Human favoritism, not AI aversion: People’s perceptions (and bias) toward generative AI, human experts, and human–GAI collaboration in persuasive content generation
With the wide availability of large language models and generative AI, there are four primary paradigms for human–AI collaboration: human-only, AI-only (ChatGPT-4), augmented human (where a human makes the final decision with AI output as a reference), or augmented AI (where the AI makes the final d...
Uloženo v:
| Vydáno v: | Judgment and decision making Ročník 18 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Cambridge University Press
2023
|
| Témata: | |
| ISSN: | 1930-2975, 1930-2975 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | With the wide availability of large language models and generative AI, there are four primary paradigms for human–AI collaboration: human-only, AI-only (ChatGPT-4), augmented human (where a human makes the final decision with AI output as a reference), or augmented AI (where the AI makes the final decision with human output as a reference). In partnership with one of the world’s leading consulting firms, we enlisted professional content creators and ChatGPT-4 to create advertising content for products and persuasive content for campaigns following the aforementioned paradigms. First, we find that, contrary to the expectations of some of the existing algorithm aversion literature on conventional predictive AI, the content generated by generative AI and augmented AI is perceived as of higher quality than that produced by human experts and augmented human experts. Second, revealing the source of content production reduces—but does not reverse—the perceived quality gap between human- and AI-generated content. This bias in evaluation is predominantly driven by human favoritism rather than AI aversion: Knowing that the same content is created by a human expert increases its (reported) perceived quality, but knowing that AI is involved in the creation process does not affect its perceived quality. Further analysis suggests this bias is not due to a ‘quality prime’ as knowing the content they are about to evaluate comes from competent creators (e.g., industry professionals and state-of-the-art AI) without knowing exactly that the creator of each piece of content does not increase participants’ perceived quality. |
|---|---|
| AbstractList | With the wide availability of large language models and generative AI, there are four primary paradigms for human–AI collaboration: human-only, AI-only (ChatGPT-4), augmented human (where a human makes the final decision with AI output as a reference), or augmented AI (where the AI makes the final decision with human output as a reference). In partnership with one of the world’s leading consulting firms, we enlisted professional content creators and ChatGPT-4 to create advertising content for products and persuasive content for campaigns following the aforementioned paradigms. First, we find that, contrary to the expectations of some of the existing algorithm aversion literature on conventional predictive AI, the content generated by generative AI and augmented AI is perceived as of higher quality than that produced by human experts and augmented human experts. Second, revealing the source of content production reduces—but does not reverse—the perceived quality gap between human- and AI-generated content. This bias in evaluation is predominantly driven by human favoritism rather than AI aversion: Knowing that the same content is created by a human expert increases its (reported) perceived quality, but knowing that AI is involved in the creation process does not affect its perceived quality. Further analysis suggests this bias is not due to a ‘quality prime’ as knowing the content they are about to evaluate comes from competent creators (e.g., industry professionals and state-of-the-art AI) without knowing exactly that the creator of each piece of content does not increase participants’ perceived quality. |
| ArticleNumber | e41 |
| Author | Zhang, Yunhao Gosline, Renée |
| Author_xml | – sequence: 1 givenname: Yunhao orcidid: 0000-0001-8576-0612 surname: Zhang fullname: Zhang, Yunhao – sequence: 2 givenname: Renée surname: Gosline fullname: Gosline, Renée |
| BookMark | eNptkU1qHDEQhUVwIP7JKhfQ0iEz45LUP2rvjEnsAUOysNeipFbbGnqkQZLHyc53yCo3yLl8kqjHwQSTVRWP976ieAdkzwdvCfnAYMGAtSerfr3gwMVCtG_IPusEzHnX1nv_7O_IQUorgJp30O6T35f3a_R0wG2ILru0nlEfMj1bUtzamFzwp_SbDZvRPj3-SnRjo7GbXOREj9H3VDtMH2kODxh7emu9jZjd1hbAjN7t0PZ7CeU0o5N9Jz09_rwoB0wYR9RhCgRPnZ_g6R7TFDfBZ-vzCzH4I_J2wDHZ93_nIbn58vn6_HJ-9fVieX52NTei6fJcaDEMA1ZQYQ0MtJZ1w5q-FbIzCD1KjVWNeqgkgtEaBiZ41xS9FsYAMnFIls_cPuBKbaJbY_yhAjq1E0K8VRizM6NVHGTddkJIqXnFpC6DcSsk7xsD1vLC-vTMMjGkFO3wwmOgpsJUKUxNhSnRFjd75TYu737PEd3438wfnl-gFQ |
| CitedBy_id | crossref_primary_10_1038_s41598_025_10358_7 crossref_primary_10_1080_02650487_2025_2458996 crossref_primary_10_1177_13548565241285742 crossref_primary_10_1108_JRIM_02_2025_0066 crossref_primary_10_1108_JHTI_01_2025_0126 crossref_primary_10_1073_pnas_2322823121 crossref_primary_10_1080_10447318_2025_2531284 crossref_primary_10_1016_j_copsyc_2024_101839 crossref_primary_10_3928_01484834_20250130_01 crossref_primary_10_1038_s41598_025_86623_6 crossref_primary_10_1145_3710998 crossref_primary_10_9728_dcs_2025_26_6_1517 crossref_primary_10_1108_IJSMS_06_2024_0147 crossref_primary_10_2139_ssrn_4782554 crossref_primary_10_1007_s43681_025_00667_y crossref_primary_10_1080_00218499_2025_2464307 crossref_primary_10_1016_j_jretconser_2025_104403 crossref_primary_10_1080_10641734_2025_2498996 crossref_primary_10_1007_s43681_025_00721_9 crossref_primary_10_1126_science_adq1814 crossref_primary_10_1371_journal_pone_0315011 crossref_primary_10_1007_s11747_024_01064_3 crossref_primary_10_1080_10447318_2025_2520997 crossref_primary_10_1177_10949968251322513 crossref_primary_10_1016_j_chb_2025_108761 crossref_primary_10_1111_all_16331 crossref_primary_10_1007_s11747_024_01044_7 crossref_primary_10_1108_JSM_10_2024_0539 crossref_primary_10_1007_s00146_024_02053_4 crossref_primary_10_1080_15252019_2025_2537019 crossref_primary_10_3390_app151910322 crossref_primary_10_1007_s00146_025_02490_9 crossref_primary_10_1007_s10660_025_09990_2 crossref_primary_10_1073_pnas_2401336121 crossref_primary_10_1080_07421222_2025_2487314 crossref_primary_10_1016_j_jretconser_2025_104496 crossref_primary_10_1007_s11301_025_00494_9 crossref_primary_10_4018_IJCRMM_381305 crossref_primary_10_2139_ssrn_4765222 crossref_primary_10_2196_73173 crossref_primary_10_1093_jcmc_zmaf013 crossref_primary_10_1007_s00146_025_02564_8 crossref_primary_10_5057_isase_2025_C000011 |
| Cites_doi | 10.1017/S1930297500002989 10.1037/xge0000033 10.1086/705716 10.1001/jama.2023.5321 10.1016/j.techfore.2016.08.019 10.1001/jamainternmed.2023.1838 10.1126/science.adh2586 10.1001/jamainternmed.2023.1835 10.1016/j.tics.2022.07.007 10.5465/amd.2023.0106 10.21203/rs.3.rs-3238396/v1 10.1257/aer.20160696 10.1016/j.obhdp.2018.12.005 10.1257/jep.33.2.31 10.1177/0022243719851788 10.2139/ssrn.4299576 10.1145/3491102.3517731 |
| ContentType | Journal Article |
| DBID | AAYXX CITATION DOA |
| DOI | 10.1017/jdm.2023.37 |
| DatabaseName | CrossRef DOAJ - Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Psychology |
| EISSN | 1930-2975 |
| ExternalDocumentID | oai_doaj_org_article_2085793388b2418b8b212e382d6c0ee2 10_1017_jdm_2023_37 |
| GroupedDBID | 09C 09D 0R~ 188 2UF 2WC 5VS 7WY 8FL 8G5 AAFWJ AASVR AAXMD AAYXX ABDBF ABGDZ ABIVO ABJNI ABUWG ABXAU ABXHF ACAJB ACDLN ACDXY ACHQT ACUHS ADBBV ADKIL ADVJH AEBAK AFFHD AFKRA AFKRZ AFMMW AFPKN AFZFC AGHGI AGTDA AHRGI AKMAY ALMA_UNASSIGNED_HOLDINGS ATFKH AZQEC BCNDV BENPR BEZIV BPHCQ C1A CCPQU CDVRH CITATION CNMHZ DWQXO E3Z EAD EAP EBS EJD EPL ESX FRNLG GNUQQ GROUPED_ABI_INFORM_RESEARCH GROUPED_DOAJ GUQSH H13 IAO ICO IEA IPNFZ IPY IPYYG ITC K60 K6~ KQ8 M0C M2O M~E OK1 P2P PHGZM PHGZT PIMPY PQBIZ PQBZA PQQKQ PROAC RCA RIG ROL TH9 TR2 TUS UZ2 JDM |
| ID | FETCH-LOGICAL-c369t-3b3fffa404a5010bb85616d7389ca0da8ba45abf48a0cbb0f13296da853cc0a13 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 54 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001112542000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1930-2975 |
| IngestDate | Fri Oct 03 12:43:03 EDT 2025 Sat Nov 29 01:34:57 EST 2025 Tue Nov 18 22:14:43 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | http://creativecommons.org/licenses/by/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c369t-3b3fffa404a5010bb85616d7389ca0da8ba45abf48a0cbb0f13296da853cc0a13 |
| ORCID | 0000-0001-8576-0612 |
| OpenAccessLink | https://doaj.org/article/2085793388b2418b8b212e382d6c0ee2 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_2085793388b2418b8b212e382d6c0ee2 crossref_primary_10_1017_jdm_2023_37 crossref_citationtrail_10_1017_jdm_2023_37 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-00-00 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – year: 2023 text: 2023-00-00 |
| PublicationDecade | 2020 |
| PublicationTitle | Judgment and decision making |
| PublicationYear | 2023 |
| Publisher | Cambridge University Press |
| Publisher_xml | – name: Cambridge University Press |
| References | Brynjolfsson (S1930297523000372_r22) 2023 S1930297523000372_r10 S1930297523000372_r21 McKendrick (S1930297523000372_r17) 2022; 15 S1930297523000372_r20 S1930297523000372_r15 S1930297523000372_r5 S1930297523000372_r6 S1930297523000372_r14 S1930297523000372_r3 S1930297523000372_r13 S1930297523000372_r4 S1930297523000372_r23 S1930297523000372_r12 S1930297523000372_r19 S1930297523000372_r1 S1930297523000372_r18 S1930297523000372_r2 S1930297523000372_r16 S1930297523000372_r9 S1930297523000372_r7 S1930297523000372_r8 |
| References_xml | – ident: S1930297523000372_r9 – ident: S1930297523000372_r5 doi: 10.1017/S1930297500002989 – ident: S1930297523000372_r7 – ident: S1930297523000372_r10 doi: 10.1037/xge0000033 – ident: S1930297523000372_r2 doi: 10.1086/705716 – ident: S1930297523000372_r13 doi: 10.1001/jama.2023.5321 – ident: S1930297523000372_r12 doi: 10.1016/j.techfore.2016.08.019 – ident: S1930297523000372_r4 doi: 10.1001/jamainternmed.2023.1838 – ident: S1930297523000372_r19 doi: 10.1126/science.adh2586 – ident: S1930297523000372_r15 doi: 10.1001/jamainternmed.2023.1835 – ident: S1930297523000372_r18 doi: 10.1016/j.tics.2022.07.007 – volume: 15 year: 2022 ident: S1930297523000372_r17 article-title: AI isn’t ready to make unsupervised decisions publication-title: Harvard Business Review – year: 2023 ident: S1930297523000372_r22 article-title: Generative AI at work (No. w31161). publication-title: National Bureau of Economic Research – ident: S1930297523000372_r14 – ident: S1930297523000372_r6 doi: 10.5465/amd.2023.0106 – ident: S1930297523000372_r21 doi: 10.21203/rs.3.rs-3238396/v1 – ident: S1930297523000372_r1 doi: 10.1257/aer.20160696 – ident: S1930297523000372_r23 doi: 10.1016/j.obhdp.2018.12.005 – ident: S1930297523000372_r3 doi: 10.1257/jep.33.2.31 – ident: S1930297523000372_r8 doi: 10.1177/0022243719851788 – ident: S1930297523000372_r20 doi: 10.2139/ssrn.4299576 – ident: S1930297523000372_r16 doi: 10.1145/3491102.3517731 |
| SSID | ssj0052907 |
| Score | 2.5432677 |
| Snippet | With the wide availability of large language models and generative AI, there are four primary paradigms for human–AI collaboration: human-only, AI-only... |
| SourceID | doaj crossref |
| SourceType | Open Website Enrichment Source Index Database |
| SubjectTerms | Algorithm Aversion Consumer Perception Generative AI Human Favortism |
| Title | Human favoritism, not AI aversion: People’s perceptions (and bias) toward generative AI, human experts, and human–GAI collaboration in persuasive content generation |
| URI | https://doaj.org/article/2085793388b2418b8b212e382d6c0ee2 |
| Volume | 18 |
| WOSCitedRecordID | wos001112542000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1930-2975 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0052907 issn: 1930-2975 databaseCode: DOA dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1930-2975 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0052907 issn: 1930-2975 databaseCode: M~E dateStart: 20060101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8QwEA4iHryIT3wzBw8qW80mTZv1puLroHhQ8FbyaETR7uLWBS-y_8GT_8Dftb_ESVrXFQQvXloahmnIDPNi8g0hG5isCQztTWTj1FernIikdc3I5VQo2pJWMB2GTaQXF_LmpnU5MurL94RV8MDVwe2GGZKYdUup0dlIja8my7lkNjE0z4P1xajnK5mqbLBgmPPVt_E8QPS99ZfOGd_x485H_M8ITH_wJ8fTZKoOBGG_2sAMGcuLWTI5tEcvc-QjFNjBqV47IA89NqBol7B_BqpXVbn24DI0gA_6713ofLeowKYqLOg71d2CMvTFwm2Al_a2DRk0IIzmgwDvX3Yb4MnD0qD_doI_-KEdcFd45t1n5VvdwTe3o6cacmwX8-T6-Ojq8DSqBytEhietMuKaO-dUTGMlMB_TWmIUldgUgxejqFVSq1go7WKpqNGaOj-NPsF1wY2hqskXyHjRLvJFAjTOE5GmvMlYK2aWq5QZY_DbotS0TpfI9tdxZ6ZGHffDLx6yqr0szVA2mZdNxpF4Y0jcqcA2fic78HIbkniE7LCAepPVepP9pTfL_8FkhUz6PVUlmVUyXj4952tkwvRQKZ7Wg0ri8_z16BMBVusc |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Human+favoritism%2C+not+AI+aversion%3A+People%E2%80%99s+perceptions+%28and+bias%29+toward+generative+AI%2C+human+experts%2C+and+human%E2%80%93GAI+collaboration+in+persuasive+content+generation&rft.jtitle=Judgment+and+decision+making&rft.au=Yunhao+Zhang&rft.au=Ren%C3%A9e+Gosline&rft.date=2023&rft.pub=Cambridge+University+Press&rft.eissn=1930-2975&rft.volume=18&rft_id=info:doi/10.1017%2Fjdm.2023.37&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_2085793388b2418b8b212e382d6c0ee2 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1930-2975&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1930-2975&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1930-2975&client=summon |