{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T03:33:10Z","timestamp":1782876790747,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467453","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:12:08Z","timestamp":1628748728000},"page":"95-105","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":50,"title":["How Interpretable and Trustworthy are GAMs?"],"prefix":"10.1145","author":[{"given":"Chun-Hao","family":"Chang","sequence":"first","affiliation":[{"name":"University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Tan","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Lengerich","sequence":"additional","affiliation":[{"name":"MIT, Broad Institute, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Goldenberg","sequence":"additional","affiliation":[{"name":"University of Toronto, Vector Institute, Hospital of Sick Children, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rich","family":"Caruana","sequence":"additional","affiliation":[{"name":"Microsoft Research, Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks.","author":"Angwin Julia","year":"2019","unstructured":"Julia Angwin , Jeff Larson , Surya Mattu , and Lauren Kirchner . 2019 . Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks. (2019). https:\/\/www.propublica.org\/article\/machine-biasrisk-assessments-in-criminal-sentencing Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2019. Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks. (2019). https:\/\/www.propublica.org\/article\/machine-biasrisk-assessments-in-criminal-sentencing"},{"key":"e_1_3_2_1_2_1","volume-title":"An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning","author":"Bauer Eric","year":"1999","unstructured":"Eric Bauer and Ron Kohavi . 1999. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning , Vol. 36 , 1 ( 1999 ). Eric Bauer and Ron Kohavi. 1999. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning , Vol. 36, 1 (1999)."},{"key":"e_1_3_2_1_3_1","volume-title":"Statistics and Computing","volume":"18","author":"Binder Harald","year":"2008","unstructured":"Harald Binder and Gerhard Tutz . 2008 . A comparison of methods for the fitting of generalized additive models . Statistics and Computing , Vol. 18 , 1 (2008). Harald Binder and Gerhard Tutz. 2008. A comparison of methods for the fitting of generalized additive models. Statistics and Computing , Vol. 18, 1 (2008)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Rich Caruana Yin Lou Johannes Gehrke Paul Koch Marc Sturm and Noemie Elhadad. 2015. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In KDD .  Rich Caruana Yin Lou Johannes Gehrke Paul Koch Marc Sturm and Noemie Elhadad. 2015. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In KDD .","DOI":"10.1145\/2783258.2788613"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In KDD .  Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In KDD .","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_6_1","volume-title":"Big Data","volume":"5","author":"Chouldechova Alexandra","year":"2017","unstructured":"Alexandra Chouldechova . 2017 . Fair prediction with disparate impact: A study of bias in recidivism prediction instruments . Big Data , Vol. 5 , 2 (2017). Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data , Vol. 5, 2 (2017)."},{"key":"e_1_3_2_1_7_1","series-title":"Springer Series on Challenges in Machine Learning: \"Explainable and Interpretable Models in Computer Vision and Machine Learning\" (2017)","volume-title":"Towards A Rigorous Science of Interpretable Machine Learning","author":"Doshi-Velez Finale","unstructured":"Finale Doshi-Velez and Been Kim . 2017. Towards A Rigorous Science of Interpretable Machine Learning . Springer Series on Challenges in Machine Learning: \"Explainable and Interpretable Models in Computer Vision and Machine Learning\" (2017) . Finale Doshi-Velez and Been Kim. 2017. Towards A Rigorous Science of Interpretable Machine Learning. Springer Series on Challenges in Machine Learning: \"Explainable and Interpretable Models in Computer Vision and Machine Learning\" (2017)."},{"key":"e_1_3_2_1_8_1","unstructured":"Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml  Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"e_1_3_2_1_9_1","unstructured":"Kenneth Dwyer and Robert Holte. 2007. Decision Tree Instability and Active Learning. In ECML .  Kenneth Dwyer and Robert Holte. 2007. Decision Tree Instability and Active Learning. In ECML ."},{"key":"e_1_3_2_1_10_1","volume-title":"Generalized Additive Models","author":"Hastie Trevor","unstructured":"Trevor Hastie and Rob Tibshirani . 1990. Generalized Additive Models . Chapman and Hall\/CRC. Trevor Hastie and Rob Tibshirani. 1990. Generalized Additive Models .Chapman and Hall\/CRC."},{"key":"e_1_3_2_1_11_1","volume-title":"Statistical Methods in Medical Research","volume":"4","author":"Hastie Trevor","year":"1995","unstructured":"Trevor Hastie and Robert Tibshirani . 1995 . Generalized additive models for medical research . Statistical Methods in Medical Research , Vol. 4 , 3 (1995). Trevor Hastie and Robert Tibshirani. 1995. Generalized additive models for medical research. Statistical Methods in Medical Research , Vol. 4, 3 (1995)."},{"key":"e_1_3_2_1_12_1","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"Hastie Trevor","unstructured":"Trevor Hastie , Robert Tibshirani , and Jerome Friedman . 2009. The elements of statistical learning: data mining, inference, and prediction . Springer Science & Business Media . Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction .Springer Science & Business Media."},{"key":"e_1_3_2_1_13_1","volume-title":"An Evaluation of the Doctor-Interpretability of Generalized Additive Models with Interactions. In Machine Learning for Healthcare Conference .","author":"Hegselmann Stefan","year":"2020","unstructured":"Stefan Hegselmann , Thomas Volkert , Hendrik Ohlenburg , Antje Gottschalk , Martin Dugas , and Christian Ertmer . 2020 . An Evaluation of the Doctor-Interpretability of Generalized Additive Models with Interactions. In Machine Learning for Healthcare Conference . Stefan Hegselmann, Thomas Volkert, Hendrik Ohlenburg, Antje Gottschalk, Martin Dugas, and Christian Ertmer. 2020. An Evaluation of the Doctor-Interpretability of Generalized Additive Models with Interactions. In Machine Learning for Healthcare Conference ."},{"key":"e_1_3_2_1_14_1","volume-title":"Please Stop Permuting Features: An Explanation and Alternatives. arXiv preprint arXiv:1905.03151","author":"Hooker Giles","year":"2019","unstructured":"Giles Hooker and Lucas Mentch . 2019. Please Stop Permuting Features: An Explanation and Alternatives. arXiv preprint arXiv:1905.03151 ( 2019 ). Giles Hooker and Lucas Mentch. 2019. Please Stop Permuting Features: An Explanation and Alternatives. arXiv preprint arXiv:1905.03151 (2019)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0164571"},{"key":"e_1_3_2_1_16_1","volume-title":"Generalized additive models to capture the death rates in Canada COVID-19. arXiv preprint arXiv:1702.08608","author":"Izadi Farzali","year":"2020","unstructured":"Farzali Izadi . 2020. Generalized additive models to capture the death rates in Canada COVID-19. arXiv preprint arXiv:1702.08608 ( 2020 ). Farzali Izadi. 2020. Generalized additive models to capture the death rates in Canada COVID-19. arXiv preprint arXiv:1702.08608 (2020)."},{"key":"e_1_3_2_1_17_1","volume-title":"Leo Anthony Celi, and Roger G Mark","author":"Johnson Alistair EW","year":"2016","unstructured":"Alistair EW Johnson , Tom J Pollard , Lu Shen , H Lehman Li-Wei , Mengling Feng , Mohammad Ghassemi , Benjamin Moody , Peter Szolovits , Leo Anthony Celi, and Roger G Mark . 2016 . MIMIC-III, a freely accessible critical care database. Scientific Data , Vol. 3 , 1 (2016). Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-Wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific Data , Vol. 3, 1 (2016)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3233231"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Yin Lou Rich Caruana and Johannes Gehrke. 2012. Intelligible models for classification and regression. In KDD .  Yin Lou Rich Caruana and Johannes Gehrke. 2012. Intelligible models for classification and regression. In KDD .","DOI":"10.1145\/2339530.2339556"},{"key":"e_1_3_2_1_20_1","unstructured":"Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In NeurIPS .  Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In NeurIPS ."},{"key":"e_1_3_2_1_21_1","volume-title":"A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635","author":"Mehrabi Ninareh","year":"2019","unstructured":"Ninareh Mehrabi , Fred Morstatter , Nripsuta Saxena , Kristina Lerman , and Aram Galstyan . 2019. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 ( 2019 ). Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 (2019)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"M Arthur Munson and Rich Caruana. 2009. On feature selection bias-variance and bagging. In ECML PKDD .  M Arthur Munson and Rich Caruana. 2009. On feature selection bias-variance and bagging. In ECML PKDD .","DOI":"10.1007\/978-3-642-04174-7_10"},{"key":"e_1_3_2_1_23_1","volume-title":"InterpretML: A Unified Framework for Machine Learning Interpretability. arXiv preprint arXiv:1909.09223","author":"Nori Harsha","year":"2019","unstructured":"Harsha Nori , Samuel Jenkins , Paul Koch , and Rich Caruana . 2019. InterpretML: A Unified Framework for Machine Learning Interpretability. arXiv preprint arXiv:1909.09223 ( 2019 ). Harsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana. 2019. InterpretML: A Unified Framework for Machine Learning Interpretability. arXiv preprint arXiv:1909.09223 (2019)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.6876"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2015.1073155"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2019.104987"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Marco Tulio Ribeiro Sameer Singh and Carlos Guestrin. 2016. \u201cWhy Should I Trust You?\": Explaining the Predictions of Any Classifier. In KDD .  Marco Tulio Ribeiro Sameer Singh and Carlos Guestrin. 2016. \u201cWhy Should I Trust You?\": Explaining the Predictions of Any Classifier. In KDD .","DOI":"10.18653\/v1\/N16-3020"},{"key":"e_1_3_2_1_28_1","volume-title":"The optimal cut-off point of vitamin D for pregnancy outcomes using a generalized additive model. Clinical Nutrition","author":"Rostami Maryam","year":"2020","unstructured":"Maryam Rostami , Masoumeh Simbar , Mina Amiri , Razieh Bidhendi-Yarandi , Farhad Hosseinpanah , and Fahimeh Ramezani Tehrani . 2020. The optimal cut-off point of vitamin D for pregnancy outcomes using a generalized additive model. Clinical Nutrition ( 2020 ). Maryam Rostami, Masoumeh Simbar, Mina Amiri, Razieh Bidhendi-Yarandi, Farhad Hosseinpanah, and Fahimeh Ramezani Tehrani. 2020. The optimal cut-off point of vitamin D for pregnancy outcomes using a generalized additive model. Clinical Nutrition (2020)."},{"key":"e_1_3_2_1_29_1","volume-title":"Additive models with trend filtering. The Annals of Statistics","author":"Sadhanala Veeranjaneyulu","year":"2019","unstructured":"Veeranjaneyulu Sadhanala and Ryan J Tibshirani . 2019. Additive models with trend filtering. The Annals of Statistics ( 2019 ). Veeranjaneyulu Sadhanala and Ryan J Tibshirani. 2019. Additive models with trend filtering. The Annals of Statistics (2019)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.14419\/ijasp.v1i3.1022"},{"key":"e_1_3_2_1_31_1","volume-title":"Horticulture Research","volume":"4","author":"Min Thaw Saw Nay Min","year":"2017","unstructured":"Nay Min Min Thaw Saw , Claudio Moser , Stefan Martens , and Pietro Franceschi . 2017 . Applying generalized additive models to unravel dynamic changes in anthocyanin biosynthesis in methyl jasmonate elicited grapevine (Vitis vinifera cv. Gamay) cell cultures . Horticulture Research , Vol. 4 , 1 (2017). Nay Min Min Thaw Saw, Claudio Moser, Stefan Martens, and Pietro Franceschi. 2017. Applying generalized additive models to unravel dynamic changes in anthocyanin biosynthesis in methyl jasmonate elicited grapevine (Vitis vinifera cv. Gamay) cell cultures. Horticulture Research , Vol. 4, 1 (2017)."},{"key":"#cr-split#-e_1_3_2_1_32_1.1","unstructured":"Daniel Serv\u00e9n and Charlie Brummitt. 2018. pyGAM: Generalized Additive Models in Python. https:\/\/doi.org\/10.5281\/zenodo.1208723 10.5281\/zenodo.1208723"},{"key":"#cr-split#-e_1_3_2_1_32_1.2","unstructured":"Daniel Serv\u00e9n and Charlie Brummitt. 2018. pyGAM: Generalized Additive Models in Python. https:\/\/doi.org\/10.5281\/zenodo.1208723"},{"key":"e_1_3_2_1_33_1","volume-title":"Statist. Sci.","volume":"25","author":"Shmueli Galit","year":"2010","unstructured":"Galit Shmueli . 2010 . To explain or to predict ? Statist. Sci. , Vol. 25 , 3 (2010). Galit Shmueli. 2010. To explain or to predict? Statist. Sci. , Vol. 25, 3 (2010)."},{"key":"e_1_3_2_1_34_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 .  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 .","DOI":"10.1145\/3375627.3375830"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-8-25"},{"key":"e_1_3_2_1_36_1","volume-title":"Learning global additive explanations for neural nets using model distillation. arXiv preprint arXiv:1801.08640","author":"Tan Sarah","year":"2018","unstructured":"Sarah Tan , Rich Caruana , Giles Hooker , Paul Koch , and Albert Gordo . 2018b. Learning global additive explanations for neural nets using model distillation. arXiv preprint arXiv:1801.08640 ( 2018 ). Sarah Tan, Rich Caruana, Giles Hooker, Paul Koch, and Albert Gordo. 2018b. Learning global additive explanations for neural nets using model distillation. arXiv preprint arXiv:1801.08640 (2018)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278725"},{"key":"e_1_3_2_1_38_1","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani Robert","year":"1996","unstructured":"Robert Tibshirani . 1996 . Regression shrinkage and selection via the lasso . Journal of the Royal Statistical Society: Series B , Vol. 58 , 1 (1996). Robert Tibshirani. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B , Vol. 58, 1 (1996).","journal-title":"Journal of the Royal Statistical Society: Series B"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00490.x"},{"key":"e_1_3_2_1_40_1","volume-title":"A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem. The Annals of Statistics","author":"Wahba Grace","year":"1985","unstructured":"Grace Wahba . 1985. A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem. The Annals of Statistics ( 1985 ). Grace Wahba. 1985. A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem. The Annals of Statistics (1985)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Grace Wahba. 1990. Spline models for observational data .SIAM.  Grace Wahba. 1990. Spline models for observational data .SIAM.","DOI":"10.1137\/1.9781611970128"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2010.00749.x"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.6339"},{"key":"e_1_3_2_1_44_1","unstructured":"Rich Zemel Yu Wu Kevin Swersky Toni Pitassi and Cynthia Dwork. 2013. Learning fair representations. In ICML .  Rich Zemel Yu Wu Kevin Swersky Toni Pitassi and Cynthia Dwork. 2013. Learning fair representations. In ICML ."},{"key":"e_1_3_2_1_45_1","volume-title":"Unbiased Measurement of Feature Importance in Tree-Based Methods. TKDD","author":"Zhou Zhengze","year":"2021","unstructured":"Zhengze Zhou and Giles Hooker . 2021. Unbiased Measurement of Feature Importance in Tree-Based Methods. TKDD ( 2021 ). Zhengze Zhou and Giles Hooker. 2021. Unbiased Measurement of Feature Importance in Tree-Based Methods. TKDD (2021)."}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467453","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467453","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:37Z","timestamp":1750191517000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467453"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":46,"alternative-id":["10.1145\/3447548.3467453","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467453","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}