{"id":"https://openalex.org/W7170161868","doi":"https://doi.org/10.1145/3811242.3819101","title":"Quantifying Regional AI Readiness Disparities: An Empirical Analysis of Indian States","display_name":"Quantifying Regional AI Readiness Disparities: An Empirical Analysis of Indian States","publication_year":2026,"publication_date":"2026-07-23","ids":{"openalex":"https://openalex.org/W7170161868","doi":"https://doi.org/10.1145/3811242.3819101"},"language":null,"primary_location":{"id":"doi:10.1145/3811242.3819101","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3811242.3819101","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3811242.3819101","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5143485356","display_name":"Nandha Sathiaseelan","orcid":"https://orcid.org/0009-0000-0549-5936"},"institutions":[{"id":"https://openalex.org/I4210122507","display_name":"The Sixth Form College Colchester","ror":"https://ror.org/039da5s33","country_code":"GB","type":"education","lineage":["https://openalex.org/I4210122507"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nandha Sathiaseelan","raw_affiliation_strings":["Hills Road Sixth Form College, Cambridge, United Kingdom"],"raw_orcid":"https://orcid.org/0009-0000-0549-5936","affiliations":[{"raw_affiliation_string":"Hills Road Sixth Form College, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I4210122507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008573786","display_name":"Jaideep Prabhu","orcid":"https://orcid.org/0000-0002-8919-9567"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jaideep Prabhu","raw_affiliation_strings":["Cambridge Judge Business School, Cambridge, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-8919-9567","affiliations":[{"raw_affiliation_string":"Cambridge Judge Business School, Cambridge, United Kingdom","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"330","last_page":"339"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/typology","display_name":"Typology","score":0.6093999743461609},{"id":"https://openalex.org/keywords/preparedness","display_name":"Preparedness","score":0.6015999913215637},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5701000094413757},{"id":"https://openalex.org/keywords/digital-divide","display_name":"Digital divide","score":0.37860000133514404},{"id":"https://openalex.org/keywords/national-interest","display_name":"National interest","score":0.3540000021457672},{"id":"https://openalex.org/keywords/diversity","display_name":"Diversity (politics)","score":0.3492000102996826},{"id":"https://openalex.org/keywords/empirical-evidence","display_name":"Empirical evidence","score":0.3483999967575073},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.3370000123977661},{"id":"https://openalex.org/keywords/human-capital","display_name":"Human capital","score":0.33640000224113464}],"concepts":[{"id":"https://openalex.org/C75795011","wikidata":"https://www.wikidata.org/wiki/Q917904","display_name":"Typology","level":2,"score":0.6093999743461609},{"id":"https://openalex.org/C2777042776","wikidata":"https://www.wikidata.org/wiki/Q4583103","display_name":"Preparedness","level":2,"score":0.6015999913215637},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5701000094413757},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3926999866962433},{"id":"https://openalex.org/C173655357","wikidata":"https://www.wikidata.org/wiki/Q244752","display_name":"Digital divide","level":3,"score":0.37860000133514404},{"id":"https://openalex.org/C2780932887","wikidata":"https://www.wikidata.org/wiki/Q622857","display_name":"National interest","level":3,"score":0.3540000021457672},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.3483999967575073},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C2776943663","wikidata":"https://www.wikidata.org/wiki/Q165687","display_name":"Human capital","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.31940001249313354},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C83646750","wikidata":"https://www.wikidata.org/wiki/Q193893","display_name":"Capital (architecture)","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C148383697","wikidata":"https://www.wikidata.org/wiki/Q1781695","display_name":"Regional science","level":1,"score":0.30300000309944153},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.296999990940094},{"id":"https://openalex.org/C68062652","wikidata":"https://www.wikidata.org/wiki/Q214693","display_name":"Social capital","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C2776475305","wikidata":"https://www.wikidata.org/wiki/Q29627","display_name":"Public interest","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.2924000024795532},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.2879999876022339},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2863999903202057},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C175025494","wikidata":"https://www.wikidata.org/wiki/Q179179","display_name":"Interest rate","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C12516257","wikidata":"https://www.wikidata.org/wiki/Q2504350","display_name":"Special Interest Group","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C56995899","wikidata":"https://www.wikidata.org/wiki/Q1126687","display_name":"Focus group","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C44725695","wikidata":"https://www.wikidata.org/wiki/Q288156","display_name":"Normative","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C39549134","wikidata":"https://www.wikidata.org/wiki/Q133080","display_name":"Public relations","level":1,"score":0.25870001316070557},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.25200000405311584},{"id":"https://openalex.org/C137996800","wikidata":"https://www.wikidata.org/wiki/Q762702","display_name":"Technological change","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3811242.3819101","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3811242.3819101","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3811242.3819101","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3811242.3819101","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2040484355","https://openalex.org/W2060738507","https://openalex.org/W2108795964","https://openalex.org/W2276382071","https://openalex.org/W2323881768","https://openalex.org/W2526781987","https://openalex.org/W2768273372","https://openalex.org/W3120151967","https://openalex.org/W3125498346","https://openalex.org/W3151605938","https://openalex.org/W4205325953","https://openalex.org/W4210406706","https://openalex.org/W4392447866","https://openalex.org/W4406316105","https://openalex.org/W6940896936"],"related_works":[],"abstract_inverted_index":{"While":[0],"global":[1],"debates":[2],"on":[3,9,112,197],"Artificial":[4],"Intelligence":[5],"(AI)":[6],"often":[7],"focus":[8],"existential":[10],"risks,":[11],"a":[12,32,83,100,141,167,174,209,238],"more":[13],"immediate":[14],"concern":[15],"for":[16,93],"developing":[17],"economies":[18],"is":[19,163],"that":[20,70,151,160,192],"uneven":[21,169],"AI":[22,37,50,65,85,194,215],"adoption":[23],"will":[24],"deepen":[25],"existing":[26],"regional":[27],"inequalities.":[28],"This":[29,157],"paper":[30],"presents":[31],"state-level":[33],"empirical":[34],"analysis":[35],"of":[36,60,144,177,229,236,242],"readiness":[38,108,122,131,134,146,225],"disparities":[39],"within":[40,248],"India,":[41],"quantifying":[42],"how":[43],"structural":[44],"preparedness":[45],"and":[46,55,76,79,109,123,129,135,140,147,218,226],"public":[47],"interest":[48,86,125,128,137,149,227],"in":[49,172,213],"co-vary":[51],"across":[52],"28":[53],"states":[54,178],"the":[56,153,223,234,243],"National":[57],"Capital":[58],"Territory":[59],"Delhi.":[61],"We":[62,190],"construct":[63],"an":[64],"Readiness":[66],"Composite":[67],"Score":[68],"(AIRCS)":[69],"integrates":[71],"educational":[72],"outcomes,":[73],"economic":[74],"capacity,":[75],"digital":[77,244],"infrastructure,":[78],"relate":[80],"it":[81],"to":[82,165,181,202,222],"normalised":[84],"index":[87],"derived":[88],"from":[89,104,246],"Google":[90],"Trends":[91],"data":[92],"AI-related":[94],"search":[95],"terms.":[96],"The":[97],"results":[98],"show":[99],"positive":[101],"but":[102],"far":[103],"deterministic":[105],"association":[106],"between":[107],"interest.":[110],"Building":[111],"this":[113],"joint":[114],"distribution,":[115],"we":[116,207],"identify":[117],"four":[118],"readiness\u2013interest":[119],"profiles:":[120],"high":[121,124,127],"leaders,":[126],"medium":[130,145,148],"aspirants,":[132],"low":[133,136],"priority":[138],"states,":[139],"large":[142],"band":[143],"regions":[150],"track":[152],"national":[154,193],"average":[155],"relationship.":[156],"typology":[158],"reveals":[159],"AI-driven":[161],"growth":[162],"likely":[164],"follow":[166],"highly":[168],"spatial":[170],"pattern,":[171],"which":[173,214],"small":[175],"number":[176],"are":[179,200,220],"positioned":[180],"capture":[182],"disproportionate":[183],"benefits":[184],"while":[185],"others":[186],"risk":[187],"long-term":[188],"exclusion.":[189],"argue":[191],"strategies":[195],"premised":[196],"universal":[198],"digitalisation":[199],"unlikely":[201],"correct":[203],"these":[204],"imbalances.":[205],"Instead,":[206],"propose":[208],"contextual":[210],"integration":[211],"framework":[212],"policies,":[216],"investments,":[217],"applications":[219],"tailored":[221],"specific":[224],"profiles":[228],"different":[230],"state":[231],"clusters,":[232],"with":[233],"goal":[235],"preventing":[237],"new,":[239],"AI-intensive":[240],"layer":[241],"divide":[245],"emerging":[247],"India.":[249]},"counts_by_year":[],"updated_date":"2026-07-24T06:07:23.975819","created_date":"2026-07-24T00:00:00"}