{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T01:55:44Z","timestamp":1786672544741,"version":"build-2736575974"},"reference-count":106,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T00:00:00Z","timestamp":1710201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2024,3,12]]},"abstract":"<jats:p>Formal feature explanations strictly maintain perfect conformity but are intractable to compute, while heuristic methods are much faster but can lead to problematic explanations due to lack of conformity guarantees. We propose relative keys that have the best of both worlds. Relative keys associate feature explanations with a set of instances as context, and warrant perfect conformity over the context as formal explanations do, whilst being orders of magnitudes faster and working for complex blackbox models. Based on it, we develop CCE, a prototype that computes explanations with provably bounded conformity and succinctness, without accessing the models. We show that computing the most succinct relative keys is NP-complete and develop various algorithms for it under the batch and online models. Using 9 real-life datasets and 7 state-of-the-art explanation methods, we demonstrate that CCE explains cases where existing methods cannot, and provides more succinct explanations with perfect conformity for cases they can; moreover, it is 2 orders of magnitude faster.<\/jats:p>","DOI":"10.1145\/3639263","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T18:51:32Z","timestamp":1711479092000},"page":"1-28","source":"Crossref","is-referenced-by-count":3,"title":["Relative Keys: Putting Feature Explanation into Context"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9309-7711","authenticated-orcid":false,"given":"Shuai","family":"An","sequence":"first","affiliation":[{"name":"University of Edinburgh, Edinburgh, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7984-3219","authenticated-orcid":false,"given":"Yang","family":"Cao","sequence":"additional","affiliation":[{"name":"University of Edinburgh, Edinburgh, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"2020. Machine Learning Best Practices in Financial Services. https:\/\/d1.awsstatic.com\/whitepapers\/machine-learning-in-financial-services-on-aws.pdf."},{"key":"e_1_2_2_2_1","unstructured":"2022. Caching for ML Model Deployments. https:\/\/www.tekhnoal.com\/caching-for-ml-models.html."},{"key":"e_1_2_2_3_1","unstructured":"2022. Compas dataset. https:\/\/www.kaggle.com\/datasets\/danofer\/compass."},{"key":"e_1_2_2_4_1","unstructured":"2022. Kaggle. https:\/\/www.kaggle.com\/."},{"key":"e_1_2_2_5_1","unstructured":"2022. Loan dataset. https:\/\/www.kaggle.com\/datasets\/vikasukani\/loan-eligible-dataset."},{"key":"e_1_2_2_6_1","unstructured":"2022. Manual Review. https:\/\/seon.io\/resources\/dictionary\/manual-reviews\/."},{"key":"e_1_2_2_7_1","unstructured":"2022. online predictions with static features. https:\/\/www.zdnet.com\/article\/machine-learning-is-going-real-time-heres-why-and-how\/."},{"key":"e_1_2_2_8_1","unstructured":"2022. UCI Machine Learning Repository. https:\/\/archive.ics.uci.edu\/ml\/index.php."},{"key":"e_1_2_2_9_1","unstructured":"2022. Unleashing the power of machine learning models in banking through explainable artificial intelligence (XAI). https:\/\/www2.deloitte.com\/us\/en\/insights\/industry\/financial-services\/explainable-ai-in-banking.html."},{"key":"e_1_2_2_10_1","unstructured":"2023. Amazon ElastiCache for Redis. https:\/\/aws.amazon.com\/elasticache\/redis\/?nc1=h_ls."},{"key":"e_1_2_2_11_1","unstructured":"2023. Amazon SageMaker. https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/clarify-model-explainability.html."},{"key":"e_1_2_2_12_1","unstructured":"2023. Explainable Artificial Intelligence: From Static to Dynamic. https:\/\/sites.google.com\/view\/dynxai-ecmlpkdd-2023."},{"key":"e_1_2_2_13_1","unstructured":"2023. Google Cloud: Precomputing and caching predictions. https:\/\/cloud.google.com\/architecture\/minimizing-predictive-serving-latency-in-machine-learning."},{"key":"e_1_2_2_14_1","unstructured":"2023. Upstart. https:\/\/www.upstart.com\/."},{"key":"e_1_2_2_15_1","unstructured":"2023. Zest AI. https:\/\/www.zest.ai\/."},{"key":"e_1_2_2_16_1","unstructured":"Darius Afchar Vincent Guigue and Romain Hennequin. 2021. Towards Rigorous Interpretations: a Formalisation of Feature Attribution. In ICML."},{"key":"e_1_2_2_17_1","unstructured":"Chirag Agarwal Satyapriya Krishna Eshika Saxena Martin Pawelczyk Nari Johnson Isha Puri Marinka Zitnik and Himabindu Lakkaraju. 2022. OpenXAI: Towards a Transparent Evaluation of Model Explanations. In NeurIPS."},{"key":"e_1_2_2_18_1","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Agrawal Rakesh","unstructured":"Rakesh Agrawal, Heikki Mannila, Ramakrishnan Srikant, Hannu Toivonen, and A. Inkeri Verkamo. 1996. Fast Discovery of Association Rules. In Advances in Knowledge Discovery and Data Mining. AAAI\/MIT Press, 307--328."},{"key":"e_1_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Emanuele Albini Antonio Rago Pietro Baroni and Francesca Toni. 2020. Relation-Based Counterfactual Explanations for Bayesian Network Classifiers.. In IJCAI. 451--457.","DOI":"10.24963\/ijcai.2020\/63"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/780542.780558"},{"key":"e_1_2_2_21_1","volume-title":"Building Machine Learning Powered Applications: Going from Idea to Product. \" O'Reilly Media","author":"Ameisen Emmanuel","unstructured":"Emmanuel Ameisen. 2020. Building Machine Learning Powered Applications: Going from Idea to Product. \" O'Reilly Media, Inc.\"."},{"key":"e_1_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Leila Amgoud and Jonathan Ben-Naim. 2022. Axiomatic Foundations of Explainability. In IJCAI. 636--642.","DOI":"10.24963\/ijcai.2022\/90"},{"key":"e_1_2_2_23_1","volume-title":"Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garc\u00eda, Sergio Gil-L\u00f3pez, Daniel Molina, Richard Benjamins, et al .","author":"Arrieta Alejandro Barredo","year":"2020","unstructured":"Alejandro Barredo Arrieta, Natalia D\u00edaz-Rodr\u00edguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garc\u00eda, Sergio Gil-L\u00f3pez, Daniel Molina, Richard Benjamins, et al . 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion 58 (2020), 82--115."},{"key":"e_1_2_2_24_1","volume-title":"Christina Lioma, and Isabelle Augenstein.","author":"Atanasova Pepa","year":"2020","unstructured":"Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020. A Diagnostic Study of Explainability Techniques for Text Classification. In EMNLP. 3256--3274."},{"key":"e_1_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Gilles Audemard Fr\u00e9d\u00e9ric Koriche and Pierre Marquis. 2020. On Tractable XAI Queries based on Compiled Representations. In KR. 838--849.","DOI":"10.24963\/kr.2020\/86"},{"key":"e_1_2_2_26_1","volume-title":"Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties","author":"Ausiello Giorgio","unstructured":"Giorgio Ausiello, M Protasi, A Marchetti-Spaccamela, G Gambosi, P Crescenzi, and V Kann. 1999. Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties. Springer-Verlag."},{"key":"e_1_2_2_27_1","first-page":"451","article-title":"Using landmarks for explaining entity matching models","volume":"2021","author":"Baraldi Andrea","year":"2021","unstructured":"Andrea Baraldi, Francesco Del Buono, Matteo Paganelli, Francesco Guerra, et al . 2021. Using landmarks for explaining entity matching models. In EDBT, Vol. 2021. 451--456.","journal-title":"EDBT"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442200"},{"key":"e_1_2_2_29_1","unstructured":"Guy Blanc Jane Lange and Li-Yang Tan. 2021. Provably efficient succinct and precise explanations. In NeurIPS. 6129--6141."},{"key":"e_1_2_2_30_1","unstructured":"Rishi Bommasani Drew A. Hudson Ehsan Adeli Russ B. Altman Simran Arora Sydney von Arx Michael S. Bernstein Jeannette Bohg Antoine Bosselut Emma Brunskill Erik Brynjolfsson Shyamal Buch Dallas Card Rodrigo Castellon Niladri S. Chatterji Annie S. Chen Kathleen Creel Jared Quincy Davis Dorottya Demszky Chris Donahue Moussa Doumbouya Esin Durmus Stefano Ermon John Etchemendy Kawin Ethayarajh Li Fei-Fei Chelsea Finn Trevor Gale Lauren Gillespie Karan Goel Noah D. Goodman Shelby Grossman Neel Guha Tatsunori Hashimoto Peter Henderson John Hewitt Daniel E. Ho Jenny Hong Kyle Hsu Jing Huang Thomas Icard Saahil Jain Dan Jurafsky Pratyusha Kalluri Siddharth Karamcheti Geoff Keeling Fereshte Khani Omar Khattab Pang Wei Koh Mark S. Krass Ranjay Krishna Rohith Kuditipudi and et al. 2021. On the Opportunities and Risks of Foundation Models. CoRR abs\/2108.07258 (2021)."},{"key":"e_1_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Peter Buneman Sanjeev Khanna and Wang Chiew Tan. 2001. Why and Where: A Characterization of Data Provenance. In ICDT.","DOI":"10.1007\/3-540-44503-X_20"},{"key":"e_1_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Peter Buneman and Wang Chiew Tan. 2007. Provenance in databases. In SIGMOD. ACM.","DOI":"10.1145\/1247480.1247646"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12228"},{"key":"e_1_2_2_34_1","volume-title":"et al","author":"Chen Tianqi","year":"2015","unstructured":"Tianqi Chen, Tong He, Michael Benesty, et al . 2015. Xgboost: extreme gradient boosting. R package version 0.4--2 1, 4 (2015), 1--4."},{"key":"e_1_2_2_35_1","first-page":"1","article-title":"On the Tractability of Explaining Decisions of Classifiers","volume":"210","author":"Cooper Martin C.","year":"2021","unstructured":"Martin C. Cooper and Jo\u00e3o Marques-Silva. 2021. On the Tractability of Explaining Decisions of Classifiers. In CP, Vol. 210. 21:1--21:18.","journal-title":"CP"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58112-1_31"},{"key":"e_1_2_2_37_1","unstructured":"Sanjib Das A Doan C Gokhale Psgc and P Konda. 2016. The magellan data repository. (2016)."},{"key":"e_1_2_2_38_1","unstructured":"Sanjoy Dasgupta Nave Frost and Michal Moshkovitz. 2022. Framework for evaluating faithfulness of local explanations. In ICML. PMLR 4794--4815."},{"key":"e_1_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Daniel Deutch and Nave Frost. 2019. Constraints-Based Explanations of Classifications. In ICDE. 530--541.","DOI":"10.1109\/ICDE.2019.00054"},{"key":"e_1_2_2_40_1","volume-title":"Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608","author":"Doshi-Velez Finale","year":"2017","unstructured":"Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)."},{"key":"e_1_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Michael Feldman Sorelle A. Friedler John Moeller Carlos Scheidegger and Suresh Venkatasubramanian. 2015. Certifying and Removing Disparate Impact. In KDD. ACM 259--268.","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_2_2_42_1","volume-title":"Johnson","author":"Garey M. R.","year":"1979","unstructured":"M. R. Garey and David S. Johnson. 1979. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman."},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24412-4_16"},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735461.2735467"},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.14778\/3570690.3570693"},{"key":"e_1_2_2_46_1","doi-asserted-by":"crossref","unstructured":"Niku Gorji and Sasha Rubin. 2022. Sufficient Reasons for Classifier Decisions in the Presence of Domain Constraints. In AAAI. 5660--5667.","DOI":"10.1609\/aaai.v36i5.20507"},{"key":"e_1_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Todd J. Green Gregory Karvounarakis and Val Tannen. 2007. Provenance semirings. In PODS. ACM 31--40.","DOI":"10.1145\/1265530.1265535"},{"key":"e_1_2_2_48_1","volume-title":"Local rule-based explanations of black box decision systems. arXiv preprint arXiv:1805.10820","author":"Guidotti Riccardo","year":"2018","unstructured":"Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, and Fosca Giannotti. 2018. Local rule-based explanations of black box decision systems. arXiv preprint arXiv:1805.10820 (2018)."},{"key":"e_1_2_2_49_1","volume-title":"DARPA's explainable artificial intelligence (XAI) program. AI magazine 40, 2","author":"Gunning David","year":"2019","unstructured":"David Gunning and David Aha. 2019. DARPA's explainable artificial intelligence (XAI) program. AI magazine 40, 2 (2019), 44--58."},{"key":"e_1_2_2_50_1","volume-title":"XAI-Explainable artificial intelligence. Science robotics 4, 37","author":"Gunning David","year":"2019","unstructured":"David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, and Guang-Zhong Yang. 2019. XAI-Explainable artificial intelligence. Science robotics 4, 37 (2019), eaay7120."},{"key":"e_1_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Johannes Haug Alexander Braun Stefan Z\u00fcrn and Gjergji Kasneci. 2022. Change Detection for Local Explainability in Evolving Data Streams. In CIKM. 706--716.","DOI":"10.1145\/3511808.3557257"},{"key":"e_1_2_2_52_1","doi-asserted-by":"crossref","unstructured":"Alexey Ignatiev. 2020. Towards Trustable Explainable AI. In IJCAI. 5154--5158.","DOI":"10.24963\/ijcai.2020\/726"},{"key":"e_1_2_2_53_1","doi-asserted-by":"crossref","unstructured":"Alexey Ignatiev Yacine Izza Peter J. Stuckey and Jo\u00e3o Marques-Silva. 2022. Using MaxSAT for Efficient Explanations of Tree Ensembles. In AAAI. 3776--3785.","DOI":"10.1609\/aaai.v36i4.20292"},{"key":"e_1_2_2_54_1","doi-asserted-by":"crossref","unstructured":"Alexey Ignatiev Nina Narodytska and Jo\u00e3o Marques-Silva. 2019. Abduction-Based Explanations for Machine Learning Models. In AAAI. 1511--1519.","DOI":"10.1609\/aaai.v33i01.33011511"},{"key":"e_1_2_2_55_1","volume-title":"Repairing and Refining Heuristic ML Explanations. CoRR abs\/1907.02509","author":"Ignatiev Alexey","year":"2019","unstructured":"Alexey Ignatiev, Nina Narodytska, and Jo\u00e3o Marques-Silva. 2019. On Validating, Repairing and Refining Heuristic ML Explanations. CoRR abs\/1907.02509 (2019)."},{"key":"e_1_2_2_56_1","unstructured":"Dominik Janzing Lenon Minorics and Patrick Bl\u00f6baum. 2020. Feature relevance quantification in explainable AI: A causal problem. In AISTATS."},{"key":"e_1_2_2_57_1","unstructured":"Amir-Hossein Karimi Gilles Barthe Borja Balle and Isabel Valera. 2020. Model-Agnostic Counterfactual Explanations for Consequential Decisions. In AISTATS."},{"key":"e_1_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.5555\/3001460.3001502"},{"key":"e_1_2_2_59_1","volume-title":"On the use of randomization in the online set cover problem","author":"Korman Simon","year":"2004","unstructured":"Simon Korman. 2004. On the use of randomization in the online set cover problem. Weizmann Institute of Science 2 (2004)."},{"key":"e_1_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2678025.2701399"},{"key":"e_1_2_2_61_1","doi-asserted-by":"crossref","unstructured":"Himabindu Lakkaraju Stephen H. Bach and Jure Leskovec. 2016. Interpretable Decision Sets: A Joint Framework for Description and Prediction. In SIGKDD. 1675--1684.","DOI":"10.1145\/2939672.2939874"},{"key":"e_1_2_2_62_1","doi-asserted-by":"crossref","unstructured":"Yaguang Li Kun Fu Zheng Wang Cyrus Shahabi Jieping Ye and Yan Liu. 2018. Multi-task Representation Learning for Travel Time Estimation. In KDD. ACM 1695--1704.","DOI":"10.1145\/3219819.3220033"},{"key":"e_1_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.14778\/3421424.3421431"},{"key":"e_1_2_2_64_1","first-page":"147","article-title":"Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI","volume":"10","author":"Liao Q Vera","year":"2022","unstructured":"Q Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, and Amit Dhurandhar. 2022. Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. In AAAI, Vol. 10. 147--159.","journal-title":"AAAI"},{"key":"e_1_2_2_65_1","doi-asserted-by":"crossref","unstructured":"Yin Lou Rich Caruana and Johannes Gehrke. 2012. Intelligible models for classification and regression. In KDD. ACM 150--158.","DOI":"10.1145\/2339530.2339556"},{"key":"e_1_2_2_66_1","volume-title":"Lundberg and Su-In Lee","author":"Scott","year":"2017","unstructured":"Scott M. Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In NeurIPS. 4765--4774."},{"key":"e_1_2_2_67_1","doi-asserted-by":"crossref","unstructured":"Jo\u00e3o Marques-Silva and Alexey Ignatiev. 2022. Delivering Trustworthy AI through Formal XAI. In AAAI. 12342--12350.","DOI":"10.1609\/aaai.v36i11.21499"},{"key":"e_1_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.14778\/1880172.1880176"},{"key":"e_1_2_2_69_1","first-page":"864","article-title":"Debating the Right to Explanation: An Autonomy-Based Analytical Framework","volume":"34","author":"Miao Michelle","year":"2022","unstructured":"Michelle Miao. 2022. Debating the Right to Explanation: An Autonomy-Based Analytical Framework. SAcLJ 34 (2022), 864.","journal-title":"SAcLJ"},{"key":"e_1_2_2_70_1","doi-asserted-by":"crossref","unstructured":"Zhengjie Miao Qitian Zeng Boris Glavic and Sudeepa Roy. 2019. Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances. In SIGMOD. ACM 485--502.","DOI":"10.1145\/3299869.3300066"},{"key":"e_1_2_2_71_1","volume-title":"Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence 267","author":"Miller Tim","year":"2019","unstructured":"Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence 267 (2019), 1--38."},{"key":"e_1_2_2_72_1","unstructured":"Christoph Molnar. 2020. Interpretable machine learning."},{"key":"e_1_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372850"},{"key":"e_1_2_2_74_1","volume-title":"Explanation Shift: Investigating Interactions between Models and Shifting Data Distributions. arXiv preprint arXiv:2303.08081","author":"Mougan Carlos","year":"2023","unstructured":"Carlos Mougan, Klaus Broelemann, David Masip, Gjergji Kasneci, Thanassis Thiropanis, and Steffen Staab. 2023. Explanation Shift: Investigating Interactions between Models and Shifting Data Distributions. arXiv preprint arXiv:2303.08081 (2023)."},{"key":"e_1_2_2_75_1","doi-asserted-by":"crossref","unstructured":"Sidharth Mudgal Han Li Theodoros Rekatsinas AnHai Doan Youngchoon Park Ganesh Krishnan Rohit Deep Esteban Arcaute and Vijay Raghavendra. 2018. Deep learning for entity matching: A design space exploration. In SIGMOD. 19--34.","DOI":"10.1145\/3183713.3196926"},{"key":"e_1_2_2_76_1","first-page":"610","article-title":"The utility of explainable ai in ad hoc human-machine teaming","volume":"34","author":"Paleja Rohan","year":"2021","unstructured":"Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige, Reed Jensen, and Matthew Gombolay. 2021. The utility of explainable ai in ad hoc human-machine teaming. Advances in Neural Information Processing Systems 34 (2021), 610--623.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_2_77_1","first-page":"19920","article-title":"Estimating training data influence by tracing gradient descent","volume":"33","author":"Pruthi Garima","year":"2020","unstructured":"Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan. 2020. Estimating training data influence by tracing gradient descent. Advances in Neural Information Processing Systems 33 (2020), 19920--19930.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_2_78_1","volume-title":"Understanding consumer preferences for explanations generated by XAI algorithms. arXiv:2107.02624","author":"Ramon Yanou","year":"2021","unstructured":"Yanou Ramon, Tom Vermeire, Olivier Toubia, David Martens, and Theodoros Evgeniou. 2021. Understanding consumer preferences for explanations generated by XAI algorithms. arXiv:2107.02624 (2021)."},{"key":"e_1_2_2_79_1","doi-asserted-by":"crossref","unstructured":"Marco T\u00falio Ribeiro Sameer Singh and Carlos Guestrin. 2016. \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier. In KDD. 1135--1144.","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_2_2_80_1","volume-title":"Anchors: High-Precision Model-Agnostic Explanations. In AAAI. 1527--1535.","author":"Ribeiro Marco T\u00falio","year":"2018","unstructured":"Marco T\u00falio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018. Anchors: High-Precision Model-Agnostic Explanations. In AAAI. 1527--1535."},{"key":"e_1_2_2_81_1","doi-asserted-by":"publisher","DOI":"10.14778\/2856318.2856329"},{"key":"e_1_2_2_82_1","doi-asserted-by":"crossref","unstructured":"Sudeepa Roy and Dan Suciu. 2014. A formal approach to finding explanations for database queries. In SIGMOD. ACM 1579--1590.","DOI":"10.1145\/2588555.2588578"},{"key":"e_1_2_2_83_1","volume-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence 1, 5","author":"Rudin Cynthia","year":"2019","unstructured":"Cynthia Rudin. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence 1, 5 (2019), 206--215."},{"key":"e_1_2_2_84_1","article-title":"Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation","volume":"24","author":"Rudin Cynthia","year":"2023","unstructured":"Cynthia Rudin and Yaron Shaposhnik. 2023. Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation. J. Mach. Learn. Res. 24 (2023), 16:1--16:44.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_2_85_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287569"},{"key":"e_1_2_2_86_1","volume-title":"Bertossi","author":"Salimi Babak","year":"2015","unstructured":"Babak Salimi and Leopoldo E. Bertossi. 2015. From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back. In ICDT."},{"key":"e_1_2_2_87_1","volume-title":"Proc. UAI'15 Causal Inference Workshop.","author":"Salimi Babak","unstructured":"Babak Salimi and Leopoldo E. Bertossi. 2015. Query-Answer Causality in Databases: Abductive Diagnosis and View Updates. In Proc. UAI'15 Causal Inference Workshop."},{"key":"e_1_2_2_88_1","volume-title":"Bertossi","author":"Salimi Babak","year":"2016","unstructured":"Babak Salimi and Leopoldo E. Bertossi. 2016. Causes for Query Answers from Databases, Datalog Abduction and View-Updates: The Presence of Integrity Constraints. In FLAIRS."},{"key":"e_1_2_2_89_1","unstructured":"Babak Salimi Leopoldo E. Bertossi Dan Suciu and Guy Van den Broeck. 2016. Quantifying Causal Effects on Query Answering in Databases. In TaPP."},{"key":"e_1_2_2_90_1","doi-asserted-by":"crossref","unstructured":"Babak Salimi Johannes Gehrke and Dan Suciu. 2018. Bias in OLAP Queries: Detection Explanation and Removal. In SIGMOD. ACM 1021--1035.","DOI":"10.1145\/3183713.3196914"},{"key":"e_1_2_2_91_1","volume-title":"Predicting recidivism in north carolina","author":"Schmidt Peter","year":"1978","unstructured":"Peter Schmidt and Ann D Witte. 1988. Predicting recidivism in north carolina, 1978 and 1980. Inter-university Consortium for Political and Social Research."},{"key":"e_1_2_2_92_1","volume-title":"conference on fairness, accountability and transparency. PMLR, 48--48","author":"Selbst Andrew","year":"2018","unstructured":"Andrew Selbst and Julia Powles. 2018. \"Meaningful information\" and the right to explanation. In conference on fairness, accountability and transparency. PMLR, 48--48."},{"key":"e_1_2_2_93_1","doi-asserted-by":"crossref","unstructured":"Andy Shih Arthur Choi and Adnan Darwiche. 2018. A Symbolic Approach to Explaining Bayesian Network Classifiers. In IJCAI. 5103--5111.","DOI":"10.24963\/ijcai.2018\/708"},{"key":"e_1_2_2_94_1","volume-title":"Vardi","author":"Shrotri Aditya A.","year":"2022","unstructured":"Aditya A. Shrotri, Nina Narodytska, Alexey Ignatiev, Kuldeep S. Meel, Jo\u00e3o Marques-Silva, and Moshe Y. Vardi. 2022. Constraint-Driven Explanations for Black-Box ML Models. In AAAI. AAAI Press, 8304--8314."},{"key":"e_1_2_2_95_1","doi-asserted-by":"crossref","unstructured":"Dylan Slack Sophie Hilgard Emily Jia Sameer Singh and Himabindu Lakkaraju. 2020. Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. In AIES. ACM 180--186.","DOI":"10.1145\/3375627.3375830"},{"key":"e_1_2_2_96_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372870"},{"key":"e_1_2_2_97_1","unstructured":"Mukund Sundararajan Ankur Taly and Qiqi Yan. 2017. Axiomatic Attribution for Deep Networks. In ICML."},{"key":"e_1_2_2_98_1","doi-asserted-by":"crossref","unstructured":"Kai Sheng Tai Vatsal Sharan Peter Bailis and Gregory Valiant. 2018. Sketching linear classifiers over data streams. In SIGMOD. 757--772.","DOI":"10.1145\/3183713.3196930"},{"key":"e_1_2_2_99_1","doi-asserted-by":"crossref","unstructured":"Tommaso Teofili Donatella Firmani Nick Koudas Vincenzo Martello Paolo Merialdo and Divesh Srivastava. 2022. Effective explanations for entity resolution models. In ICDE. 2709--2721.","DOI":"10.1109\/ICDE53745.2022.00248"},{"key":"e_1_2_2_100_1","unstructured":"Michael Tsang Sirisha Rambhatla and Yan Liu. 2020. How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions. In NeurIPS."},{"key":"e_1_2_2_101_1","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.2001.1775"},{"key":"e_1_2_2_102_1","doi-asserted-by":"crossref","unstructured":"Danding Wang Qian Yang Ashraf Abdul and Brian Y Lim. 2019. Designing theory-driven user-centric explainable AI. In CHI. 1--15.","DOI":"10.1145\/3290605.3300831"},{"key":"e_1_2_2_103_1","doi-asserted-by":"crossref","unstructured":"Hao Wang Naiyan Wang and Dit-Yan Yeung. 2015. Collaborative Deep Learning for Recommender Systems. In KDD. ACM 1235--1244.","DOI":"10.1145\/2783258.2783273"},{"key":"e_1_2_2_104_1","doi-asserted-by":"publisher","DOI":"10.1145\/3282486"},{"key":"e_1_2_2_105_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536354.2536356"},{"key":"e_1_2_2_106_1","volume-title":"Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges. In NLPCC","author":"Xu Feiyu","year":"2019","unstructured":"Feiyu Xu, Hans Uszkoreit, Yangzhou Du, Wei Fan, Dongyan Zhao, and Jun Zhu. 2019. Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges. In NLPCC, Vol. 11839. Springer, 563--574."}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639263","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3639263","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T15:11:54Z","timestamp":1755789114000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,12]]},"references-count":106,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3,12]]}},"alternative-id":["10.1145\/3639263"],"URL":"https:\/\/doi.org\/10.1145\/3639263","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,12]]}}}