{"id":"https://openalex.org/W4386242301","doi":"https://doi.org/10.1145/3600211.3604697","title":"Measures of Disparity and their Efficient Estimation","display_name":"Measures of Disparity and their Efficient Estimation","publication_year":2023,"publication_date":"2023-08-08","ids":{"openalex":"https://openalex.org/W4386242301","doi":"https://doi.org/10.1145/3600211.3604697"},"language":"en","primary_location":{"id":"doi:10.1145/3600211.3604697","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3600211.3604697","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069880614","display_name":"Harvineet Singh","orcid":"https://orcid.org/0000-0003-0166-9331"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Harvineet Singh","raw_affiliation_strings":["Center for Data Science, New York University, USA"],"affiliations":[{"raw_affiliation_string":"Center for Data Science, New York University, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005061793","display_name":"Rumi Chunara","orcid":"https://orcid.org/0000-0002-5346-7259"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rumi Chunara","raw_affiliation_strings":["School of Global Public Health, New York University, USA"],"affiliations":[{"raw_affiliation_string":"School of Global Public Health, New York University, USA","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5069880614"],"corresponding_institution_ids":["https://openalex.org/I57206974"],"apc_list":null,"apc_paid":null,"fwci":0.5877,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.75409067,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"927","last_page":"938"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10446","display_name":"Income, Poverty, and Inequality","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10446","display_name":"Income, Poverty, and Inequality","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9800000190734863,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10804","display_name":"Health Systems, Economic Evaluations, Quality of Life","score":0.953000009059906,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6929577589035034},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.6672683358192444},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6489027738571167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6438671946525574},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.5813648104667664},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4865531027317047},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4695231020450592},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.46679267287254333},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.45868563652038574},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44422945380210876},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.44137436151504517},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4219121038913727},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34316694736480713},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33879074454307556},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23012489080429077},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.08521652221679688}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6929577589035034},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.6672683358192444},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6489027738571167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6438671946525574},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.5813648104667664},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4865531027317047},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4695231020450592},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.46679267287254333},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.45868563652038574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44422945380210876},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.44137436151504517},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4219121038913727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34316694736480713},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33879074454307556},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23012489080429077},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.08521652221679688},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3600211.3604697","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3600211.3604697","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.5899999737739563}],"awards":[{"id":"https://openalex.org/G5065328260","display_name":null,"funder_award_id":"1845487, 1922658","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5693414920","display_name":null,"funder_award_id":"1922658","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5786089488","display_name":null,"funder_award_id":"1845487","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337534","display_name":"National Institute on Minority Health and Health Disparities","ror":"https://ror.org/0493hgw16"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W6193819","https://openalex.org/W620038288","https://openalex.org/W1975503357","https://openalex.org/W1981457167","https://openalex.org/W1993616033","https://openalex.org/W2032562260","https://openalex.org/W2052654293","https://openalex.org/W2092773519","https://openalex.org/W2123697620","https://openalex.org/W2131112076","https://openalex.org/W2168736985","https://openalex.org/W2299453729","https://openalex.org/W2435667741","https://openalex.org/W2461825338","https://openalex.org/W2963718755","https://openalex.org/W2997591727","https://openalex.org/W3048849798","https://openalex.org/W3101206394","https://openalex.org/W3114015353","https://openalex.org/W3183205774","https://openalex.org/W3191938074","https://openalex.org/W4213052788","https://openalex.org/W4224080055","https://openalex.org/W4224093423","https://openalex.org/W4225006831","https://openalex.org/W4238788310","https://openalex.org/W4249743219","https://openalex.org/W4253904376","https://openalex.org/W4280548710","https://openalex.org/W4281609070","https://openalex.org/W4283166422","https://openalex.org/W4288096724","https://openalex.org/W4288359825","https://openalex.org/W4289751798","https://openalex.org/W4300667758","https://openalex.org/W4402958366"],"related_works":["https://openalex.org/W4388311650","https://openalex.org/W5922282","https://openalex.org/W1974056099","https://openalex.org/W4245343541","https://openalex.org/W2386077341","https://openalex.org/W563589758","https://openalex.org/W62490179","https://openalex.org/W2954004777","https://openalex.org/W2949638731","https://openalex.org/W2144630952"],"abstract_inverted_index":{"Quantifying":[0],"disparities,":[1],"that":[2,99,121,168],"is":[3,10,59],"differences":[4],"in":[5,14,20,76],"outcomes":[6],"among":[7],"population":[8],"groups,":[9,165],"an":[11],"important":[12],"task":[13],"public":[15],"health,":[16,154],"economics,":[17],"and":[18,153,155],"increasingly":[19],"machine":[21,77],"learning.":[22],"In":[23],"this":[24],"work,":[25],"we":[26,88,166],"study":[27],"the":[28,90,101,140,164,171],"question":[29],"of":[30,40,70,82,92,103],"how":[31],"to":[32,35,50,94,131],"collect":[33],"data":[34,109],"measure":[36],"disparities.":[37,64,125],"The":[38],"field":[39],"survey":[41],"statistics":[42],"provides":[43],"extensive":[44],"guidance":[45,61],"on":[46,142,163],"sample":[47,115,133],"sizes":[48,134],"necessary":[49],"accurately":[51],"estimate":[52],"quantities":[53],"such":[54],"as":[55],"averages.":[56],"However,":[57],"there":[58],"limited":[60],"for":[62,79,118,123,135,147],"estimating":[63],"We":[65,112,138],"consider":[66],"a":[67,107,156,160],"broad":[68],"class":[69],"disparity":[71,104],"metrics":[72],"including":[73],"those":[74],"used":[75,130,146],"learning":[78],"measuring":[80],"fairness":[81,136],"model":[83],"outputs.":[84],"For":[85],"each":[86],"metric,":[87],"derive":[89],"number":[91],"samples":[93],"be":[95,129],"collected":[96],"per":[97],"group":[98],"increases":[100],"precision":[102],"estimates":[105],"given":[106],"fixed":[108],"collection":[110],"budget.":[111],"also":[113],"provide":[114],"size":[116],"calculations":[117],"hypothesis":[119],"tests":[120],"check":[122],"significant":[124],"Our":[126],"methods":[127,141],"can":[128],"determine":[132],"evaluations.":[137],"validate":[139],"two":[143],"nationwide":[144],"surveys,":[145],"understanding":[148],"population-level":[149],"attributes":[150],"like":[151],"employment":[152],"prediction":[157],"model.":[158],"Absent":[159],"priori":[161],"information":[162],"find":[167],"equally":[169],"sampling":[170],"groups":[172],"typically":[173],"performs":[174],"well.":[175]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}