nach oben

AI & SOCIETY

Erschienen in:

01.01.2020 | Open Forum

In AI we trust? Perceptions about automated decision-making by artificial intelligence

verfasst von: Theo Araujo, Natali Helberger, Sanne Kruikemeier, Claes H. de Vreese

Erschienen in: AI & SOCIETY | Ausgabe 3/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Fueled by ever-growing amounts of (digital) data and advances in artificial intelligence, decision-making in contemporary societies is increasingly delegated to automated processes. Drawing from social science theories and from the emerging body of research about algorithmic appreciation and algorithmic perceptions, the current study explores the extent to which personal characteristics can be linked to perceptions of automated decision-making by AI, and the boundary conditions of these perceptions, namely the extent to which such perceptions differ across media, (public) health, and judicial contexts. Data from a scenario-based survey experiment with a national sample (N = 958) show that people are by and large concerned about risks and have mixed opinions about fairness and usefulness of automated decision-making at a societal level, with general attitudes influenced by individual characteristics. Interestingly, decisions taken automatically by AI were often evaluated on par or even better than human experts for specific decisions. Theoretical and societal implications about these findings are discussed.

Vorheriger Artikel Automation for the artisanal economy: enhancing the economic and environmental sustainability of crafting professions with human–machine collaboration

Nächster Artikel Artificial intelligence assistants and risk: framing a connectivity risk narrative

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

While perceptions of ADM are relevant in many societal contexts, this study has chosen to focus on media, (public) health, and justice. In these three sectors, we expect that ADM can have a significant impact on individual rights, well-being, and functioning in a society (as citizens and voters in the case of the media, as members of a society in the case of justice, and as humans in the case of health).

Results are reported for the measure with higher reliability (without the reversed item), but differences are communicated in the notes (Table 2).

Approximately 28% (818) of the responses for all the scenarios combined (N = 2874) were removed because of the manipulation check. When running the analyses with these responses included, the results stay largely the same with regards to direction and significance levels. Exceptions are indicated in the notes.

Agarwal R, Gao G, DesRoches C, Jha AK (2010) Research commentary—the digital transformation of healthcare: current status and the road ahead. Inf Syst Res 21:796–809. https://doi.org/10.1287/isre.1100.0327CrossRef

Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine Bias. In: ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Accessed 18 Jun 2018

Baek TH, Morimoto M (2012) Stay away from me. J Advert 41:59–76. https://doi.org/10.2753/JOA0091-3367410105CrossRef

Bickmore T, Utami D, Matsuyama R, Paasche-Orlow MK (2016) Improving access to online health information with conversational agents: a randomized controlled experiment. J Med Internet Res. https://doi.org/10.2196/jmir.5239CrossRef

Boerman SC, Kruikemeier S, Borgesius FJZ (2017) Online behavioral advertising: a literature review and research agenda. J Advert. https://doi.org/10.1080/00913367.2017.1339368CrossRef

Boerman SC, Kruikemeier S, Zuiderveen Borgesius FJ (2018) Exploring motivations for online privacy protection behavior: insights from panel data. Commun Res. https://doi.org/10.1177/0093650218800915CrossRef

Bol N, Kruikemeier S, Boerman SC et al (2018) Understanding the effects of personalization as a privacy calculus: analyzing self-disclosure across health, news, and commerce contexts. J Comput-Mediat Commun 23(6):370–388CrossRef

Carlson M (2018) Automating judgment? Algorithmic judgment, news knowledge, and journalistic professionalism. New Media Soc 20:1755–1772. https://doi.org/10.1177/1461444817706684CrossRef

Chu Z, Gianvecchio S, Wang H, Jajodia S (2012) Detecting automation of twitter accounts: Are you a human, bot, or cyborg? IEEE Trans Dependable Secure Comput 9:811–824. https://doi.org/10.1109/TDSC.2012.75CrossRef

Cox D, Cox AD (2001) Communicating the consequences of early detection: the role of evidence and framing. J Mark 65:91–103. https://doi.org/10.1509/jmkg.65.3.91.18336CrossRef

Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–340. https://doi.org/10.2307/249008CrossRef

Dawes RM, Faust D, Meehl PE (1989) Clinical versus actuarial judgment. Science 243:1668–1674. https://doi.org/10.1126/science.2648573CrossRef

Diakopoulos N, Koliska M (2017) Algorithmic transparency in the news media. Digit J 5:809–828. https://doi.org/10.1080/21670811.2016.1208053CrossRef

Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: people erroneously avoid algorithms after seeing them err. J Exp Psychol Gen 144:114–126. https://doi.org/10.1037/xge0000033CrossRef

Dijkstra JJ, Liebrand WBG, Timminga E (1998) Persuasiveness of expert systems. Behav Inf Technol 17:155–163. https://doi.org/10.1080/014492998119526CrossRef

Dilsizian SE, Siegel EL (2013) Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep 16:441. https://doi.org/10.1007/s11886-013-0441-8CrossRef

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 43:127–145. https://doi.org/10.1002/hrm.20011CrossRef

Dodge M, Kitchin R (2007) The automatic management of drivers and driving spaces. Geoforum 38:264–275. https://doi.org/10.1016/j.geoforum.2006.08.004CrossRef

Dressel J, Farid H (2018) The accuracy, fairness, and limits of predicting recidivism. Sci Adv. https://doi.org/10.1126/sciadv.aao5580CrossRef

Elish MC, Boyd danah (2018) Situating methods in the magic of Big Data and AI. Commun Monogr 85:57–80. https://doi.org/10.1080/03637751.2017.1375130CrossRef

European Commission (2018) Data protection working party. Directive 95/46/EC § Articles 29 and 30

Ferrara E, Varol O, Davis C et al (2016) The rise of social bots. Commun ACM 59:96–104. https://doi.org/10.1145/2818717CrossRef

Field A (2013) Discovering statistics using IBM SPSS statistics. Sage, Newcastle

Gillespie T (2014) The relevance of algorithms. Media Technol Essays Commun Mater Soc 167:167

Graefe A, Haim M, Haarmann B, Brosius H-B (2018) Readers’ perception of computer-generated news: credibility, expertise, and readability. Journalism 19:595–610. https://doi.org/10.1177/1464884916641269CrossRef

Grolleman J, van Dijk B, Nijholt A, van Emst A (2006) Break the Habit! designing an e-therapy intervention using a virtual coach in aid of smoking cessation. In: IJsselsteijn WA, de Kort YAW, Midden C et al (eds) Persuasive technology. Springer, Berlin, pp 133–141CrossRef

Hajian S, Bonchi F, Castillo C (2016) Algorithmic bias: from discrimination discovery to fairness-aware data mining. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD’16. ACM Press, San Francisco, California, USA, pp 2125–2126. https://doi.org/10.1145/2939672.2945386

Hudlicka E (2013) Virtual training and coaching of health behavior: example from mindfulness meditation training. Patient Educ Couns 92:160–166. https://doi.org/10.1016/j.pec.2013.05.007CrossRef

Inglehart R, Haerpfer C, Moreno A, Welzel C, Kizilova K, Diez-Medrano J, Lagos M, Norris P, Ponarin E, Puranen B et al (eds) (2014). World values survey: round five-country-pooled datafile version. JD Systems Institute, Madrid. www.worldvaluessurvey.org/WVSDocumentationWV5.jsp

Jha S, Topol EJ (2016) Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 316:2353–2354. https://doi.org/10.1001/jama.2016.17438CrossRef

Kennedy LW, Caplan JM, Piza E (2011) Risk clusters, hotspots, and spatial intelligence: risk terrain modeling as an algorithm for police resource allocation strategies. J Quant Criminol 27:339–362. https://doi.org/10.1007/s10940-010-9126-2CrossRef

Kitchin R (2017) Thinking critically about and researching algorithms. Inf Commun Soc 20:14–29. https://doi.org/10.1080/1369118X.2016.1154087CrossRef

Lee MK (2018) Understanding perception of algorithmic decisions: fairness, trust, and emotion in response to algorithmic management. Big Data Soc 5:2053951718756684. https://doi.org/10.1177/2053951718756684CrossRef

Lee MK, Baykal S (2017) Algorithmic Mediation in Group Decisions: Fairness Perceptions of Algorithmically Mediated vs. Discussion-Based Social Division. ACM Press, pp 1035–1048

Logg J (2017) Theory of machine: When do people rely on algorithms? SSRN Electron J. https://doi.org/10.2139/ssrn.2941774CrossRef

Logg J, Minson J, Moore DA (2018) Algorithm appreciation: people prefer algorithmic to human judgment. Social Science Research Network, Rochester

Madhavan P, Wiegmann DA (2007) Effects of information source, pedigree, and reliability on operator interaction with decision support systems. Hum Factors J Hum Factors Ergon Soc 49:773–785. https://doi.org/10.1518/001872007X230154CrossRef

McQuillan D (2015) Algorithmic states of exception. Eur J Cult Stud 18:564–576. https://doi.org/10.1177/1367549415577389CrossRef

Nass C, Steuer J, Tauber ER (1994) Computers are social actors. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, pp 72–78

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 24:3–14. https://doi.org/10.1016/j.jsis.2015.02.001CrossRef

Nissan E (2017) Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement. AI Soc 32:441–464. https://doi.org/10.1007/s00146-015-0596-5CrossRef

Nysveen H (2005) Intentions to use mobile services: antecedents and cross-service comparisons. J Acad Mark Sci 33:330–346. https://doi.org/10.1177/0092070305276149CrossRef

O’Neil C (2017) Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books

Pierson E (2017) Demographics and discussion influence views on algorithmic fairness. ArXiv:1712.09124 Cs

Siddiqui H, Healy E, Olmsted A (2017) Bot or not. In: 2017 12th international conference for internet technology and secured transactions (ICITST). pp 462–463. https://doi.org/10.23919/ICITST.2017.8356448

Smith A (2018) Public attitudes toward computer algorithms. pp 1–40. Retrieved from Pew Research Center website http://www.pewinternet.org/2018/11/16/public-attitudes-toward-computer-algorithms/

Stanford University (2016) One hundred year study on artificial intelligence (AI). Retrieved from https://ai100.stanford.edu/

Sundar SS (2008) The MAIN model: a heuristic approach to understanding technology effects on credibility. In: Metzger MJ, Flanagin AJ (eds) Digital media, youth, and credibility. MIT Press, Cambridge, MA, pp 73–100

Sundar SS, Nass C (2000) Source orientation in human-computer interaction programmer, networker, or independent social actor. Commun Res 27:683–703. https://doi.org/10.1177/009365000027006001CrossRef

Sundar SS, Nass C (2001) Conceptualizing sources in online news. J Commun 51:52–72CrossRef

Thurman N, Schifferes S (2012) The future of personalization at news websites. J Stud 13:775–790. https://doi.org/10.1080/1461670X.2012.664341CrossRef

Thurman N, Moeller J, Helberger N, Trilling D (2018) My friends, editors, algorithms, and I. Digit J. https://doi.org/10.1080/21670811.2018.1493936CrossRef

van Dijck J, Poell T, de Waal M (2018) The platform society: public values in a connective world. Oxford University Press, OxfordCrossRef

Yeomans M, Shah A, Mullainathan S, Kleinberg J (2019) Making sense of recommendations. J Behav Decis Making 32(4):403–414. https://doi.org/10.1002/bdm.2118CrossRef

Yu K-H, Kohane IS (2018) Framing the challenges of artificial intelligence in medicine. BMJ Qual Saf. https://doi.org/10.1136/bmjqs-2018-008551CrossRef

Zarsky T (2016) The trouble with algorithmic decisions: an analytic road map to examine efficiency and fairness in automated and opaque decision making. Sci Technol Hum Values 41:118–132. https://doi.org/10.1177/0162243915605575CrossRef

Titel: In AI we trust? Perceptions about automated decision-making by artificial intelligence
verfasst von: Theo Araujo
Natali Helberger
Sanne Kruikemeier
Claes H. de Vreese
Publikationsdatum: 01.01.2020
Verlag: Springer London
Erschienen in: AI & SOCIETY / Ausgabe 3/2020
Print ISSN: 0951-5666
Elektronische ISSN: 1435-5655
DOI: https://doi.org/10.1007/s00146-019-00931-w

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 3/2020

How software developers can fix part of GDPR’s problem of click-through consents

Prediction paradigm: the human price of instrumentalism

Behavioural artificial intelligence: an agenda for systematic empirical studies of artificial inference

Community protocols for researchers: using sketches to communicate interaction guidelines

Artificial intelligence assistants and risk: framing a connectivity risk narrative

Hand rehabilitation assessment system using leap motion controller