{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:09:00Z","timestamp":1780765740442,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"United States Air Force Research Laboratory and United States Air Force Artificial Intelligence Accelerator","award":["FA8750-19-2-1000"],"award-info":[{"award-number":["FA8750-19-2-1000"]}]},{"name":"MIT-IBM Watson AI Lab","award":["Towards Intuitive AI"],"award-info":[{"award-number":["Towards Intuitive AI"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,27]]},"DOI":"10.1145\/3491102.3501965","type":"proceedings-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T17:09:36Z","timestamp":1651165776000},"page":"1-17","source":"Crossref","is-referenced-by-count":45,"title":["Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior"],"prefix":"10.1145","author":[{"given":"Angie","family":"Boggust","sequence":"first","affiliation":[{"name":"CSAIL, Massachusetts Institute of Technology, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Hoover","sequence":"additional","affiliation":[{"name":"IBM Research, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arvind","family":"Satyanarayan","sequence":"additional","affiliation":[{"name":"CSAIL, Massachusetts Institute of Technology, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hendrik","family":"Strobelt","sequence":"additional","affiliation":[{"name":"IBM Research, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Proceedings of the Conference on Neural Information Processing Systems (NeurIPS)","author":"Adebayo Julius","year":"2018","unstructured":"Julius Adebayo , Justin Gilmer , Michael Muelly , Ian\u00a0 J. Goodfellow , Moritz Hardt , and Been Kim . 2018 . Sanity Checks for Saliency Maps . In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) . Montr\u00e9al, Canada, 9525\u20139536. Julius Adebayo, Justin Gilmer, Michael Muelly, Ian\u00a0J. Goodfellow, Moritz Hardt, and Been Kim. 2018. Sanity Checks for Saliency Maps. In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS). Montr\u00e9al, Canada, 9525\u20139536."},{"key":"e_1_3_2_2_2_1","unstructured":"Julius Adebayo Michael Muelly Ilaria Liccardi and Been Kim. 2020. Debugging Tests for Model Explanations. arXiv preprint arXiv:2011.05429(2020).  Julius Adebayo Michael Muelly Ilaria Liccardi and Been Kim. 2020. Debugging Tests for Model Explanations. arXiv preprint arXiv:2011.05429(2020)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.354"},{"key":"e_1_3_2_2_4_1","unstructured":"Lucas Beyer Olivier\u00a0J. H\u00e9naff Alexander Kolesnikov Xiaohua Zhai and A\u00e4ron van\u00a0den Oord. 2020. Are we done with ImageNet?arxiv:2006.07159\u00a0[cs.CV]  Lucas Beyer Olivier\u00a0J. H\u00e9naff Alexander Kolesnikov Xiaohua Zhai and A\u00e4ron van\u00a0den Oord. 2020. Are we done with ImageNet?arxiv:2006.07159\u00a0[cs.CV]"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00158"},{"key":"e_1_3_2_2_6_1","volume-title":"Proceedings of the Conference on Neural Information Processing Systems (NeurIPS). Virtual Event.","author":"Carter Brandon","year":"2021","unstructured":"Brandon Carter , Siddhartha Jain , Jonas Mueller , and David Gifford . 2021 . Overinterpretation reveals image classification model pathologies . In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS). Virtual Event. Brandon Carter, Siddhartha Jain, Jonas Mueller, and David Gifford. 2021. Overinterpretation reveals image classification model pathologies. In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS). Virtual Event."},{"key":"e_1_3_2_2_7_1","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR","author":"Carter Brandon","year":"2019","unstructured":"Brandon Carter , Jonas Mueller , Siddhartha Jain , and David\u00a0 K. Gifford . 2019 . What made you do this? Understanding black-box decisions with sufficient input subsets . In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR , Naha, Japan, 567\u2013576. Brandon Carter, Jonas Mueller, Siddhartha Jain, and David\u00a0K. Gifford. 2019. What made you do this? Understanding black-box decisions with sufficient input subsets. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, Naha, Japan, 567\u2013576."},{"key":"e_1_3_2_2_8_1","volume-title":"https:\/\/doi.org\/10.23915\/distill.00015 https:\/\/distill.pub\/2019\/activation-atlas","author":"Carter Shan","year":"2019","unstructured":"Shan Carter , Zan Armstrong , Ludwig Schubert , Ian Johnson , and Chris Olah . 2019. Activation Atlas . Distill ( 2019 ). https:\/\/doi.org\/10.23915\/distill.00015 https:\/\/distill.pub\/2019\/activation-atlas . Shan Carter, Zan Armstrong, Ludwig Schubert, Ian Johnson, and Chris Olah. 2019. Activation Atlas. Distill (2019). https:\/\/doi.org\/10.23915\/distill.00015 https:\/\/distill.pub\/2019\/activation-atlas."},{"key":"e_1_3_2_2_9_1","unstructured":"Noel Codella Veronica Rotemberg Philipp Tschandl M.\u00a0Emre Celebi Stephen Dusza David Gutman Brian Helba Aadi Kalloo Konstantinos Liopyris Michael Marchetti Harald Kittler and Allan Halpern. 2019. Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC). arxiv:1902.03368\u00a0[cs.CV]  Noel Codella Veronica Rotemberg Philipp Tschandl M.\u00a0Emre Celebi Stephen Dusza David Gutman Brian Helba Aadi Kalloo Konstantinos Liopyris Michael Marchetti Harald Kittler and Allan Halpern. 2019. Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC). arxiv:1902.03368\u00a0[cs.CV]"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_2_11_1","unstructured":"Finale Doshi-Velez and Been Kim. 2017. Towards A Rigorous Science of Interpretable Machine Learning. arxiv:1702.08608\u00a0[stat.ML]  Finale Doshi-Velez and Been Kim. 2017. Towards A Rigorous Science of Interpretable Machine Learning. arxiv:1702.08608\u00a0[stat.ML]"},{"key":"e_1_3_2_2_13_1","volume-title":"Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 7639","author":"Esteva Andre","year":"2017","unstructured":"Andre Esteva , Brett Kuprel , Roberto\u00a0 A. Novoa , Justin Ko , Susan\u00a0 M. Swetter , Helen\u00a0 M. Blau , and Sebastian Thrun . 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 7639 ( 2017 ), 115\u2013118. Andre Esteva, Brett Kuprel, Roberto\u00a0A. Novoa, Justin Ko, Susan\u00a0M. Swetter, Helen\u00a0M. Blau, and Sebastian Thrun. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 7639 (2017), 115\u2013118."},{"key":"e_1_3_2_2_14_1","volume-title":"Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI)","author":"Gutman David","year":"2016","unstructured":"David Gutman , Noel C.\u00a0F. Codella , Emre Celebi , Brian Helba , Michael Marchetti , Nabin Mishra , and Allan Halpern . 2016. Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016 , hosted by the International Skin Imaging Collaboration (ISIC) . arxiv:1605.01397\u00a0[cs.CV] David Gutman, Noel C.\u00a0F. Codella, Emre Celebi, Brian Helba, Michael Marchetti, Nabin Mishra, and Allan Halpern. 2016. Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC). arxiv:1605.01397\u00a0[cs.CV]"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2843369"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2019.2934659"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-demos.22"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744718"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2864500"},{"key":"e_1_3_2_2_21_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML). PMLR","author":"Kim Been","year":"2018","unstructured":"Been Kim , Martin Wattenberg , Justin Gilmer , Carrie Cai , James Wexler , Fernanda Viegas , and Rory Sayres . 2018 . Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) . In Proceedings of the International Conference on Machine Learning (ICML). PMLR , Stockholm, Sweden, 2668\u20132677. Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. 2018. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). In Proceedings of the International Conference on Machine Learning (ICML). PMLR, Stockholm, Sweden, 2668\u20132677."},{"key":"e_1_3_2_2_22_1","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Vol.\u00a011700","author":"Kindermans Pieter-Jan","unstructured":"Pieter-Jan Kindermans , Sara Hooker , Julius Adebayo , Maximilian Alber , Kristof\u00a0 T. Sch\u00fctt , Sven D\u00e4hne , Dumitru Erhan , and Been Kim . 2019. The (Un)reliability of Saliency Methods . In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Vol.\u00a011700 . Springer , 267\u2013280. Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof\u00a0T. Sch\u00fctt, Sven D\u00e4hne, Dumitru Erhan, and Been Kim. 2019. The (Un)reliability of Saliency Methods. In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Vol.\u00a011700. Springer, 267\u2013280."},{"key":"e_1_3_2_2_23_1","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"P.","unstructured":"Diederik\u00a0 P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization . In Proceedings of the International Conference on Learning Representations (ICLR) . San Diego, USA. Diederik\u00a0P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In Proceedings of the International Conference on Learning Representations (ICLR). San Diego, USA."},{"key":"e_1_3_2_2_24_1","volume-title":"Captum: A unified and generic model interpretability library for PyTorch. arxiv:2009.07896\u00a0[cs.LG]","author":"Kokhlikyan Narine","year":"2020","unstructured":"Narine Kokhlikyan , Vivek Miglani , Miguel Martin , Edward Wang , Bilal Alsallakh , Jonathan Reynolds , Alexander Melnikov , Natalia Kliushkina , Carlos Araya , Siqi Yan , and Orion Reblitz-Richardson . 2020 . Captum: A unified and generic model interpretability library for PyTorch. arxiv:2009.07896\u00a0[cs.LG] Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson. 2020. Captum: A unified and generic model interpretability library for PyTorch. arxiv:2009.07896\u00a0[cs.LG]"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1011"},{"key":"e_1_3_2_2_26_1","volume-title":"Proceedings of the Conference on Neural Information Processing Systems (NIPS)","author":"M.","unstructured":"Scott\u00a0 M. Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions . In Proceedings of the Conference on Neural Information Processing Systems (NIPS) . Long Beach, USA, 4765\u20134774. Scott\u00a0M. Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Proceedings of the Conference on Neural Information Processing Systems (NIPS). Long Beach, USA, 4765\u20134774."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.110"},{"key":"e_1_3_2_2_28_1","unstructured":"Christopher Meek. 2016. A Characterization of Prediction Errors. arxiv:1611.05955\u00a0[cs.LG]  Christopher Meek. 2016. A Characterization of Prediction Errors. arxiv:1611.05955\u00a0[cs.LG]"},{"key":"e_1_3_2_2_29_1","volume-title":"A Survey on Bias and Fairness in Machine Learning. Comput. Surveys 54, 6","author":"Mehrabi Ninareh","year":"2021","unstructured":"Ninareh Mehrabi , Fred Morstatter , Nripsuta Saxena , Kristina Lerman , and Aram Galstyan . 2021. A Survey on Bias and Fairness in Machine Learning. Comput. Surveys 54, 6 ( 2021 ), 115:1\u2013115:35. Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021. A Survey on Bias and Fairness in Machine Learning. Comput. Surveys 54, 6 (2021), 115:1\u2013115:35."},{"key":"e_1_3_2_2_30_1","volume-title":"The Building Blocks of Interpretability. Distill","author":"Olah Chris","year":"2018","unstructured":"Chris Olah , Arvind Satyanarayan , Ian Johnson , Shan Carter , Ludwig Schubert , Katherine Ye , and Alexander Mordvintsev . 2018. The Building Blocks of Interpretability. Distill ( 2018 ). https:\/\/doi.org\/10.23915\/distill.00010 https:\/\/distill.pub\/2018\/building-blocks. Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, and Alexander Mordvintsev. 2018. The Building Blocks of Interpretability. Distill (2018). https:\/\/doi.org\/10.23915\/distill.00010 https:\/\/distill.pub\/2018\/building-blocks."},{"key":"e_1_3_2_2_31_1","volume-title":"Proceedings of the Conference on Neural Information Processing Systems (NeurIPS)","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , Alban Desmaison , Andreas K\u00f6pf , Edward\u00a0 Z. Yang , Zachary DeVito , Martin Raison , Alykhan Tejani , Sasank Chilamkurthy , Benoit Steiner , Lu Fang , Junjie Bai , and Soumith Chintala . 2019 . PyTorch: An Imperative Style, High-Performance Deep Learning Library . In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) . Vancouver, Canada, 8024\u20138035. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas K\u00f6pf, Edward\u00a0Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS). Vancouver, Canada, 8024\u20138035."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11747-019-00710-5"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2598828"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_2_2_36_1","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR), Workshop Track","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan , Andrea Vedaldi , and Andrew Zisserman . 2014 . Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps . In Proceedings of the International Conference on Learning Representations (ICLR), Workshop Track . Banff, Canada. Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. In Proceedings of the International Conference on Learning Representations (ICLR), Workshop Track. Banff, Canada."},{"key":"e_1_3_2_2_37_1","unstructured":"Daniel Smilkov Nikhil Thorat Been Kim Fernanda\u00a0B. Vi\u00e9gas and Martin Wattenberg. 2017. SmoothGrad: removing noise by adding noise. arxiv:1706.03825\u00a0[cs.LG]  Daniel Smilkov Nikhil Thorat Been Kim Fernanda\u00a0B. Vi\u00e9gas and Martin Wattenberg. 2017. SmoothGrad: removing noise by adding noise. arxiv:1706.03825\u00a0[cs.LG]"},{"key":"e_1_3_2_2_38_1","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR), Workshop Track, Yoshua Bengio and Yann LeCun (Eds.)","author":"Springenberg Jost\u00a0Tobias","year":"2015","unstructured":"Jost\u00a0Tobias Springenberg , Alexey Dosovitskiy , Thomas Brox , and Martin\u00a0 A. Riedmiller . 2015 . Striving for Simplicity: The All Convolutional Net . In Proceedings of the International Conference on Learning Representations (ICLR), Workshop Track, Yoshua Bengio and Yann LeCun (Eds.) . San Diego, USA. Jost\u00a0Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin\u00a0A. Riedmiller. 2015. Striving for Simplicity: The All Convolutional Net. In Proceedings of the International Conference on Learning Representations (ICLR), Workshop Track, Yoshua Bengio and Yann LeCun (Eds.). San Diego, USA."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744158"},{"key":"e_1_3_2_2_40_1","volume-title":"Visualizing the Impact of Feature Attribution Baselines. Distill","author":"Sturmfels Pascal","year":"2020","unstructured":"Pascal Sturmfels , Scott Lundberg , and Su-In Lee . 2020. Visualizing the Impact of Feature Attribution Baselines. Distill ( 2020 ). https:\/\/doi.org\/10.23915\/distill.00022 https:\/\/distill.pub\/2020\/attribution-baselines. Pascal Sturmfels, Scott Lundberg, and Su-In Lee. 2020. Visualizing the Impact of Feature Attribution Baselines. Distill (2020). https:\/\/doi.org\/10.23915\/distill.00022 https:\/\/distill.pub\/2020\/attribution-baselines."},{"key":"e_1_3_2_2_41_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML). PMLR","author":"Sundararajan Mukund","year":"2017","unstructured":"Mukund Sundararajan , Ankur Taly , and Qiqi Yan . 2017 . Axiomatic Attribution for Deep Networks . In Proceedings of the International Conference on Machine Learning (ICML). PMLR , Sydney, Australia, 3319\u20133328. Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic Attribution for Deep Networks. In Proceedings of the International Conference on Machine Learning (ICML). PMLR, Sydney, Australia, 3319\u20133328."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6064"},{"key":"e_1_3_2_2_43_1","volume-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data 5, 1","author":"Tschandl Philipp","year":"2018","unstructured":"Philipp Tschandl , Cliff Rosendahl , and Harald Kittler . 2018. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data 5, 1 ( 2018 ). Philipp Tschandl, Cliff Rosendahl, and Harald Kittler. 2018. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data 5, 1 (2018)."},{"key":"e_1_3_2_2_44_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML), Vol.\u00a0119","author":"Tsipras Dimitris","year":"2020","unstructured":"Dimitris Tsipras , Shibani Santurkar , Logan Engstrom , Andrew Ilyas , and Aleksander Madry . 2020 . From ImageNet to Image Classification: Contextualizing Progress on Benchmarks . In Proceedings of the International Conference on Machine Learning (ICML), Vol.\u00a0119 . PMLR, Virtual Event, 9625\u20139635. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, and Aleksander Madry. 2020. From ImageNet to Image Classification: Contextualizing Progress on Benchmarks. In Proceedings of the International Conference on Machine Learning (ICML), Vol.\u00a0119. PMLR, Virtual Event, 9625\u20139635."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88693-8_52"},{"key":"e_1_3_2_2_46_1","first-page":"56","article-title":"The What-If Tool: Interactive Probing of Machine Learning Models","volume":"26","author":"Wexler James","year":"2020","unstructured":"James Wexler , Mahima Pushkarna , Tolga Bolukbasi , Martin Wattenberg , Fernanda\u00a0 B. Vi\u00e9gas , and Jimbo Wilson . 2020 . The What-If Tool: Interactive Probing of Machine Learning Models . IEEE Transactions on Visualization and Computer Graphics 26 , 1(2020), 56 \u2013 65 . James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda\u00a0B. Vi\u00e9gas, and Jimbo Wilson. 2020. The What-If Tool: Interactive Probing of Machine Learning Models. IEEE Transactions on Visualization and Computer Graphics 26, 1(2020), 56\u201365.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744878"},{"key":"e_1_3_2_2_48_1","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR). OpenReview.net, Virtual Event.","author":"Xiao Kai\u00a0Yuanqing","year":"2021","unstructured":"Kai\u00a0Yuanqing Xiao , Logan Engstrom , Andrew Ilyas , and Aleksander Madry . 2021 . Noise or Signal: The Role of Image Backgrounds in Object Recognition . In Proceedings of the International Conference on Learning Representations (ICLR). OpenReview.net, Virtual Event. Kai\u00a0Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry. 2021. Noise or Signal: The Role of Image Backgrounds in Object Recognition. In Proceedings of the International Conference on Learning Representations (ICLR). OpenReview.net, Virtual Event."},{"key":"e_1_3_2_2_49_1","unstructured":"Mengjiao Yang and Been Kim. 2019. Benchmarking Attribution Methods with Relative Feature Importance. arxiv:1907.09701\u00a0[cs.LG]  Mengjiao Yang and Been Kim. 2019. Benchmarking Attribution Methods with Relative Feature Importance. arxiv:1907.09701\u00a0[cs.LG]"},{"key":"e_1_3_2_2_50_1","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV), Vol.\u00a08689","author":"D.","unstructured":"Matthew\u00a0 D. Zeiler and Rob Fergus. 2014. Visualizing and Understanding Convolutional Networks . In Proceedings of the European Conference on Computer Vision (ECCV), Vol.\u00a08689 . Springer, Zurich, Switzerland, 818\u2013833. Matthew\u00a0D. Zeiler and Rob Fergus. 2014. Visualizing and Understanding Convolutional Networks. In Proceedings of the European Conference on Computer Vision (ECCV), Vol.\u00a08689. Springer, Zurich, Switzerland, 818\u2013833."}],"event":{"name":"CHI '22: CHI Conference on Human Factors in Computing Systems","location":"New Orleans LA USA","acronym":"CHI '22","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["CHI Conference on Human Factors in Computing Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3491102.3501965","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3491102.3501965","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:04Z","timestamp":1750188664000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3491102.3501965"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,27]]},"references-count":49,"alternative-id":["10.1145\/3491102.3501965","10.1145\/3491102"],"URL":"https:\/\/doi.org\/10.1145\/3491102.3501965","relation":{},"subject":[],"published":{"date-parts":[[2022,4,27]]}}}