@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}
}

@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}
}

@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}
}

@article{colin2022cannot,
  title={What i cannot predict, i do not understand: A human-centered evaluation framework for explainability methods},
  author={Colin, Julien and Fel, Thomas and Cad{\`e}ne, R{\'e}mi and Serre, Thomas},
  journal={Advances in neural information processing systems},
  volume={35},
  pages={2832--2845},
  year={2022}
}

@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{russakovsky2015imagenet,
  title={Imagenet large scale visual recognition challenge},
  author={Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and others},
  journal={International journal of computer vision},
  volume={115},
  pages={211--252},
  year={2015},
  publisher={Springer}
}

@inproceedings{dasgupta2022framework,
  title={Framework for evaluating faithfulness of local explanations},
  author={Dasgupta, Sanjoy and Frost, Nave and Moshkovitz, Michal},
  booktitle={International Conference on Machine Learning},
  pages={4794--4815},
  year={2022},
}

@inproceedings{ajalloeian2022smoothed,
  title={On smoothed explanations: Quality and robustness},
  author={Ajalloeian, Ahmad and Moosavi-Dezfooli, Seyed-Mohsen and Vlachos, Michalis and Frossard, Pascal},
  booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
  pages={15--25},
  year={2022}
}

@article{wang2024multi,
  title={Multi-objective feature attribution explanation for explainable machine learning},
  author={Wang, Ziming and Huang, Changwu and Li, Yun and Yao, Xin},
  journal={ACM Transactions on Evolutionary Learning and Optimization},
  volume={4},
  number={1},
  pages={1--32},
  year={2024},
  publisher={ACM New York, NY}
}
