https://m2.mtmt.hu/ hun 2026-08-24 12:22 JournalArticle 36075196 VALIDATED true 0 false 2026-08-16T09:34:23.913+0000 2026-05-21T11:44:29.173+0000 2025-04-05T07:58:59.751+0000 true 10027725 Bokor Tamás /api/author/10027725 Author Bokor Tamás (Kommunikációkutatás) true 10027725 2026-08-14T10:57:43.397+0000 2025-12-28T09:23:20.880+0000 true 521 Szuper Admin /api/admin/521 Admin Szuper Admin (admin) true Admin 10101397 /api/admin/10101397 Tokai Tamás (Központi 2-3, admin) Tokai Tamás true true 2026-05-21T11:51:34.974+0000 true false true 24 24 /api/publicationtype/24 PublicationType Folyóiratcikk 1 true 24 JournalArticle true 10000059 Article true 24 24 /api/publicationtype/24 PublicationType Folyóiratcikk 1 true 24 JournalArticle /api/subtype/10000059 Szakcikk SubType Szakcikk (Folyóiratcikk) 101 true 10000059 true 1 /api/category/1 Category Tudományos true 1 Orbán, Ferenc true true /api/journal/10020629 REVIEWED AI AND SOCIETY: THE JOURNAL OF HUMAN-CENTERED SYSTEMS AND MACHINE INTELLIGENCE 0951-5666 1435-5655 true false 10020629 false 10020629 true 0951-5666 1435-5655 Journal FOREIGN 40 6 4955 4969 4955 15 2025 Artificial intelligence (AI) has seen rapid development over the past decade, leading to its integration into various aspects of human life. The ability to integrate AI systems hinges not solely on their technical efficacy but also on the perceptions held by users or decision-makers. Previous researches indicate that many people harbor concerns about AI, which can hinder the adoption of these technologies. This study uses a pre-registered survey experiment embedded in an online survey in Hungary ( N = 2100) to assess trust in AI-based Automated Decision-Making (ADM). Participants were presented with hypothetical decisions in four domains (medical diagnoses, hiring, transportation, and financial investments). In a split-ballot design, participants were randomly assigned to a control group with human involvement and an experimental group where decision were supported by AI-based ADM. The main results show that decisions supported by human intervention are perceived as more trustworthy than those made by ADM (except for financial investment). However, our treatment heterogeneity analysis indicates that these effects are not consistent across all segments of society. A good understanding of AI, low privacy concerns, and an open personality can mitigate the negative impact of AI assistance on trust. true 2025 true true true false false GREEN 2026-08-16 true https://doi.org/10.1007/s00146-025-02237-6 45 1 32 32 0 32 34 9 2 9 9 9 9 45 0 0 45 45 32 32 0 5 5 Folyóiratcikk Journal Article 24 30 Könyvrészlet Chapter in Book 25 2 Könyv Book 23 0 Egyéb konferenciaközlemény Conference paper 31 0 Egyéb konferenciakötet Conference proceedings 32 0 Oltalmi formák Protection forms 26 0 Disszertáció Thesis 28 1 Egyéb Miscellaneous 29 12 Alkotás Achievement 22 0 Kutatási adat Research data 33 0 Folyóiratcikk Journal Article 24 0 0 0 Könyvrészlet Chapter in Book 25 0 0 0 Könyv Book 23 0 0 0 Egyéb konferenciaközlemény Conference paper 31 0 0 0 Egyéb konferenciakötet Conference proceedings 32 0 0 0 Oltalmi formák Protection forms 26 0 0 0 Disszertáció Thesis 28 0 0 0 Egyéb Miscellaneous 29 0 0 0 Alkotás Achievement 22 0 0 0 Kutatási adat Research data 33 0 0 0 2025 0 11 11 11 11 2026 0 33 33 33 33 D1 false false false false 2025-07-04T07:59:00.637+0000 Lendület Recens Kutatócsoport (HUN-REN TK); Századvég Csoport 2 12 2 2 0 true 10101397 Language 10002 /api/language/10002 Angol Angol English true 2 true PersonAuthorship 126062391 /api/authorship/126062391 Orbán, Ferenc [Orbán, Ferenc (Politikai elemző), szerző] Századvég Csoport 1 0.5 true false false Author 10089341 /api/author/10089341 Orbán Ferenc (Politikai elemző) Orbán Ferenc true true Orbán Ferenc true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PersonAuthorship 126062392 /api/authorship/126062392 Stefkovics, Ádám [Stefkovics, Ádám (szociológia), szerző] Lendület Recens Kutatócsoport (HUN-REN TK); Századvég Csoport 2 0.5 false true false Author 10065427 /api/author/10065427 Stefkovics Ádám (szociológia) Stefkovics Ádám true true Stefkovics Ádám true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PublicationIdentifier 29199149 /api/publicationidentifier/29199149 DOI: 10.1007/s00146-025-02237-6 PlainSource 6 /api/publicationsource/6 DOI PublicationSourceType 10001 /api/publicationsourcetype/10001 DOI true true true DOI DOI https://doi.org/@@@ true true 6 true GREEN true IDENTICAL 10.1007/s00146-025-02237-6 https://doi.org/10.1007/s00146-025-02237-6 false true PublicationIdentifier 30330801 /api/publicationidentifier/30330801 WoS: 001456464900001 PlainSource 1 /api/publicationsource/1 WoS PublicationSourceType 10003 /api/publicationsourcetype/10003 Indexelő adatbázis false true true WoS WoS https://www.webofscience.com/wos/woscc/full-record/@@@ true true 1 true IDENTICAL 001456464900001 https://www.webofscience.com/wos/woscc/full-record/001456464900001 true true PublicationIdentifier 30330800 /api/publicationidentifier/30330800 Scopus: 105001865721 PlainSource 3 /api/publicationsource/3 Scopus PublicationSourceType 10003 /api/publicationsourcetype/10003 Indexelő adatbázis false true true Scopus Scopus http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-@@@ true true 3 true IDENTICAL 105001865721 http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-105001865721 true true PublicationIdentifier 29199150 /api/publicationidentifier/29199150 Egyéb URL: https://link.springer.com/10.1007/s00146-025-02237-6 PlainSource 40 /api/publicationsource/40 Egyéb URL PublicationSourceType 10006 /api/publicationsourcetype/10006 Link true true true Egyéb URL Other URL @@@ true true 40 true https://link.springer.com/10.1007/s00146-025-02237-6 https://link.springer.com/10.1007/s00146-025-02237-6 false true SjrRating 11591148 /api/sjrrating/11591148 sjr:D1 (2025) Scopus - Human-Computer Interaction AI AND SOCIETY: THE JOURNAL OF HUMAN-CENTERED SYSTEMS AND MACHINE INTELLIGENCE 0951-5666 1435-5655 16 0.1 journal RatingType 10002 /api/ratingtype/10002 sjr sjr true true ClassificationExternal 1709 /api/classificationexternal/1709 Scopus - Human-Computer Interaction true 1709 true D1 DIRECT true true Reference 65211638 /api/reference/65211638 1. Araujo T, Helberger N, Kruikemeier S, de Vreese CH (2020) In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI Soc 35(3):611–623. https://doi.org/10.1007/s00146-019-00931-w, DOI: 10.1007/s00146-019-00931-w 1 10.1007/s00146-019-00931-w false true Reference 65211639 /api/reference/65211639 2. Araujo T, Brosius A, Goldberg AC, Möller J, Vreese Cd (2023) Humans vs. AI: the role of trust, political attitudes, and individual characteristics on perceptions about automated decision making across Europe. Int J Commun 17:28 2 false true Reference 65211640 /api/reference/65211640 3. Bates D, Maechler M, Bolker B, Walker S, Christensen RHB, Singmann H, Dai B (2015) Package ‘lme4’. Convergence 12(1):2 3 false true Reference 65211641 /api/reference/65211641 4. Brauner P, Dalibor M, Jarke M, Kunze I, Koren I, Lakemeyer G, Ziefle M (2022) A computer science perspective on digital transformation in production. ACM Trans Internet Things 3(2):1–32. https://doi.org/10.1145/3502265, DOI: 10.1145/3502265 4 10.1145/3502265 false true Reference 65211642 /api/reference/65211642 5. Breton A (2002) Political extremism and rationality. Cambridge University Press, Cambridge, DOI: 10.1017/CBO9780511550478 5 10.1017/CBO9780511550478 false true Reference 65211643 /api/reference/65211643 6. Brkan M (2019) Do algorithms rule the world? Algorithmic decision-making and data protection in the framework of the GDPR and beyond. Int J Law Inf Technol 27(2):91–121. https://doi.org/10.1093/ijlit/eay017, DOI: 10.1093/ijlit/eay017 6 10.1093/ijlit/eay017 false true Reference 65211644 /api/reference/65211644 7. Burbach L, Halbach P, Plettenberg N, Nakayama J, Ziefle M, Calero Valdez A (2019) “Hey, Siri”, “Ok, Google”, “Alexa”. acceptance-relevant factors of virtual voice-assistants. 2019 IEEE international professional communication conference (ProComm) (pp. 101-111). Aachen, Germany: IEEE, DOI: 10.1109/ProComm.2019.00025 7 10.1109/ProComm.2019.00025 false true Reference 65211645 /api/reference/65211645 8. Byrnes JP, Miller DC, Schafer WD (1999) Gender differences in risk taking: A meta-analysis. Psychol Bull 125(3):367–383. https://doi.org/10.1037/0033-2909.125.3.367, DOI: 10.1037/0033-2909.125.3.367 8 10.1037/0033-2909.125.3.367 false true Reference 65211646 /api/reference/65211646 9. Cabiddu F, Moi L, Patriotta G, Allen DG (2022) Why do users trust algorithms? A review and conceptualization of initial trust and trust over time. Eur Manag J 40(5):685–706. https://doi.org/10.1016/j.emj.2022.06.001, DOI: 10.1016/j.emj.2022.06.001 9 10.1016/j.emj.2022.06.001 false true Reference 65211647 /api/reference/65211647 10. Cai X, Ning H, Dhelim S, Zhou R, Zhang T, Xu Y, Wan Y (2021) Robot and its living space: a roadmap for robot development based on the view of living space. Digit Commun Netw 7(4):505–517. https://doi.org/10.1016/j.dcan.2020.12.001, DOI: 10.1016/j.dcan.2020.12.001 10 10.1016/j.dcan.2020.12.001 false true Reference 65211648 /api/reference/65211648 11. Castelo N, Ward AF (2021) Conservatism predicts aversion to consequential artificial intelligence. PLoS One 16(12):e0261467. https://doi.org/10.1371/journal.pone.0261467, DOI: 10.1371/journal.pone.0261467 11 10.1371/journal.pone.0261467 false true Reference 65211649 /api/reference/65211649 12. Castelo N, Bos MW, Lehmann D (2019) Let the machine decide: when consumers trust or distrust algorithms. NIM Market Intell Rev 11(2):24–29. https://doi.org/10.2478/nimmir-2019-0012, DOI: 10.2478/nimmir-2019-0012 12 10.2478/nimmir-2019-0012 false true Reference 65211650 /api/reference/65211650 13. Choung H, David P, Ross A (2023) Trust and ethics in AI. AI Soc 38(2):733–745. https://doi.org/10.1007/s00146-022-01473-4, DOI: 10.1007/s00146-022-01473-4 13 10.1007/s00146-022-01473-4 false true Reference 65211651 /api/reference/65211651 14. Corea F (2019) AI knowledge map: how to classify AI technologies. In: Corea F (ed) An introduction to data: everything you need to know about AI, big data and data science. Springer International Publishing, Cham, DOI: 10.1007/978-3-030-04468-8 14 10.1007/978-3-030-04468-8 false true Reference 65211652 /api/reference/65211652 15. De Freitas J, Agarwal S, Schmitt B, Haslam N (2023) Psychological factors underlying attitudes toward AI tools. Nat Hum Behav 7(11):1845–1854. https://doi.org/10.1038/s41562-023-01734-2, DOI: 10.1038/s41562-023-01734-2 15 10.1038/s41562-023-01734-2 false true Reference 65211653 /api/reference/65211653 16. Devaraj S, Easley RF, Crant JM (2008) Research note-how does personality matter? Relating the five-factor model to technology acceptance and use. Inf Syst Res 19(1):93–105. https://doi.org/10.1287/isre.1070.0153, DOI: 10.1287/isre.1070.0153 16 10.1287/isre.1070.0153 false true Reference 65211654 /api/reference/65211654 17. Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: people erroneously avoid algorithms after seeing them err. J Exp Psychol Gen 144(1):114–126. https://doi.org/10.1037/xge0000033, DOI: 10.1037/xge0000033 17 10.1037/xge0000033 false true Reference 65211655 /api/reference/65211655 18. Dineen BR, Noe RA, Wang C (2004) Perceived fairness of web-based applicant screening procedures: weighing the rules of justice and the role of individual differences. Hum Resour Manage 43(2–3):127–145. https://doi.org/10.1002/hrm.20011, DOI: 10.1002/hrm.20011 18 10.1002/hrm.20011 false true Reference 65211656 /api/reference/65211656 19. Engler A (2021) We’re using AI more and more for hiring. how can we ensure it’s fair? World Economic Forum, Retrieved from https://www.weforum.org/agenda/2021/06/auditing-employment-algorithmsdiscrimination-fairness/ 19 false true Reference 65211657 /api/reference/65211657 20. Eubanks V (2018) Automating inequality: how high-tech tools profile, police, and punish the poor. St. Martin’s Press, New York, NY 20 false true Reference 65211658 /api/reference/65211658 21. European Commission (2017) Attitudes towards the impact of digitisation and automation on daily life: report. Publications Office of the European Union 21 false true Reference 65211659 /api/reference/65211659 22. European Union (2024) Regulation 22 false true Reference 65211660 /api/reference/65211660 23. (EU) 2024/1689 of the European Parliament and of the Council on Artificial Intelligence (AI Act) 23 false true Reference 65211661 /api/reference/65211661 24. Fehér K, Vicsek L, Deuze M (2024) Modeling AI trust for 2050: perspectives from media and info-communication experts. AI Soc. https://doi.org/10.1007/s00146-023-01827-6, DOI: 10.1007/s00146-023-01827-6 24 10.1007/s00146-023-01827-6 false true Reference 65211662 /api/reference/65211662 25. Fortuna P, Wróblewski Z, Gorbaniuk O (2023) The structure and correlates of anthropocentrism as a psychological construct. Curr Psychol 42(5):3630–3642. https://doi.org/10.1007/s12144-021-01835-z, DOI: 10.1007/s12144-021-01835-z 25 10.1007/s12144-021-01835-z false true Reference 65211663 /api/reference/65211663 26. Frederiksen M, Larsen CA, Lolle HL (2016) Education and trust: exploring the association across social relationships and nations. Acta Sociologica 59(4):293–308. https://doi.org/10.1177/0001699316658936, DOI: 10.1177/0001699316658936 26 10.1177/0001699316658936 false true Reference 65211664 /api/reference/65211664 27. Fujita H (2020) AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol Phys Technol 13(1):6–19. https://doi.org/10.1007/s12194-019-00552-4, DOI: 10.1007/s12194-019-00552-4 27 10.1007/s12194-019-00552-4 false true Reference 65211665 /api/reference/65211665 28. Gerdon F, Bach RL, Kern C, Kreuter F (2022) Social impacts of algorithmic decision-making: a research agenda for the social sciences. Big Data Soc 9(1):20539517221089304. https://doi.org/10.1177/20539517221089305, DOI: 10.1177/20539517221089305 28 10.1177/20539517221089305 false true Reference 65211666 /api/reference/65211666 29. Gillespie N, Lockey S, Curtis C, Pool J, Ali Akbari (2023) Trust in artificial intelligence: a global study (Tech. Rep.). Brisbane, Australia: The University of Queensland; KPMG Australia, DOI: 10.14264/00d3c94 29 10.14264/00d3c94 false true Reference 65211667 /api/reference/65211667 30. Grimmelikhuijsen S (2023) Explaining why the computer says no: algorithmic transparency affects the perceived trustworthiness of automated decision-making. Public Adm Rev 83(2):241–262. https://doi.org/10.1111/puar.13483, DOI: 10.1111/puar.13483 30 10.1111/puar.13483 false true Reference 65211668 /api/reference/65211668 31. Grimmelikhuijsen S, Meijer A (2022) Legitimacy of algorithmic decision-making: six threats and the need for a calibrated institutional response. Perspect Public Manag Govern 5(3):232–242. https://doi.org/10.1093/ppmgov/gvac008, DOI: 10.1093/ppmgov/gvac008 31 10.1093/ppmgov/gvac008 false true Reference 65211669 /api/reference/65211669 32. Gustafson PE (1998) Gender Differences in Risk Perception: theoretical and Methodological erspectives. Risk Anal 18(6):805–811. https://doi.org/10.1111/j.1539-6924.1998.tb01123.x, DOI: 10.1111/j.1539-6924.1998.tb01123.x 32 10.1111/j.1539-6924.1998.tb01123.x false true Reference 65211670 /api/reference/65211670 33. Halpern D, Katz JE (2012) Unveiling robotophobia and cyber-dystopianism: the role of gender, technology and religion on attitudes towards robots. Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction (pp. 139-140). Boston Massachusetts USA: ACM, DOI: 10.1145/2157689.2157724 33 10.1145/2157689.2157724 false true Reference 65211671 /api/reference/65211671 34. Heerink M (2011) Exploring the influence of age, gender, education and computer experience on robot acceptance by older adults. Proceedings of the 6th international conference on Human-robot interaction (pp. 147-148). Lausanne Switzerland: ACM, DOI: 10.1145/1957656.1957704 34 10.1145/1957656.1957704 false true Reference 65211672 /api/reference/65211672 35. Helberger N, Araujo T, De Vreese CH (2020) Who is the fairest of them all? Public attitudes and expectations regarding automated decision-making. Comput Law Secur Rev 39:105456. https://doi.org/10.1016/j.clsr.2020.105456, DOI: 10.1016/j.clsr.2020.105456 35 10.1016/j.clsr.2020.105456 false true Reference 65211673 /api/reference/65211673 36. Herke B, Vicsek L (2022) The attitudes of young citizens in higher education towards universal basic income in the context of automation-a qualitative study. Int J Soc Welf 31(3):310–322. https://doi.org/10.1111/ijsw.12533, DOI: 10.1111/ijsw.12533 36 10.1111/ijsw.12533 false true Reference 65211674 /api/reference/65211674 37. Herzfeld N (2023) The artifice of intelligence: divine and human relationship in a robotic age. Augsburg Fortress Publishers, Minneapolis 37 false true Reference 65211675 /api/reference/65211675 38. Hoff KA, Bashir M (2015) Trust in automation: integrating empirical evidence on factors that influence trust. Human Factors: J Human Factors Ergon Soc 57(3):407–434. https://doi.org/10.1177/0018720814547570, DOI: 10.1177/0018720814547570 38 10.1177/0018720814547570 false true Reference 65211676 /api/reference/65211676 39. Hofmann HCH (2024) Automated decision-making (adm) in eu public law. In: Hofmann HCH, Pflücke F (eds) Governance of automated decision-making and eu law. Oxford University Press, Oxford, DOI: 10.1093/9780198919575.001.0001 39 10.1093/9780198919575.001.0001 false true Reference 65211677 /api/reference/65211677 40. Hong J-W (2022) I was born to love AI: the influence of social status on AI self-efficacy and intentions to use AI. Int J Commun 16:179–191 40 false true Reference 65211678 /api/reference/65211678 41. Hooghe M, Marien S, De Vroome T (2012) The cognitive basis of trust. The relation between education, cognitive ability, and generalized and political trust. Intelligence 40(6):604–613. https://doi.org/10.1016/j.intell.2012.08.006, DOI: 10.1016/j.intell.2012.08.006 41 10.1016/j.intell.2012.08.006 false true Reference 65211679 /api/reference/65211679 42. Horváth A, Vicsek L (2023) Visions of Hungarian artificial intelligence specialists about the future of work and their roles. Sci Technol Soc 28(4):603–620. https://doi.org/10.1177/09717218231186105, DOI: 10.1177/09717218231186105 42 10.1177/09717218231186105 false true Reference 65211680 /api/reference/65211680 43. Hungarian Central Statistical Office (2022) Census 2022. Retrieved 2024-03-27, from https://nepszamlalas2022.ksh.hu/en/ 43 false true Reference 65211681 /api/reference/65211681 44. Ikari S, Sato K, Burdett E, Ishiguro H, Jong J, Nakawake Y (2023) Religion- related values differently influence moral attitude for robots in the United States and Japan. J Cross Cult Psychol 54(6–7):742–759. https://doi.org/10.1177/00220221231193369, DOI: 10.1177/00220221231193369 44 10.1177/00220221231193369 false true Reference 65211682 /api/reference/65211682 45. Ivanov S, Kuyumdzhiev M, Webster C (2020) Automation fears: drivers and solutions. Technol Soc 63:101431. https://doi.org/10.1016/j.techsoc.2020.101431, DOI: 10.1016/j.techsoc.2020.101431 45 10.1016/j.techsoc.2020.101431 false true Reference 65211683 /api/reference/65211683 46. Jangjarat K, Kraiwanit T, Limna P, Sonsuphap R (2023) Public perceptions towards ChatGPT as the robo-assistant [SSRN Scholarly Paper]. Rochester, NY 46 false true Reference 65211684 /api/reference/65211684 47. Joyce K, Smith-Doerr L, Alegria S, Bell S, Cruz T, Hoffman SG, Shestakofsky B (2021) Toward a sociology of artificial intelligence: a call for research on inequalities and structural change. Socius 7:2378023121999581. https://doi.org/10.1177/2378023121999581, DOI: 10.1177/2378023121999581 47 10.1177/2378023121999581 false true Reference 65211685 /api/reference/65211685 48. Karataş M, Cutright KM (2023) Thinking about god increases acceptance of artificial intelligence in decision-making. Proc Natl Acad Sci 120(33):e2218961120. https://doi.org/10.1073/pnas.2218961120, DOI: 10.1073/pnas.2218961120 48 10.1073/pnas.2218961120 false true Reference 65211686 /api/reference/65211686 49. Kern C, Gerdon F, Bach RL, Keusch F, Kreuter F (2022) Humans versus machines: Who is perceived to decide fairer? Experimental evidence on attitudes toward automated decision-making. Patterns. https://doi.org/10.1016/j.patter.2022.100591, DOI: 10.1016/j.patter.2022.100591 49 10.1016/j.patter.2022.100591 false true Reference 65211687 /api/reference/65211687 50. Kitchin R (2017) Thinking critically about and researching algorithms. Inf Commun Soc 20(1):14–29. https://doi.org/10.1080/1369118X.2016.1154087, DOI: 10.1080/1369118X.2016.1154087 50 10.1080/1369118X.2016.1154087 false true Reference 65211688 /api/reference/65211688 51. Kizilcec RF (2016) How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface. Proceedings of the 2016 CHI conference on human factors in computing systems (pp 2390-2395). San Jose California USA: ACM, DOI: 10.1145/2858036.2858402 51 10.1145/2858036.2858402 false true Reference 65211689 /api/reference/65211689 52. Klos A, Rosenbaum M, Schiffmann W (2020) emergency landing field identification based on a hierarchical ensemble transfer learning model. 2020 Eighth international symposium on computing and networking (CANDAR) (pp 49-58). Naha, Japan: IEEE, DOI: 10.1109/CANDAR51075.2020.00014 52 10.1109/CANDAR51075.2020.00014 false true Reference 65211690 /api/reference/65211690 53. Kulida E, Lebedev V (2020) About the use of artificial intelligence methods in aviation. 2020 13th international conference “Management of large-scale system development” (MLSD) (pp 1-5). Moscow, Russia: IEEE, DOI: 10.1109/MLSD49919.2020.9247822 53 10.1109/MLSD49919.2020.9247822 false true Reference 65211691 /api/reference/65211691 54. Kuznetsova A, Brockhoff PB, Christensen RHB (2017) lmerTest package tests in linear mixed effects models. J Stat Softw. https://doi.org/10.18637/jss.v082.i13, DOI: 10.18637/jss.v082.i13 54 10.18637/jss.v082.i13 false true Reference 65211692 /api/reference/65211692 55. Lang FR, John D, Lüdtke O, Schupp J, Wagner GG (2011) Short assessment of the big five: robust across survey methods except telephone interviewing. Behav Res Methods 43(2):548–567. https://doi.org/10.3758/s13428-011-0066-z, DOI: 10.3758/s13428-011-0066-z 55 10.3758/s13428-011-0066-z false true Reference 65211693 /api/reference/65211693 56. Lee JD, See KA (2004) Trust in automation: designing for appropriate reliance. Hum Factors 46(1):50–80. https://doi.org/10.1518/hfes.46.1.50_30392, DOI: 10.1518/hfes.46.1.50_30392 56 10.1518/hfes.46.1.50_30392 false true Reference 65211694 /api/reference/65211694 57. Levi M, Stoker L (2000) Political trust and trustworthiness. Annu Rev Polit Sci 3(1):475–507. https://doi.org/10.1146/annurev.polisci.3.1.475, DOI: 10.1146/annurev.polisci.3.1.475 57 10.1146/annurev.polisci.3.1.475 false true Reference 65211695 /api/reference/65211695 58. Liu R, Rizzo S, Whipple S, Pal N, Pineda AL, Lu M, Zou J (2021) Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature 592(7855):629–633. https://doi.org/10.1038/s41586-021-03430-5, DOI: 10.1038/s41586-021-03430-5 58 10.1038/s41586-021-03430-5 false true Reference 65211696 /api/reference/65211696 59. Logg JM (2017) Theory of machine: When do people rely on algorithms? Harvard Business School working paper series 17-086, DOI: 10.2139/ssrn.2941774 59 10.2139/ssrn.2941774 false true Reference 65211697 /api/reference/65211697 60. Logg J, Minson J, Moore DA (2018) Algorithm appreciation: people prefer algorithmic to human judgment [SSRN Scholarly Paper]. Rochester, NY 60 false true Reference 65211698 /api/reference/65211698 61. Lünich M, Kieslich K (2024) Exploring the roles of trust and social group preference on the legitimacy of algorithmic decision-making vs. human decisionmaking for allocating covid-19 vaccinations. AI Soc 39:309–327. https://doi.org/10.1007/s00146-022-01412-3, DOI: 10.1007/s00146-022-01412-3 61 10.1007/s00146-022-01412-3 false true Reference 65211699 /api/reference/65211699 62. Malhotra NK, Kim SS, Agarwal J (2004) Internet users’ information privacy concerns (IUIPC): the construct, the scale, and a causal model. Inf Syst Res 15(4):336–355. https://doi.org/10.1287/isre.1040.0032, DOI: 10.1287/isre.1040.0032 62 10.1287/isre.1040.0032 false true Reference 65211700 /api/reference/65211700 63. Malik M, Malik MK, Mehmood K, Makhdoom I (2021) Automatic speech recognition: a survey. Multimed Tools Appl 80(6):9411–9457. https://doi.org/10.1007/s11042-020-10073-7, DOI: 10.1007/s11042-020-10073-7 63 10.1007/s11042-020-10073-7 false true Reference 65211701 /api/reference/65211701 64. Manheim K, Kaplan L (2019) Artificial intelligence risks to privacy and democracy. Yale J Law Technol 21:106 64 false true Reference 65211702 /api/reference/65211702 65. Mayer RC, Davis JH, Schoorman FD (1995) An Integrative model of organizational trust. Acad Manag Rev 20(3):709–734. https://doi.org/10.2307/258792, DOI: 10.2307/258792 65 10.2307/258792 false true Reference 65211703 /api/reference/65211703 66. Melchert C (2011) God created adam in his image. J Qur’anic Stud 13(1):113–124. https://doi.org/10.3366/jqs.2011.0009, DOI: 10.3366/jqs.2011.0009 66 10.3366/jqs.2011.0009 false true Reference 65211704 /api/reference/65211704 67. Miller AP (2018) Want less-biased decisions? Use Algorithms. Harvard Business Review 67 false true Reference 65211705 /api/reference/65211705 68. Neudert L-M, Knuutila A, Howard PN (2020) Global attitudes towards AI, machine learning & automated decision making 68 false true Reference 65211706 /api/reference/65211706 69. Newell S, Marabelli M (2015) Strategic opportunities (and challenges) of algorithmic decision-making: a call for action on the long-term societal effects of ‘datification’. J Strateg Inf Syst 24(1):3–14. https://doi.org/10.1016/j.jsis.2015.02.001, DOI: 10.1016/j.jsis.2015.02.001 69 10.1016/j.jsis.2015.02.001 false true Reference 65211707 /api/reference/65211707 70. Oksanen A, Savela N, Latikka R, Koivula A (2020) Trust toward robots and artificial intelligence: an experimental approach to human-technology interactions online. Front Psychol. https://doi.org/10.3389/fpsyg.2020.568256, DOI: 10.3389/fpsyg.2020.568256 70 10.3389/fpsyg.2020.568256 false true Reference 65211708 /api/reference/65211708 71. O’Shaughnessy MR, Schiff DS, Varshney LR, Rozell CJ, Davenport MA (2023) What governs attitudes toward artificial intelligence adoption and governance? Sci Public Policy 50(2):161–176. https://doi.org/10.1093/scipol/scac056, DOI: 10.1093/scipol/scac056 71 10.1093/scipol/scac056 false true Reference 65211709 /api/reference/65211709 72. Park J, Woo SE (2022) Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. J Psychol 156(1):68–94. https://doi.org/10.1080/00223980.2021.2012109, DOI: 10.1080/00223980.2021.2012109 72 10.1080/00223980.2021.2012109 false true Reference 65211710 /api/reference/65211710 73. Park S, Yoon S-H (2005) Separating early-adopters from the majority: the case of broadband internet access in Korea. Technol Forecast Soc Chang 72(3):301–325. https://doi.org/10.1016/j.techfore.2004.08.013, DOI: 10.1016/j.techfore.2004.08.013 73 10.1016/j.techfore.2004.08.013 false true Reference 65211711 /api/reference/65211711 74. Philip L, Cottrill C, Farrington J, Williams F, Ashmore F (2017) The digital divide: patterns, policy and scenarios for connecting the ‘final few’ in rural communities across Great Britain. J Rural Stud 54:386–398. https://doi.org/10.1016/j.jrurstud.2016.12.002, DOI: 10.1016/j.jrurstud.2016.12.002 74 10.1016/j.jrurstud.2016.12.002 false true Reference 65211712 /api/reference/65211712 75. Pieters W (2011) Explanation and trust: what to tell the user in security and AI? Ethics Inf Technol 13(1):53–64. https://doi.org/10.1007/s10676-010-9253-3, DOI: 10.1007/s10676-010-9253-3 75 10.1007/s10676-010-9253-3 false true Reference 65211713 /api/reference/65211713 76. Qu W, Sun H, Ge Y (2021) The effects of trait anxiety and the big five personality traits on self-driving car acceptance. Transportation 48(5):2663–2679. https://doi.org/10.1007/s11116-020-10143-7, DOI: 10.1007/s11116-020-10143-7 76 10.1007/s11116-020-10143-7 false true Reference 65211714 /api/reference/65211714 77. Ragnedda M, Kreitem H (2018) The three levels of digital divide in East EU countries World of Media. J Russ Media Journal Stud 1(4):5–26. https://doi.org/10.30547/worldofmedia.4.2018.1, DOI: 10.30547/worldofmedia.4.2018.1 77 10.30547/worldofmedia.4.2018.1 false true Reference 65211715 /api/reference/65211715 78. Rashidi P, Mihailidis A (2013) A survey on ambient-assisted living tools for older adults. IEEE J Biomed Health Inform 17(3):579–590. https://doi.org/10.1109/JBHI.2012.2234129, DOI: 10.1109/JBHI.2012.2234129 78 10.1109/JBHI.2012.2234129 false true Reference 65211716 /api/reference/65211716 79. Ritschel H, Janowski K, Seiderer A, Wagner S, André E (2019) Insights on usability and user feedback for an assistive robotic health companion with adaptive linguistic style. Proceedings of the 12th acm international conference on pervasive technologies related to assistive environments (pp. 319-320). Rhodes Greece: ACM, DOI: 10.1145/3316782.3322737 79 10.1145/3316782.3322737 false true Reference 65211717 /api/reference/65211717 80. Rosseel Y (2012) lavaan: an r package for structural equation modeling. J Stat Softw 48:1–36, DOI: 10.18637/jss.v048.i02 80 10.18637/jss.v048.i02 false true Reference 65211718 /api/reference/65211718 81. Said N, Potinteu AE, Brich I, Buder J, Schumm H, Huff M (2023) An artificial intelligence perspective: How knowledge and confidence shape risk and benefit perception. Comput Hum Behav 149:107855. https://doi.org/10.1016/j.chb.2023.107855, DOI: 10.1016/j.chb.2023.107855 81 10.1016/j.chb.2023.107855 false true Reference 65211719 /api/reference/65211719 82. Selbst AD, Barocas S (2018) The intuitive appeal of explainable machines. SSRN Electron J. https://doi.org/10.2139/ssrn.3126971, DOI: 10.2139/ssrn.3126971 82 10.2139/ssrn.3126971 false true Reference 65211720 /api/reference/65211720 83. Shin D (2020) How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance. Comput Hum Behav 109:106344. https://doi.org/10.1016/j.chb.2020.106344, DOI: 10.1016/j.chb.2020.106344 83 10.1016/j.chb.2020.106344 false true Reference 65211721 /api/reference/65211721 84. Smith A (2018) Public attitudes toward computer algorithms. Retrieved 2024-05- 03, from https://www.pewresearch.org/internet/2018/11/16/public-attitudestoward-computer-algorithms/ 84 false true Reference 65211722 /api/reference/65211722 85. Soundararajan R, Shenbagaraman VM (2024) Enhancing financial decisionmaking through explainable ai and blockchain integration improving transparency and trust in predictive models. Educ Adm: Theory Pract 30(4):9341–9351. https://doi.org/10.53555/kuey.v30i4.3672, DOI: 10.53555/kuey.v30i4.3672 85 10.53555/kuey.v30i4.3672 false true Reference 65211723 /api/reference/65211723 86. Stai B, Heller N, McSweeney S, Rickman J, Blake P, Vasdev R, Weight C (2020) Public perceptions of artificial intelligence and robotics in medicine. J Endourol 34(10):1041–1048. https://doi.org/10.1089/end.2020.0137, DOI: 10.1089/end.2020.0137 86 10.1089/end.2020.0137 false true Reference 65211724 /api/reference/65211724 87. Svendsen GB, Johnsen J-AK, Almås-Sørensen L, Vittersø J (2013) Personality and technology acceptance: the influence of personality factors on the core constructs of the technology acceptance model. Behav Inf Technol 32(4):323–334. https://doi.org/10.1080/0144929X.2011.553740, DOI: 10.1080/0144929X.2011.553740 87 10.1080/0144929X.2011.553740 false true Reference 65211725 /api/reference/65211725 88. Thurman N, Moeller J, Helberger N, Trilling D (2018) My friends, editors, algorithms, and I. Digit J 7(4):447–469. https://doi.org/10.1080/21670811.2018.1493936, DOI: 10.1080/21670811.2018.1493936 88 10.1080/21670811.2018.1493936 false true Reference 65211726 /api/reference/65211726 89. van Deursen AJ, van Dijk JA (2019) The first-level digital divide shifts from inequalities in physical access to inequalities in material access. New Media Soc 21(2):354–375. https://doi.org/10.1177/1461444818797082, DOI: 10.1177/1461444818797082 89 10.1177/1461444818797082 false true Reference 65211727 /api/reference/65211727 90. Vértesy D (2020) A mesterséges intelligencia fejlesztéséért folyó globális verseny és magyarország = the global race for ai development and hungary. Köz-gazdaság 15(1):197–202, DOI: 10.14267/RETP2020.01.18 90 10.14267/RETP2020.01.18 false true Reference 65211728 /api/reference/65211728 91. Vicsek L, Bokor T, Pataki G (2022) Younger generations’ expectations regarding artificial intelligence in the job market: mapping accounts about the future relationship of automation and work. J Sociol 60(1):21–38. https://doi.org/10.1177/14407833221089365, DOI: 10.1177/14407833221089365 91 10.1177/14407833221089365 false true Reference 65211729 /api/reference/65211729 92. Vimalkumar M, Sharma SK, Singh JB, Dwivedi YK (2021) ‘Okay google, what about my privacy?’: User’s privacy perceptions and acceptance of voice based digital assistants. Comput Hum Behav 120:106763. https://doi.org/10.1016/j.chb.2021.106763, DOI: 10.1016/j.chb.2021.106763 92 10.1016/j.chb.2021.106763 false true Reference 65211730 /api/reference/65211730 93. Waldman AE (2019) Power, process, and automated decision-making. Fordham L Rev 88:613 93 false true Reference 65211731 /api/reference/65211731 94. Wilhelm C, Steckelberg A, Rebitschek FG (2025) Benefits and harms associated with the use of ai-related algorithmic decision-making systems by healthcare professionals: a systematic review. Lancet Reg Health-Europe. https://doi.org/10.1016/j.lanepe.2024.101145, DOI: 10.1016/j.lanepe.2024.101145 94 10.1016/j.lanepe.2024.101145 false true Reference 65211732 /api/reference/65211732 95. Xu H, Gupta S (2009) The effects of privacy concerns and personal innovativeness on potential and experienced customers’ adoption of location-based services. Electron Mark 19(2):137–149. https://doi.org/10.1007/s12525-009-0012-4, DOI: 10.1007/s12525-009-0012-4 95 10.1007/s12525-009-0012-4 false true Reference 65211733 /api/reference/65211733 96. Yokoi R, Nakayachi K (2021) Trust in autonomous cars: exploring the role of shared moral values, reasoning, and emotion in safety-critical decisions. Hum Factors 63(8):1465–1484. https://doi.org/10.1177/0018720820933041, DOI: 10.1177/0018720820933041 96 10.1177/0018720820933041 false true Reference 65211734 /api/reference/65211734 97. Zajko M (2021) Conservative AI and social inequality: conceptualizing alternatives to bias through social theory. AI Soc 36(3):1047–1056. https://doi.org/10.1007/s00146-021-01153-9, DOI: 10.1007/s00146-021-01153-9 97 10.1007/s00146-021-01153-9 false true Reference 65211735 /api/reference/65211735 98. Zajko M (2022) Artificial intelligence, algorithms, and social inequality: sociological contributions to contemporary debates. Sociol Compass. https://doi.org/10.1111/soc4.12962, DOI: 10.1111/soc4.12962 98 10.1111/soc4.12962 false true /api/publication/36075196 Orbán Ferenc et al. Trust in artificial intelligence: a survey experiment to assess trust in algorithmic decision-making. (2025) AI AND SOCIETY: THE JOURNAL OF HUMAN-CENTERED SYSTEMS AND MACHINE INTELLIGENCE 0951-5666 1435-5655 40 6 4955-4969<div class="JournalArticle Publication long-list"> <div class="authors"> <img title="Forrásközlemény" style="float: left" src="/frontend/resources/grid/publication-core-icon.png"> <img title="Idézőközlemény" style="float: left" src="/frontend/resources/grid/publication-citation-icon.png"> <div class="autype autype0"> <span class="author-name" mtid="10089341"><a href="/gui2/?type=authors&mode=browse&sel=10089341" target="_blank">Orbán Ferenc (<span class="authorship-author-name">Orbán Ferenc</span> <span class="authorAux-mtmt"> Politikai elemző</span>) </a> </span> <span class="author-affil">Századvég Csoport</span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="10065427"><a href="/gui2/?type=authors&mode=browse&sel=10065427" target="_blank">Stefkovics Ádám (<span class="authorship-author-name">Stefkovics Ádám</span> <span class="authorAux-mtmt"> szociológia</span>) </a> </span> <span class="author-affil"><span title="HUN-REN Társadalomtudományi Kutatóközpont">HUN-REN TK</span>/Lendület Recens Kutatócsoport; Századvég Csoport</span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;36075196" target="_blank">Trust in artificial intelligence: a survey experiment to assess trust in algorithmic decision-making</a></div> <div> <span class="journal-title">AI AND SOCIETY: THE JOURNAL OF HUMAN-CENTERED SYSTEMS AND MACHINE INTELLIGENCE</span> <span class="journal-issn">(<a target="_blank" href="https://portal.issn.org/resource/ISSN/0951-5666">0951-5666</a> <a target="_blank" href="https://portal.issn.org/resource/ISSN/1435-5655">1435-5655</a>)</span>: <span class="journal-volume">40</span> <span class="journal-issue">6</span> <span class="page"> pp 4955-4969 </span> <span class="year">(2025)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_GREEN" title=" Green "> <a style="color:blue" title="10.1007/s00146-025-02237-6" target="_blank" href="https://doi.org/10.1007/s00146-025-02237-6"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="001456464900001" target="_blank" href="https://www.webofscience.com/wos/woscc/full-record/001456464900001"> WoS </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="105001865721" target="_blank" href="http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-105001865721"> Scopus </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:black" title="https://link.springer.com/10.1007/s00146-025-02237-6" target="_blank" href="https://link.springer.com/10.1007/s00146-025-02237-6"> Egyéb URL </a> </span> </span> <OnlyViewableByAuthor><div class="ratings"> <div class="journal-subject">Folyóirat szakterülete: Scopus - Human-Computer Interaction&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;D1</div> <div class="journal-subject">Folyóirat szakterülete: Scopus - Philosophy&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;D1</div> <div class="journal-subject">Folyóirat szakterülete: Scopus - Artificial Intelligence&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q1</div> </div></OnlyViewableByAuthor> <div class="publication-citation" style="margin-left: 0.5cm;"> <span title="Nyilvános idézőközlemények összesen, említések nélkül" class="citingPub-count">Nyilvános idéző összesen: 45</span> | Független: 45 | Függő: 0 | Nem jelölt: 0 | WoS jelölt: 9 | Scopus jelölt:&nbsp;2 | WoS/Scopus jelölt:&nbsp;9 | DOI jelölt:&nbsp;34 (Nem nyilvános:&nbsp;1) </div> <div class="publication-citation"> <a target="_blank" href="/api/publication?cond=citations.related;eq;36075196&sort=publishedYear,desc&sort=title"> Idézett közlemények száma: 5 </a> </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 36075196</span> | <span class="status-data status-VALIDATED"> Egyeztetett </span> Forrás Idéző | <span class="type-subtype">Folyóiratcikk ( Szakcikk ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">kézi felvitel</span> </div> <div class="lastModified">Utolsó módosítás: 2026.05.21. 13:44 Tokai Tamás (Központi 2-3, admin) </div> <div class="lockedBy">Központi kezelésű 2026.05.21. 13:51 Tokai Tamás (Központi 2-3, admin) </div> </div></div>