{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T19:19:12Z","timestamp":1787858352269,"version":"build-2784847793"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"IHUB-ANUBHUTI-IIITD FOUNDATION"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,26]]},"DOI":"10.1145\/3514094.3534191","type":"proceedings-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T22:25:13Z","timestamp":1658960713000},"page":"599-608","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability"],"prefix":"10.1145","author":[{"given":"Aditya","family":"Saini","sequence":"first","affiliation":[{"name":"Indraprastha Institute of Information Technology, Delhi, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ranjitha","family":"Prasad","sequence":"additional","affiliation":[{"name":"Indraprastha Institute of Information Technology, Delhi, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"e_1_3_2_1_2_1","volume-title":"Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)","author":"Adadi Amina","year":"2018","unstructured":"Amina Adadi and Mohammed Berrada . 2018. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI) . IEEE access 6 ( 2018 ), 52138--52160. Amina Adadi and Mohammed Berrada. 2018. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access 6 (2018), 52138--52160."},{"key":"e_1_3_2_1_3_1","unstructured":"Arthur Asuncion and David Newman. 2007. UCI machine learning repository.  Arthur Asuncion and David Newman. 2007. UCI machine learning repository."},{"key":"e_1_3_2_1_4_1","unstructured":"The GPyOpt authors. 2016. GPyOpt: A Bayesian Optimization framework in Python. http:\/\/github.com\/SheffieldML\/GPyOpt.  The GPyOpt authors. 2016. GPyOpt: A Bayesian Optimization framework in Python. http:\/\/github.com\/SheffieldML\/GPyOpt."},{"key":"e_1_3_2_1_5_1","volume-title":"Explanations can be manipulated and geometry is to blame. Advances in Neural Information Processing Systems 32","author":"Dombrowski Ann-Kathrin","year":"2019","unstructured":"Ann-Kathrin Dombrowski , Maximillian Alber , Christopher Anders , Marcel Ackermann , Klaus-Robert M\u00fcller , and Pan Kessel . 2019. Explanations can be manipulated and geometry is to blame. Advances in Neural Information Processing Systems 32 ( 2019 ). Ann-Kathrin Dombrowski, Maximillian Alber, Christopher Anders, Marcel Ackermann, Klaus-Robert M\u00fcller, and Pan Kessel. 2019. Explanations can be manipulated and geometry is to blame. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_6_1","volume-title":"Explaining Deep Learning Models--A Bayesian Non-parametric Approach. Advances in Neural Information Processing Systems 31","author":"Guo Wenbo","year":"2018","unstructured":"Wenbo Guo , Sui Huang , Yunzhe Tao , Xinyu Xing , and Lin Lin . 2018. Explaining Deep Learning Models--A Bayesian Non-parametric Approach. Advances in Neural Information Processing Systems 31 ( 2018 ). Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, and Lin Lin. 2018. Explaining Deep Learning Models--A Bayesian Non-parametric Approach. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_8_1","volume-title":"et al","author":"Joel Marina Z","year":"2021","unstructured":"Marina Z Joel , Sachin Umrao , Enoch Chang , Rachel Choi , Daniel Yang , James Duncan , Antonio Omuro , Roy Herbst , Harlan Krumholz , Sanjay Aneja , et al . 2021 . Adversarial attack vulnerability of deep learning models for oncologic images. medRxiv (2021), 2021--01. Marina Z Joel, Sachin Umrao, Enoch Chang, Rachel Choi, Daniel Yang, James Duncan, Antonio Omuro, Roy Herbst, Harlan Krumholz, Sanjay Aneja, et al . 2021. Adversarial attack vulnerability of deep learning models for oncologic images. medRxiv (2021), 2021--01."},{"key":"e_1_3_2_1_9_1","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 675--690","author":"Kim Jungtaek","year":"2020","unstructured":"Jungtaek Kim and Seungjin Choi . 2020 . On local optimizers of acquisition functions in bayesian optimization . In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 675--690 . Jungtaek Kim and Seungjin Choi. 2020. On local optimizers of acquisition functions in bayesian optimization. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 675--690."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-2099-5_1"},{"key":"e_1_3_2_1_11_1","volume-title":"A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30","author":"Lundberg Scott M","year":"2017","unstructured":"Scott M Lundberg and Su-In Lee . 2017. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30 ( 2017 ). Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287574"},{"key":"e_1_3_2_1_13_1","volume-title":"Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification. arXiv preprint physics\/9701026","author":"Neal Radford M","year":"1997","unstructured":"Radford M Neal . 1997. Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification. arXiv preprint physics\/9701026 ( 1997 ). Radford M Neal. 1997. Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification. arXiv preprint physics\/9701026 (1997)."},{"key":"e_1_3_2_1_14_1","volume-title":"The 22nd International Conference on Intelligence and Statistics. PMLR, 1743--1752","author":"Paananen Topi","year":"2019","unstructured":"Topi Paananen , Juho Piironen , Michael Riis Andersen , and Aki Vehtari . 2019 . Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution . In The 22nd International Conference on Intelligence and Statistics. PMLR, 1743--1752 . Topi Paananen, Juho Piironen, Michael Riis Andersen, and Aki Vehtari. 2019. Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. In The 22nd International Conference on Intelligence and Statistics. PMLR, 1743--1752."},{"key":"e_1_3_2_1_15_1","volume-title":"et al","author":"Pedregosa Fabian","year":"2011","unstructured":"Fabian Pedregosa , Ga\u00ebl Varoquaux , Alexandre Gramfort , Vincent Michel , Bertrand Thirion , Olivier Grisel , Mathieu Blondel , Peter Prettenhofer , Ron Weiss , Vincent Dubourg , et al . 2011 . Scikit-learn : Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825--2830. Fabian Pedregosa, Ga\u00ebl Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al . 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825--2830."},{"key":"e_1_3_2_1_16_1","volume-title":"Summer school on machine learning","author":"Rasmussen Carl Edward","unstructured":"Carl Edward Rasmussen . 2003. Gaussian processes in machine learning . In Summer school on machine learning . Springer , 63--71. Carl Edward Rasmussen. 2003. Gaussian processes in machine learning. In Summer school on machine learning. Springer, 63--71."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_18_1","volume-title":"et al","author":"Russakovsky Olga","year":"2015","unstructured":"Olga Russakovsky , Jia Deng , Hao Su , Jonathan Krause , Sanjeev Satheesh , Sean Ma , Zhiheng Huang , Andrej Karpathy , Aditya Khosla , Michael Bernstein , et al . 2015 . Imagenet large scale visual recognition challenge. International journal of computer vision 115, 3 (2015), 211--252. Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al . 2015. Imagenet large scale visual recognition challenge. International journal of computer vision 115, 3 (2015), 211--252."},{"key":"e_1_3_2_1_19_1","volume-title":"Improving LIME Robustness with Smarter Locality Sampling. arXiv:2006.12302","author":"Saito Sean","year":"2020","unstructured":"Sean Saito , Eugene Chua , Nicholas Capel , and Rocco Hu. 2020. Improving LIME Robustness with Smarter Locality Sampling. arXiv:2006.12302 ( 2020 ). Sean Saito, Eugene Chua, Nicholas Capel, and Rocco Hu. 2020. Improving LIME Robustness with Smarter Locality Sampling. arXiv:2006.12302 (2020)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"e_1_3_2_1_22_1","volume-title":"ALIME: Autoencoder based approach for local interpretability","author":"Shankaranarayana S M","year":"2019","unstructured":"S M Shankaranarayana and D Runje . 2019 . ALIME: Autoencoder based approach for local interpretability . In IDEAL. Springer , 454--463. S M Shankaranarayana and D Runje. 2019. ALIME: Autoencoder based approach for local interpretability. In IDEAL. Springer, 454--463."},{"key":"e_1_3_2_1_23_1","volume-title":"Proceedings of ICML. PMLR, 3145--3153","author":"Shrikumar A","year":"2017","unstructured":"A Shrikumar , P Greenside , and A Kundaje . 2017 . Learning important features through propagating activation differences . In Proceedings of ICML. PMLR, 3145--3153 . A Shrikumar, P Greenside, and A Kundaje. 2017. Learning important features through propagating activation differences. In Proceedings of ICML. PMLR, 3145--3153."},{"key":"e_1_3_2_1_24_1","volume-title":"Reliable post hoc explanations: Modeling uncertainty in explainability. Advances in Neural Information Processing Systems 34","author":"Slack Dylan","year":"2021","unstructured":"Dylan Slack , Anna Hilgard , Sameer Singh , and Himabindu Lakkaraju . 2021. Reliable post hoc explanations: Modeling uncertainty in explainability. Advances in Neural Information Processing Systems 34 ( 2021 ). Dylan Slack, Anna Hilgard, Sameer Singh, and Himabindu Lakkaraju. 2021. Reliable post hoc explanations: Modeling uncertainty in explainability. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375830"},{"key":"e_1_3_2_1_26_1","volume-title":"Engineering design via surrogate modelling: a practical guide","author":"Sobester Andr\u00e1s","unstructured":"Andr\u00e1s Sobester , Alexander Forrester , and Andy Keane . 2008. Engineering design via surrogate modelling: a practical guide . John Wiley & Sons . Andr\u00e1s Sobester, Alexander Forrester, and Andy Keane. 2008. Engineering design via surrogate modelling: a practical guide. John Wiley & Sons."},{"key":"e_1_3_2_1_27_1","volume-title":"Gaussian process optimization in the bandit setting: No regret and experimental design. arXiv preprint arXiv:0912.3995","author":"Srinivas Niranjan","year":"2009","unstructured":"Niranjan Srinivas , Andreas Krause , Sham M Kakade , and Matthias Seeger . 2009. Gaussian process optimization in the bandit setting: No regret and experimental design. arXiv preprint arXiv:0912.3995 ( 2009 ). Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger. 2009. Gaussian process optimization in the bandit setting: No regret and experimental design. arXiv preprint arXiv:0912.3995 (2009)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"e_1_3_2_1_29_1","unstructured":"G. Visani E. Bagli and F. Chesani. 2020. OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms. arXiv:2006.05714 (2020).  G. Visani E. Bagli and F. Chesani. 2020. OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms. arXiv:2006.05714 (2020)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1080\/01605682.2020.1865846"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.3390\/make3030027"},{"key":"e_1_3_2_1_32_1","volume-title":"Baylime: Bayesian local interpretable model-agnostic explanations. In Uncertainty in Artificial Intelligence. PMLR, 887--896.","author":"Zhao Xingyu","year":"2021","unstructured":"Xingyu Zhao , Wei Huang , Xiaowei Huang , Valentin Robu , and David Flynn . 2021 . Baylime: Bayesian local interpretable model-agnostic explanations. In Uncertainty in Artificial Intelligence. PMLR, 887--896. Xingyu Zhao, Wei Huang, Xiaowei Huang, Valentin Robu, and David Flynn. 2021. Baylime: Bayesian local interpretable model-agnostic explanations. In Uncertainty in Artificial Intelligence. PMLR, 887--896."},{"key":"e_1_3_2_1_33_1","volume-title":"Electronic and Automation Control Conference (ITNEC). IEEE, 982--986","author":"Zhou Wei","year":"2019","unstructured":"Wei Zhou , XiaoWei Yuan , Wenjun Chai , and Hui Ma . 2019 . Deep learning based attack on social authentication system. In 2019 IEEE 3rd Information Technology, Networking , Electronic and Automation Control Conference (ITNEC). IEEE, 982--986 . Wei Zhou, XiaoWei Yuan, Wenjun Chai, and Hui Ma. 2019. Deep learning based attack on social authentication system. In 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 982--986."}],"event":{"name":"AIES '22: AAAI\/ACM Conference on AI, Ethics, and Society","location":"Oxford United Kingdom","acronym":"AIES '22","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence","AAAI"]},"container-title":["Proceedings of the 2022 AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514094.3534191","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514094.3534191","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:37Z","timestamp":1750186957000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514094.3534191"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,26]]},"references-count":33,"alternative-id":["10.1145\/3514094.3534191","10.1145\/3514094"],"URL":"https:\/\/doi.org\/10.1145\/3514094.3534191","relation":{},"subject":[],"published":{"date-parts":[[2022,7,26]]},"assertion":[{"value":"2022-07-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}