{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T07:04:59Z","timestamp":1792739099940,"version":"4.5.0"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T00:00:00Z","timestamp":1724716800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Science of The Total Environment"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1016\/j.scitotenv.2024.175730","type":"journal-article","created":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T14:41:19Z","timestamp":1724510479000},"page":"175730","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"special_numbering":"C","title":["Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California"],"prefix":"10.1016","volume":"951","author":[{"given":"Seigi","family":"Karasaki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel","family":"Morello-Frosch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duncan","family":"Callaway","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.scitotenv.2024.175730_bb0005","first-page":"219","article-title":"The color of algorithms: an analysis and proposed research agenda for deterring algorithmic redlining artificial intelligence and predictive algorithms: why big data can Lead to big problems","volume":"46","author":"Allen","year":"2019","journal-title":"Fordham Urban Law J."},{"issue":"4","key":"10.1016\/j.scitotenv.2024.175730_bb0010","doi-asserted-by":"crossref","first-page":"603","DOI":"10.2105\/AJPH.2013.301664","article-title":"The drinking water disparities framework: on the origins and persistence of inequities in exposure","volume":"104","author":"Balazs","year":"2014","journal-title":"Am. J. Public Health"},{"issue":"9","key":"10.1016\/j.scitotenv.2024.175730_bb0015","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1289\/ehp.1002878","article-title":"Social disparities in nitrate-contaminated drinking water in California\u2019s San Joaquin Valley","volume":"119","author":"Balazs","year":"2011","journal-title":"Environ. Health Perspect."},{"issue":"1","key":"10.1016\/j.scitotenv.2024.175730_bb0020","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1186\/1476-069X-11-84","article-title":"Environmental justice implications of arsenic contamination in California\u2019s San Joaquin Valley: a cross-sectional, cluster-design examining exposure and compliance in community drinking water systems","volume":"11","author":"Balazs","year":"2012","journal-title":"Environ. Health"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0025","author":"Baston"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0030","author":"Beaudette"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0035","first-page":"90","article-title":"The distributive effects of risk prediction in environmental compliance: algorithmic design, environmental justice, and public policy","author":"Benami","year":"2021"},{"issue":"6464","key":"10.1016\/j.scitotenv.2024.175730_bb0040","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1126\/science.aaz3873","article-title":"Assessing risk, automating racism","volume":"366","author":"Benjamin","year":"2019","journal-title":"Science"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0045","series-title":"Race After Technology: Abolitionist Tools for the New Jim Code","author":"Benjamin","year":"2019"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0050","article-title":"The legacy of American apartheid and environmental racism","volume":"9","author":"Bullard","year":"1993","journal-title":"St. John\u2019s J. Leg. Comment."},{"key":"10.1016\/j.scitotenv.2024.175730_bb0055","author":"California State Water Resources Control Board"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0060","author":"California State Water Resources Control Board"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0065","author":"California State Water Resources Control Board"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0070","author":"California State Water Resources Control Board"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0075","author":"California State Water Resources Control Board"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0080","author":"California State Water Resources Control Board"},{"issue":"7","key":"10.1016\/j.scitotenv.2024.175730_bb0085","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0270746","article-title":"High-resolution gridded estimates of population sociodemographics from the 2020 census in California","volume":"17","author":"Depsky","year":"2022","journal-title":"PloS One"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0090","series-title":"Threats on tap: Widespread violations highlight need for investment in water infrastructure and protections","first-page":"1","author":"Fedinick","year":"2017"},{"issue":"5","key":"10.1016\/j.scitotenv.2024.175730_bb0095","doi-asserted-by":"crossref","first-page":"2843","DOI":"10.1021\/acs.est.1c01739","article-title":"Selecting data analytic and modeling methods to support air pollution and environmental justice investigations: a critical review and guidance framework","volume":"56","author":"Gardner-Frolick","year":"2022","journal-title":"Environ. Sci. Technol."},{"issue":"4","key":"10.1016\/j.scitotenv.2024.175730_bb0100","doi-asserted-by":"crossref","DOI":"10.1088\/1748-9326\/ac58aa","article-title":"Safe drinking water for small low-income communities: the long road from violation to remediation","volume":"17","author":"Glade","year":"2022","journal-title":"Environ. Res. Lett."},{"key":"10.1016\/j.scitotenv.2024.175730_bb0105","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google earth engine: planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"issue":"7985","key":"10.1016\/j.scitotenv.2024.175730_bb0110","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1038\/d41586-023-03316-8","article-title":"Garbage in, garbage out: mitigating risks and maximizing benefits of AI in research","volume":"623","author":"Hanson","year":"2023","journal-title":"Nature"},{"issue":"10","key":"10.1016\/j.scitotenv.2024.175730_bb0115","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41893-018-0142-9","article-title":"Machine learning for environmental monitoring","volume":"1","author":"Hino","year":"2018","journal-title":"Nat. Sustain."},{"issue":"12","key":"10.1016\/j.scitotenv.2024.175730_bb0120","doi-asserted-by":"crossref","DOI":"10.1088\/1748-9326\/ab4e55","article-title":"Machine learning and artificial intelligence to aid climate change research and preparedness","volume":"14","author":"Huntingford","year":"2019","journal-title":"Environ. Res. Lett."},{"issue":"2","key":"10.1016\/j.scitotenv.2024.175730_bb0125","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1038\/s42256-024-00793-y","article-title":"Mitigating allocative tradeoffs and harms in an environmental justice data tool","volume":"6","author":"Huynh","year":"2024","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.scitotenv.2024.175730_bb0130","author":"Kuhn"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2021.150046","article-title":"Identifying schools at high-risk for elevated lead in drinking water using only publicly available data","volume":"803","author":"Lobo","year":"2022","journal-title":"Sci. Total Environ."},{"issue":"8","key":"10.1016\/j.scitotenv.2024.175730_bb0140","doi-asserted-by":"crossref","first-page":"5012","DOI":"10.1021\/acs.est.0c05239","article-title":"Machine learning models of arsenic in Private Wells throughout the conterminous United States as a tool for exposure assessment in human health studies","volume":"55","author":"Lombard","year":"2021","journal-title":"Environ. Sci. Technol."},{"issue":"3","key":"10.1016\/j.scitotenv.2024.175730_bb0145","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1111\/tran.12441","article-title":"Thinking algorithmically: the making of hegemonic knowledge in climate governance","volume":"46","author":"Machen","year":"2021","journal-title":"Trans. Inst. Br. Geogr."},{"issue":"10","key":"10.1016\/j.scitotenv.2024.175730_bb0150","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.2105\/AJPH.2018.304621","article-title":"Drinking water violations and environmental justice in the United States, 2011\u20132015","volume":"108","author":"McDonald","year":"2018","journal-title":"Am. J. Public Health"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0155","doi-asserted-by":"crossref","DOI":"10.1017\/eds.2022.5","article-title":"Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science","volume":"1","author":"McGovern","year":"2022","journal-title":"Environ. Data Sci."},{"key":"10.1016\/j.scitotenv.2024.175730_bb0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.envres.2021.112146","article-title":"A new approach to a legacy concern: evaluating machine-learned Bayesian networks to predict childhood lead exposure risk from community water systems","volume":"204","author":"Mulhern","year":"2022","journal-title":"Environ. Res."},{"issue":"6464","key":"10.1016\/j.scitotenv.2024.175730_bb0165","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"issue":"9","key":"10.1016\/j.scitotenv.2024.175730_bb0170","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1038\/s41591-020-1020-3","article-title":"Those designing healthcare algorithms must become actively anti-racist","volume":"26","author":"Owens","year":"2020","journal-title":"Nat. Med."},{"issue":"1","key":"10.1016\/j.scitotenv.2024.175730_bb0175","doi-asserted-by":"crossref","first-page":"88","DOI":"10.2105\/AJPH.2021.306561","article-title":"Inequities in drinking water quality among domestic well communities and community water systems, California, 2011\u20132019","volume":"112","author":"Pace","year":"2022","journal-title":"Am. J. Public Health"},{"issue":"1","key":"10.1016\/j.scitotenv.2024.175730_bb0180","doi-asserted-by":"crossref","first-page":"439","DOI":"10.32614\/RJ-2018-009","article-title":"Simple features for R: standardized support for spatial vector data","volume":"10","author":"Pebesma","year":"2018","journal-title":"R J."},{"key":"10.1016\/j.scitotenv.2024.175730_bb0185","series-title":"R: A Language and Environment for Statistical Computing","author":"R Core Team","year":"2023"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0190","series-title":"Information Retrieval","author":"van Rijsbergen","year":"1979"},{"issue":"2","key":"10.1016\/j.scitotenv.2024.175730_bb0195","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1111\/1468-2427.12833","article-title":"Geographies of algorithmic violence: redlining the Smart City","volume":"44","author":"Safransky","year":"2020","journal-title":"Int. J. Urban Reg. Res."},{"issue":"3","key":"10.1016\/j.scitotenv.2024.175730_bb0200","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets","volume":"10","author":"Saito","year":"2015","journal-title":"PloS One"},{"issue":"1","key":"10.1016\/j.scitotenv.2024.175730_bb0205","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s12940-018-0442-6","article-title":"Environmental justice and drinking water quality: are there socioeconomic disparities in nitrate levels in U.S. drinking water?","volume":"18","author":"Schaider","year":"2019","journal-title":"Environ. Health"},{"issue":"4","key":"10.1016\/j.scitotenv.2024.175730_bb0210","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1002\/j.1551-8833.1999.tb08617.x","article-title":"Capacity development: the small system perspective","volume":"91","author":"Soelter","year":"1999","journal-title":"J. AWWA"},{"issue":"1","key":"10.1016\/j.scitotenv.2024.175730_bb0215","doi-asserted-by":"crossref","first-page":"2468","DOI":"10.1038\/s41467-020-15871-z","article-title":"AI for social good: unlocking the opportunity for positive impact","volume":"11","author":"Toma\u0161ev","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.scitotenv.2024.175730_bb0220","author":"United States Department of Agriculture, National Agricultural Statistics Service"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0225","author":"United States Department of Agriculture Natural Resources Conservation Service"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0230","author":"United States Environmental Protection Agency"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0235","author":"United States Environmental Protection Agency"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0240","author":"United States Geological Service"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0245","series-title":"Unreliable State Data Limit EPA\u2019s Ability to Target Enforcement Priorities and Communicate Water Systems\u2019 Performance","first-page":"1","author":"United States Government Accountability Office","year":"2011"},{"key":"10.1016\/j.scitotenv.2024.175730_bb0250","author":"Veale"},{"issue":"7","key":"10.1016\/j.scitotenv.2024.175730_bb0255","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1080\/1369118X.2019.1596293","article-title":"When data justice and environmental justice meet: formulating a response to extractive logic through environmental data justice","volume":"22","author":"Vera","year":"2019","journal-title":"Inf. Commun. Soc."},{"key":"10.1016\/j.scitotenv.2024.175730_bb0260","author":"Walker"},{"issue":"7","key":"10.1016\/j.scitotenv.2024.175730_bb0265","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1080\/01900692.2018.1498103","article-title":"Artificial intelligence and the public sector\u2014applications and challenges","volume":"42","author":"Wirtz","year":"2019","journal-title":"Int. J. Public Adm."},{"key":"10.1016\/j.scitotenv.2024.175730_bb0270","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.bdr.2015.12.001","article-title":"Finding the best classification threshold in imbalanced classification","volume":"5","author":"Zou","year":"2016","journal-title":"Big Data Res."}],"container-title":["Science of The Total Environment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0048969724058868?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0048969724058868?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T21:41:22Z","timestamp":1786484482000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0048969724058868"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":54,"alternative-id":["S0048969724058868"],"URL":"https:\/\/doi.org\/10.1016\/j.scitotenv.2024.175730","relation":{},"ISSN":["0048-9697"],"issn-type":[{"value":"0048-9697","type":"print"}],"subject":[],"published":{"date-parts":[[2024,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California","name":"articletitle","label":"Article Title"},{"value":"Science of The Total Environment","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.scitotenv.2024.175730","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"175730"}}