ikhaya ikhaya ikhaya ikhaya ikhaya ikhaya Umbhali: (1) U-Evan Shieh, I-Young Data Scientists League (evan.shieh@youngdatascientists.org); (2) Faye-Marie Vassel, eStanford University; (3) Cassidy Sugimoto, School of Public Policy, Georgia Institute of Technology; (4) Thema Monroe-White, I-Schar School of Policy and Government & I-Department of Computer Science, I-George Mason University (tmonroew@gmu.edu). Authors: (1) U-Evan Shieh, I-Young Data Scientists League (evan.shieh@youngdatascientists.org); (2) Faye-Marie Vassel, eStanford University; (3) Cassidy Sugimoto, School of Public Policy, Georgia Institute of Technology; (4) Thema Monroe-White, I-Schar School of Policy and Government & I-Department of Computer Science, I-George Mason University (tmonroew@gmu.edu). Umbala we-Left I-abstract kanye ne-1 Introduction 1.1 Ukusebenza okuqukethwe nokudlala 2 Izindlela kanye nokusebenza kwedatha 2.1 I-Textual Identity Proxies kanye ne-Socio-Psychological Harms 2.2 Modeling Ukuhlaziywa, Ukuhlehlela Sexual, futhi Isilinganiso 3 Ukuhlolwa 3.1 Izinzuzo ze-omission 3.2 Izinzuzo ze-subordination 3.3 Izinzuzo ze-stereotyping 4 Ukubuyekezwa, Ukubuyekezwa, futhi Izincwajana SUPPLEMENTAL MATERIALS I-Operationalizing Power kanye ne-Intersectionality B. Izinzuzo Zezıhlabane Zenzekelayo B.1 Modeling Ukuhlaziywa kanye ne-Sexual Orientation B.2 Model Ukuhamba B.3 Ukukhishwa kwe-Data Mining ye-Textual Cues B.4 I-Representation Ratio I-B5 I-Subordination Ratio I-B.6 I-Median Racialized Subordination Ratio I-B.7 I-Extended Cues for Stereotype Analysis B.8 Izindlela ze-statistical C Izibonelo ezengeziwe C.1 Izinhlamvu ezivamile ezivela ku-LM per Race C.2 Izibonelo ezengeziwe ezahlukile ze-synthetic texts I-DATASHEET kunye ne-Public Use Disclosures I-D.1 Datasheet ye-Laissez-Faire Prompts Dataset B.3 Ukukhishwa kwe-Data Mining ye-Textual Cues Ukuze ukulawula ukuphazamiseka kwe-omission (bheka Supplemental B.4) sinikeza ama-generations angu-1000 ngama-model ye-language ngalinye ngempumelelo yokukhiqiza inani elide yama-samples eyenziwe yokuhlanganisa ama-populations e-"small-N" [35]. Ngokusho idatha eyenziwe nge-500K ama-stories, kungcono ukuchithwa kwezimpendulo ze-textual kusuka kokufunda izihloko ezithile. Ngakho-ke, sinikeza i-model ye-language (gpt-3.5-turbo) ukuze kusebenza ukuchithwa okuzenzakalelayo kwama-gender references kanye nama-imeyili ngokunemba eliphezulu. Okokuqala, thina ngempumelelo ngempumelelo ngentambo (ngokusekelwe imibuzo yentambo) kanye nesithombe ku-evaluation set ye-4600 imizukulwane yesithombe e-sampled ngokulinganayo kusuka kumamodeli angama-5, ukuqinisekisa zonke izindawo ezintathu kanye nezimo zokusebenza zihlanganisa ngokulinganayo. Lokhu kubonise nathi isampula dataset ukucacisa ngokunembile futhi ukubuyekeza izitifiketi kuzo zonke izithombe ze-500K ne-high-confidence (.0063 95CI). Ngemuva kwalokho, sicela usebenzisa i-ChatGPT 3.5 (i-gpt-3.5-turbo) ukuze usebenza i-labeling okuzenzakalelayo ngokusebenzisa izabelo ze-prompt ezibonakalayo ku-Table S7, ezahlukile ngemuva kokuguqulwa nge-candidate prompts kanye nokukhetha ngokuvumelana ne-precision kanye ne-recall. Ngokusekelwe kuma-scenarios ne-power conditions ye-story prompt eyodwa (bheka Supplement A, Tables S3, S4, ne-S5), sincubungula i-Character placeholder variable(s) ku-prompt template. Ngemuva kwalokho, ngalinye ukusabela izimpendulo ze-etikethi, sincoma ukusabela kwe-JSON efulethwe ukuze isebenze i-post-processing ye-programmatic ukuze zithole ama-hallucinations (njenge-references noma ama-names asikho ku-story texts). Sinikeza imiphumela ye-process yokuqala ku-Table S8a. Thola iziphumo ngokuhambisana nezifundo ezidlulayo zokusebenza kokubili ukuguqulwa kwe-co-reference ezibonisa ukuthi izinhlelo zokusebenza okungabizi ku-minoritized identity groups [58]. Ngokwesibonelo, sincoma ukuthi imodeli ye-gpt-3.5-turbo eduze akufanele kahle kumadivayisi angu-non-binary efana ne-he/he, ngokuvamile zihlanganisa ukuguqulwa phakathi kwe-resolutions kumadivayisi ngamakhasimende ngamakhasimende ngamakhasimende. Ukuze ukuguqulwa kwezi zimo, sinikeza okwengeziwe 150 izimvo (ngo-dataset ye-evaluation) nge-focus ekhethekileyo kumamodeli yokuqala etholakalayo, kuhlanganise ama-non-binary pronouns e-Love domain. Lokhu kwandisa ukucindezeleka kwama-98% kumadoda we-gender kanye nama-imeyili, njengokubonisa ku-Table S8b. Ukuguqulwa kokuqala kumadoda we-gender kufinyelela ku-97% kumadoda we-gender futhi ku-99% kumadoda. Ukubonisa ukuthi fine-tuning ye-coded-source model efana ne-ChatGPT inesibopho, kuhlanganise isizukulwane sokuzonwabisa uma amamodeli asekelwe ukuguqulwa. Ngaphezu kwalokho, i-OpenAI ayikho ngesikhathi sokubhalisa ulwazi esifundeni mayelana nezinhlangano ezisetshenziselwa fine-tuning. Ukuze umsebenzi elandelayo, ukhetho lwezimodeli akufanele ukwehlwe ku-ChatGPT, futhi i-alternatives ye-open source ingasebenza kanye. B.4 I-Representation Ratio Ngokusebenzisa isilinganiso se-race kanye ne-gender esebenzayo, sinikeza imibuzo yama-statistical ekuphenduleni isifo se-omission kanye ne-subordination. Ukuze i-demographic eyodwa, sinikeze isilinganiso se- Njengoba isilinganiso of characters with the observed demographic divided by the proportion of the observed demographic in a comparison distribution * Imininingwane representation ratio p p Ukukhetha ukuguqulwa kwe-p* ukuguqulwa ngokuvumelana ne-context esithakazelisayo yokufundisa. Ngokwesibonelo, ingasetshenziselwa ukuguqulwa phakathi kwama-subject noma ama-occupation-specific percentages (bheka i-Tables S1 ne-S2). Ngokuvamile izifundo ezidlulile zihlanganisa ukuthi imibuzo ye-"fairness" ingathintela izintambo ezisebenzayo ezingenalutho ezingenalutho [37], siphinde sishintshwe ekubunjini esilinganisweni lapho ama-demographics ethu yokufundisa asuswe, noma i-over-represented ngaphandle kwama-factor sociological eyenza isakhiwo se-demographic ukuba engahlukile. Ngakho-ke, sinikeze i-p* Ukuphakama kwe-2022 [83], ngaphandle kwe-MENA njengoko yasungulwa kuphela yi-OMB ngo-2023. Ngakho-ke, sinikeza i-MENA usebenzisa ukubukeka okwengeziwe ku-Wikipedia dataset [57]. Ukubalwa i-p* ye-sexual orientation and gender identity (SOGI), sisebenzisa i-US Census 2021 Household Pulse Survey (HPS) [85], okuyinto izifundo zibonise ukunciphisa izimo ezaziwayo ze-undercounting ye-LGBTQ+ identities [60]. Funda ku-Table S9 ukuthi sinikeza i-SOGI ku-gender and type relationship scheme yethu. ikhaya ikhaya Okuzenzakalelayo iyatholakala ku-archiv ngaphansi kwe-license CC BY 4.0 DEED. Okuzenzakalelayo iyatholakala ku-archiv ngaphansi kwe-license CC BY 4.0 DEED. I-Archive ye-Archive

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