{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T12:25:32Z","timestamp":1750767932918,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T00:00:00Z","timestamp":1626825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"ERC","award":["819702"],"award-info":[{"award-number":["819702"]}]},{"name":"NSF CAREER","award":["1942926"],"award-info":[{"award-number":["1942926"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,7,21]]},"DOI":"10.1145\/3461702.3462574","type":"proceedings-article","created":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T01:21:32Z","timestamp":1627694492000},"page":"1034-1041","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Who's Responsible? Jointly Quantifying the Contribution of the Learning Algorithm and Data"],"prefix":"10.1145","author":[{"given":"Gal","family":"Yona","sequence":"first","affiliation":[{"name":"Weizmann Institute, Rehovot, Israel"}]},{"given":"Amirata","family":"Ghorbani","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}]},{"given":"James","family":"Zou","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,7,30]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"A marketplace for data: an algorithmic solution. arXiv preprint arXiv:1805.08125","author":"Agarwal Anish","year":"2018","unstructured":"Anish Agarwal , Munther Dahleh , and Tuhin Sarkar . 2018. A marketplace for data: an algorithmic solution. arXiv preprint arXiv:1805.08125 ( 2018 ). Anish Agarwal, Munther Dahleh, and Tuhin Sarkar. 2018. A marketplace for data: an algorithmic solution. arXiv preprint arXiv:1805.08125 (2018)."},{"key":"e_1_3_2_2_2_1","first-page":"15479","article-title":"Differential privacy has disparate impact on model accuracy","volume":"32","author":"Bagdasaryan Eugene","year":"2019","unstructured":"Eugene Bagdasaryan , Omid Poursaeed , and Vitaly Shmatikov . 2019 . Differential privacy has disparate impact on model accuracy . Advances in Neural Information Processing Systems 32 (2019), 15479 -- 15488 . Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov. 2019. Differential privacy has disparate impact on model accuracy. Advances in Neural Information Processing Systems 32 (2019), 15479--15488.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_3_1","volume-title":"Conference on Fairness, Accountability and Transparency. 77--91","author":"Buolamwini Joy","year":"2018","unstructured":"Joy Buolamwini and Timnit Gebru . 2018 . Gender shades: Intersectional accuracy disparities in commercial gender classification . In Conference on Fairness, Accountability and Transparency. 77--91 . Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency. 77--91."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2008.04.004"},{"key":"e_1_3_2_2_5_1","volume-title":"L-Shapley and C-Shapley: Efficient model interpretation for structured data. arXiv preprint arXiv:1808.02610","author":"Chen Jianbo","year":"2018","unstructured":"Jianbo Chen , Le Song , Martin J Wainwright , and Michael I Jordan . 2018. L-Shapley and C-Shapley: Efficient model interpretation for structured data. arXiv preprint arXiv:1808.02610 ( 2018 ). Jianbo Chen, Le Song, Martin JWainwright, and Michael I Jordan. 2018. L-Shapley and C-Shapley: Efficient model interpretation for structured data. arXiv preprint arXiv:1808.02610 (2018)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2007.19.7.1939"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.42"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2008.05.003"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10440"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013681"},{"key":"e_1_3_2_2_11_1","volume-title":"A Distributional Framework for Data Valuation. arXiv preprint arXiv:2002.12334","author":"Ghorbani Amirata","year":"2020","unstructured":"Amirata Ghorbani , Michael P Kim , and James Zou . 2020. A Distributional Framework for Data Valuation. arXiv preprint arXiv:2002.12334 ( 2020 ). Amirata Ghorbani, Michael P Kim, and James Zou. 2020. A Distributional Framework for Data Valuation. arXiv preprint arXiv:2002.12334 (2020)."},{"key":"e_1_3_2_2_12_1","volume-title":"Data Shapley: Equitable Valuation of Data for Machine Learning. In International Conference on Machine Learning. 2242--2251","author":"Ghorbani Amirata","year":"2019","unstructured":"Amirata Ghorbani and James Zou . 2019 . Data Shapley: Equitable Valuation of Data for Machine Learning. In International Conference on Machine Learning. 2242--2251 . Amirata Ghorbani and James Zou. 2019. Data Shapley: Equitable Valuation of Data for Machine Learning. In International Conference on Machine Learning. 2242--2251."},{"key":"e_1_3_2_2_13_1","unstructured":"The Gradient. June 24 2020. Lessons from the PULSE Model and Discussion. https:\/\/thegradient.pub\/pulse-lessons\/.  The Gradient. June 24 2020. Lessons from the PULSE Model and Discussion. https:\/\/thegradient.pub\/pulse-lessons\/."},{"key":"e_1_3_2_2_14_1","volume-title":"Bargaining foundations of Shapley value. Econometrica: Journal of the Econometric Society","author":"Gul Faruk","year":"1989","unstructured":"Faruk Gul . 1989. Bargaining foundations of Shapley value. Econometrica: Journal of the Econometric Society ( 1989 ), 81--95. Faruk Gul. 1989. Bargaining foundations of Shapley value. Econometrica: Journal of the Econometric Society (1989), 81--95."},{"key":"e_1_3_2_2_15_1","unstructured":"Herbert Hamers Bart Husslage R Lindelauf Tjeerd Campen etal 2016. A New Approximation Method for the Shapley Value Applied to the WTC 9\/11 Terrorist Attack. Technical Report.  Herbert Hamers Bart Husslage R Lindelauf Tjeerd Campen et al. 2016. A New Approximation Method for the Shapley Value Applied to the WTC 9\/11 Terrorist Attack. Technical Report."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2021.100241"},{"key":"e_1_3_2_2_17_1","volume-title":"What Do Compressed Deep Neural Networks Forget? arXiv preprint arXiv:1911.05248","author":"Hooker Sara","year":"2019","unstructured":"Sara Hooker , Aaron Courville , Gregory Clark , Yann Dauphin , and Andrea Frome . 2019. What Do Compressed Deep Neural Networks Forget? arXiv preprint arXiv:1911.05248 ( 2019 ). Sara Hooker, Aaron Courville, Gregory Clark, Yann Dauphin, and Andrea Frome. 2019. What Do Compressed Deep Neural Networks Forget? arXiv preprint arXiv:1911.05248 (2019)."},{"key":"e_1_3_2_2_18_1","volume-title":"Characterising bias in compressed models. arXiv preprint arXiv:2010.03058","author":"Hooker Sara","year":"2020","unstructured":"Sara Hooker , Nyalleng Moorosi , Gregory Clark , Samy Bengio , and Emily Denton . 2020. Characterising bias in compressed models. arXiv preprint arXiv:2010.03058 ( 2020 ). Sara Hooker, Nyalleng Moorosi, Gregory Clark, Samy Bengio, and Emily Denton. 2020. Characterising bias in compressed models. arXiv preprint arXiv:2010.03058 (2020)."},{"key":"e_1_3_2_2_19_1","volume-title":"Nick Hynes, Nezihe Merve Gurel, Bo Li, Ce Zhang, Dawn Song, and Costas Spanos.","author":"Jia Ruoxi","year":"2019","unstructured":"Ruoxi Jia , David Dao , BoxinWang , Frances Ann Hubis , Nick Hynes, Nezihe Merve Gurel, Bo Li, Ce Zhang, Dawn Song, and Costas Spanos. 2019 . Towards Efficient Data Valuation Based on the Shapley Value . arXiv preprint arXiv:1902.10275 (2019). Ruoxi Jia, David Dao, BoxinWang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gurel, Bo Li, Ce Zhang, Dawn Song, and Costas Spanos. 2019. Towards Efficient Data Valuation Based on the Shapley Value. arXiv preprint arXiv:1902.10275 (2019)."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314287"},{"key":"e_1_3_2_2_21_1","first-page":"1","article-title":"An efficient explanation of individual classifications using game theory","author":"Igor Kononenko","year":"2010","unstructured":"Igor Kononenko et al. 2010 . An efficient explanation of individual classifications using game theory . Journal of Machine Learning Research 11 , Jan (2010), 1 -- 18 . Igor Kononenko et al. 2010. An efficient explanation of individual classifications using game theory. Journal of Machine Learning Research 11, Jan (2010), 1--18.","journal-title":"Journal of Machine Learning Research 11"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Lars Kotthoff Alexandre Fr\u00e9chette Tomasz P Michalak Talal Rahwan Holger H Hoos and Kevin Leyton-Brown. 2018. Quantifying Algorithmic Improvements over Time. In IJCAI. 5165--5171.  Lars Kotthoff Alexandre Fr\u00e9chette Tomasz P Michalak Talal Rahwan Holger H Hoos and Kevin Leyton-Brown. 2018. Quantifying Algorithmic Improvements over Time. In IJCAI. 5165--5171.","DOI":"10.24963\/ijcai.2018\/716"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_2_2_24_1","volume-title":"Consistent Individualized Feature Attribution for Tree Ensembles. arXiv preprint arXiv:1802.03888","author":"Erion Gabriel G","year":"2018","unstructured":"ScottMLundberg, Gabriel G Erion , and Su-In Lee . 2018. Consistent Individualized Feature Attribution for Tree Ensembles. arXiv preprint arXiv:1802.03888 ( 2018 ). ScottMLundberg, Gabriel G Erion, and Su-In Lee. 2018. Consistent Individualized Feature Attribution for Tree Ensembles. arXiv preprint arXiv:1802.03888 (2018)."},{"key":"e_1_3_2_2_25_1","unstructured":"Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems. 4765--4774.  Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems. 4765--4774."},{"key":"e_1_3_2_2_26_1","volume-title":"Bounding the estimation error of sampling-based Shapley value approximation. arXiv preprint arXiv:1306.4265","author":"Maleki Sasan","year":"2013","unstructured":"Sasan Maleki , Long Tran-Thanh , Greg Hines , Talal Rahwan , and Alex Rogers . 2013. Bounding the estimation error of sampling-based Shapley value approximation. arXiv preprint arXiv:1306.4265 ( 2013 ). Sasan Maleki, Long Tran-Thanh, Greg Hines, Talal Rahwan, and Alex Rogers. 2013. Bounding the estimation error of sampling-based Shapley value approximation. arXiv preprint arXiv:1306.4265 (2013)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00251"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/2512538.2512553"},{"key":"e_1_3_2_2_29_1","volume-title":"Values of large games II: Oceanic games. Mathematics of operations research 3, 4","author":"Milnor John Willard","year":"1978","unstructured":"John Willard Milnor and Lloyd S Shapley . 1978. Values of large games II: Oceanic games. Mathematics of operations research 3, 4 ( 1978 ), 290--307. John Willard Milnor and Lloyd S Shapley. 1978. Values of large games II: Oceanic games. Mathematics of operations research 3, 4 (1978), 290--307."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1137\/130936233"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_2_32_1","volume-title":"A value for n-person games. Contributions to the Theory of Games 2, 28","author":"Shapley Lloyd S","year":"1953","unstructured":"Lloyd S Shapley . 1953. A value for n-person games. Contributions to the Theory of Games 2, 28 ( 1953 ), 307--317. Lloyd S Shapley. 1953. A value for n-person games. Contributions to the Theory of Games 2, 28 (1953), 307--317."},{"key":"e_1_3_2_2_33_1","unstructured":"Lloyd S Shapley Alvin E Roth etal 1988. The Shapley value: essays in honor of Lloyd S. Shapley. Cambridge University Press.  Lloyd S Shapley Alvin E Roth et al. 1988. The Shapley value: essays in honor of Lloyd S. Shapley. Cambridge University Press."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Cathie Sudlow John Gallacher Naomi Allen Valerie Beral Paul Burton John Danesh Paul Downey Paul Elliott Jane Green Martin Landray etal 2015. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine 12 3 (2015) e1001779.  Cathie Sudlow John Gallacher Naomi Allen Valerie Beral Paul Burton John Danesh Paul Downey Paul Elliott Jane Green Martin Landray et al. 2015. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine 12 3 (2015) e1001779.","DOI":"10.1371\/journal.pmed.1001779"},{"key":"e_1_3_2_2_35_1","first-page":"12","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","volume":"4","author":"Szegedy Christian","year":"2017","unstructured":"Christian Szegedy , Sergey Ioffe , Vincent Vanhoucke , and Alexander A Alemi . 2017 . Inception-v4, inception-resnet and the impact of residual connections on learning . In AAAI , Vol. 4. 12 . Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi. 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, Vol. 4. 12.","journal-title":"AAAI"},{"key":"e_1_3_2_2_36_1","unstructured":"Venturebeat. June 26 2020. AI Weekly: A deep learning pioneer's teachable moment on AI bias. https:\/\/venturebeat.com\/2020\/06\/26\/ai-weekly-a-deeplearning-pioneers-teachable-moment-on-ai-bias\/.  Venturebeat. June 26 2020. AI Weekly: A deep learning pioneer's teachable moment on AI bias. https:\/\/venturebeat.com\/2020\/06\/26\/ai-weekly-a-deeplearning-pioneers-teachable-moment-on-ai-bias\/."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.230"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Tom Yan and A. Procaccia. 2020. If You Like Shapley Then You'll Love the Core.  Tom Yan and A. Procaccia. 2020. If You Like Shapley Then You'll Love the Core.","DOI":"10.1609\/aaai.v35i6.16721"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"James Zou and Londa Schiebinger. 2018. AI can be sexist and racist-it's time to make it fair.  James Zou and Londa Schiebinger. 2018. AI can be sexist and racist-it's time to make it fair.","DOI":"10.1038\/d41586-018-05707-8"}],"event":{"name":"AIES '21: AAAI\/ACM Conference on AI, Ethics, and Society","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence","AAAI"],"location":"Virtual Event USA","acronym":"AIES '21"},"container-title":["Proceedings of the 2021 AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3461702.3462574","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3461702.3462574","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:49:06Z","timestamp":1750193346000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3461702.3462574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,21]]},"references-count":39,"alternative-id":["10.1145\/3461702.3462574","10.1145\/3461702"],"URL":"https:\/\/doi.org\/10.1145\/3461702.3462574","relation":{},"subject":[],"published":{"date-parts":[[2021,7,21]]},"assertion":[{"value":"2021-07-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}