This study attempts to determine a correlation effect between people’s perception and awareness of the operationalization of artificial intelligence in their everyday lives and in the production, presentation, and publication of news media in the U.S. By looking at the effect individual characteristics may have on a person’s perception and awareness of AI operationalized for news media and looking at whether perception and/or awareness of AI operationalized in a person’s daily life affects their perception and awareness of AI operationalized for news media, we seek to find correlation between these two factors. The research relies on Actor-network theory, the MAIN (Modality, Agency, Interactivity, Navigability) Model, and utilizes a convenience sample survey method using the MTurk participant platform.
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Data availability statement
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
As early as 2006, Thompson Reuters began using algorithms to automatically generate financial news content to free up human journalists for more nuanced reporting that computers were incapable of producing (Dalen 2012). Other news organizations have begun to rely on AI to produce similar news content (e.g., sports articles, elections, and earnings reports). Xinhua news agency, in collaboration with Sogu search company, produced the first male and female AI broadcast news anchors. Thompson Reuters is now in expanded development of similar technology to generate a male persona AI news anchor.
The intermediary refers to a ‘thing’ as a ‘mean’ or ‘tool’ and is defined as an entity capable of transporting meaning without transformation (i.e., a computer) (Latour 2005).
A mediator is defined as an entity capable of transforming, distorting, and/or modifying meaning (Latour 2005).
Researchers found that MTurk exhibited concerns regarding demographic mapping to U.S. census data. According to Buhrmester et al. (2018), the pool is not representative of the U.S. population. Sheehan (2018) notes in her findings that workers on MTurk are generally younger than the U.S. population overall, better educated, predominately Caucasian, and mostly male.
According to the Nielsen Total Audience Report: Q1 2019, adults age 18–34 are a targeted interest group for marketers, the 35–49 age group is the “key” market, and people age 50–64 have been consistently the largest group of media consumers (The Nielsen Total Audience Report 2019).
MTurk workers accounted for more than 40% of studies published from 2012 to 2017 in the Journal of Consumer Research, and many other fields, such as psychology and political science, rely on MTurk for data collection (Sheehan 2018).
To facilitate participant payment, four separate instances of the survey were initiated on the Qualtrics platform. An attention check asking for the respondent’s age-limited responses to meet the desired amount of participation to satisfy a 95% confidence level per age group.
Tesla reached a global vehicle distribution level of 367,000–368,000 in 2019 (Wagner, 2020).
Artificial Narrow Intelligence (ANI) is the weakest level of the three classifications: narrow, general, and super (Kaplan and Haenlein, 2019). It is below human level intelligence, and contains all known AI systems currently in operation (Kaplan and Haenlein 2019). ANI is considered weaker than human intelligence because an ANI system cannot borrow intelligence or knowledge from memories or experiences outside of its programmed operation (Kaplan and Haenlein 2019). Within its programmed operationalization, an ANI will outperform a human assigned the same task, however, an ANI is incapable of adaptation beyond its specific program (Kaplan and Haenlein 2019).
Anderson C (2013a) Towards a sociology of computational and algorithmic journalism. New Media Soc 15(7):1005–1021. https://doi.org/10.1177/1461444812465137
Anderson C (2013b) What aggregators do: Towards a networked concept of journalistic expertise in the digital age. Journalism 14(8):1008–1023. https://doi.org/10.1177/1464884913492460
Bellman R (1978) An introduction to artificial intelligence: can computers think? Boyd & Fraser Pub. Co.
Buhrmester MD, Talaifar S, Gosling SD (2018) An evaluation of Amazon’s mechanical turk, its rapid rise, and its effective use. Perspect Psychol Sci 13(2):149–154. https://doi.org/10.1177/1745691617706516
Carlson M (2015) The robotic reporter. Digit Journal 3(3):416–431. https://doi.org/10.1080/21670811.2014.976412
Carlson M (2019) News algorithms, photojournalism and the assumption of mechanical objectivity in journalism. Digit Journal 7(8):1117–1133. https://doi.org/10.1080/21670811.2019.1601577
Clerwall C (2014) Enter the Robot Journalist. Journal Pract 8(5):519–531. https://doi.org/10.1080/17512786.2014.883116
Comer DE, Gries D, Mulder MC, Tucker A, Turner AJ, Young PR (1989) Computing As a Discipline. Commun ACM 32(1):9–23. https://doi.org/10.1145/63238.63239
Diakopoulos N (2019) Automating the news: How algorithms are rewriting the media. (MU Journalism Library). Harvard University Press.
Dörr KN (2016) Mapping the field of Algorithmic Journalism. Digit J 4(6):700–722. https://doi.org/10.1080/21670811.2015.1096748
Edwards C, Edwards A, Spence PR, Shelton AK (2014) Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter. Comput Hum Behav 33:372–376. https://doi.org/10.1016/j.chb.2013.08.013
Gafoor KA (2012) Considerations in the measurement of awareness. 6. https://doi.org/10.13140/2.1.2109.2643
Graefe A (2016) Guide to automated journalism (Tow Center for Digital Journalism Publications). Columbia University. https://doi.org/10.7916/D80G3XDJ
Graefe A, Haim M, Haarmann B, Brosius H-B (2016) Replication Data for: Readers’ perception of computer-generated news: Credibility, expertise, and readability. https://doi.org/10.7910/DVN/WHUEZA
Graefe A, Haim M, Haarmann B, Brosius H-B (2018) Readers’ perception of computer-generated news: Credibility, expertise, and readability. Journalism 19(5):595–610. https://doi.org/10.1177/1464884916641269
Guzman AL (2018) What is human-machine communication, anyway? In: Guzman AL (ed) Human-machine communication: rethinking communication, technology, and ourselves. Peter Lang, pp 1–28
Haugeland J (1985) Artificial intelligence: the very idea (MU Engineering Library). MIT Press
Howden LM, Meyer JA (2011) Age and sex composition: 2010, U.S. Census Data (Census No. C2010BR-03; 2010 Census Briefs, p. 16). U.S. Department of Commerce Economics and Statistics Administration
Israel GD (1992) Determining sample size. Agricultural Education and Communication Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida; University of Florida IFAS Extension
Kaplan A, Haenlein M (2019) Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz 62(1):15–25. https://doi.org/10.1016/j.bushor.2018.08.004
Kobie N (2018) Reuters is taking a big gamble on AI-supported journalism. Wired UK. https://www.wired.co.uk/article/reuters-artificial-intelligence-journalism-newsroom-ai-lynx-insight. Accessed 22 June 2021
Korosec K (2015) Elon Musk says tesla vehicles will drive themselves in two years. Fortune. https://fortune.com/2015/12/21/elon-musk-interview/. Accessed 23 June 2021
Latour B (2005) Reassembling the social: an introduction to actor-network-theory. Oxford University Press
Lehmuskallio A, Hӓkkinen J, Seppӓnen J (2018) Photorealistic computer-generated images are difficult to distinguish from digital photographs: a case study with professional photographers and photo-editors. Vis Commun 18(4):427. https://doi.org/10.1177/1470357218759809
Leow RP, Johson E, Zárate-Sández. (2011) Getting a grip on the slippery construct of Awareness: Toward a finer-grained methodological perspective. In: Sanz C (ed) Implicit and explicit language learning: conditions, processes, and knowledge in SLA and bilingualism. Georgetown University Press, pp 61–72
Lewis SC, Guzman AL, Schmidt TR (2019) Automation, journalism, and human-machine communication: rethinking roles and relationships of humans and machines in news. Digit J 7(4):409–427. https://doi.org/10.1080/21670811.2019.1577147
Lindén C-G (2017) Algorithms for journalism: the future of news work. J Media Innov 4(1):60–76. https://doi.org/10.5617/jmi.v4i1.2420
McCroskey JC, Young TJ (1979) The use and abuse of factor analysis in communication research. Hum Commun Res 5(4):375–382. https://doi.org/10.1111/j.1468-2958.1979.tb00651.x
McHugh M (2015) Tesla’s cars now drive themselves, Kinda. Wired. https://www.wired.com/2015/10/tesla-self-driving-over-air-update-live/. Accessed 23 June 2021
McWhorter C (2019) News media literacy: effects of consumption. Int J Commun 13:19
Merikle PM, Smilek D, Eastwood JD (2001) Perception without awareness: perspectives from cognitive psychology. Cognition 79(1–2):115–134. https://doi.org/10.1016/s0010-0277(00)00126-8
Miroshnichenko A (2018) AI to bypass creativity. Will robots replace journalists? (The Answer Is “Yes”). Information. https://doi.org/10.3390/info9070183
Moses L (2017) The Washington Post’s robot reporter has published 850 articles in the past year. Digiday, p 4
Nelson TL (2008) Perception Question. In: Lavrakas P (ed) Encyclopedia of survey research methods. SAGE Publications Inc., pp 580–580. https://doi.org/10.4135/9781412963947
The Nielsen total audience report: Q1 2019 (Media Q2 2019) (2019) The Nielsen Company
Nilsson NJ (1998) Artificial intelligence: a new synthesis (MU Online). Morgan Kaufmann Publishers
Operto S (2019) Evaluating public opinion towards robots: A mixed-method approach. Paladyn J Behav Robot 10(1):286–297. https://doi.org/10.1515/pjbr-2019-0023
Poole D, Mackworth A, Goebel R (1998) Computational intelligence: A logical approach. Oxford University Press
Ray C, Mondada F, Siegwart R (2008) What do people expect from robots? In: 2008 IEEE/RSJ International Conference on intelligent robots and systems, pp 3816–3821. https://doi.org/10.1109/IROS.2008.4650714
Russell SJ, Norvig P (2010) Artificial intelligence: A modern approach (MU Engineering Reserve; 3rd ed). Prentice Hall
Seuwou P, Banissi E, Ubakanma G, Sharif MS, Healey A (2016) Actor-network theory as a framework to analyse technology acceptance model’s external variables: the case of autonomous vehicles. In: Jahankhani H, Carlile A, Emm D, Hosseinian-Far A, Brown G, Sexton G, Jamal A (eds) Global security, safety and sustainability—the security challenges of the connected world, vol 630. Springer International Publishing, pp 305–320. https://doi.org/10.1007/978-3-319-51064-4_24
Sheehan KB (2018) Crowdsourcing research: data collection with Amazon’s Mechanical Turk. Commun Monogr 85(1):140–156. https://doi.org/10.1080/03637751.2017.1342043
Special Eurobarometer 460: Attitudes towards the impact of digitisation and automation on daily life (Special No. 20173564) (2017) European Commission. http://data.europa.eu/euodp/en/data/dataset/S2160_87_1_460_ENG. Accessed 07 Dec 2019
Stein J-P, Ohler P (2017) Venturing into the uncanny valley of mind—the influence of mind attribution on the acceptance of human-like characters in a virtual reality setting. Cognition 160:43–50. https://doi.org/10.1016/j.cognition.2016.12.010
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, pp 73–100
Sundar SS, Nass C (2001) Conceptualizing sources in online news. J Commun 51(1):52–72. https://doi.org/10.1111/j.1460-2466.2001.tb02872.x
Tomlin RS, Villa V (1994) Attention in cognitive science and second language acquisition. Stud Second Lang Acquis 16(2):183–203. https://doi.org/10.1017/S0272263100012870
van Dalen A (2012) The Algorithms Behind the Headlines. Journal Pract 6(5–6):648–658. https://doi.org/10.1080/17512786.2012.667268
Waddell TF (2019) Can an algorithm reduce the perceived bias of news? Testing the effect of machine attribution on news readers’ evaluations of bias, anthropomorphism, and credibility. J Mass Commun Q 96(1):82. https://doi.org/10.1177/1077699018815891
Wagner I (2020) Tesla—statistics and facts. Statista. https://www.statista.com/topics/2086/tesla/. Accessed 06 Jan 2021
White JB (2014) Tesla aims to leapfrog rivals. Wall Street J. https://online.wsj.com/articles/tesla-aims-to-leapfrog-rivals-1412980889. Accessed 23 June 2021
Witschge T, Anderson CW, Domingo D, Hermida A (2016) Emotion and journalism. In: Witschge T, Anderson CW, Domingo D, Hermida A (eds) The SAGE handbook of digital journalism. SAGE, pp 128–143
Zalatimo S (2018) Entering the next century with a new Forbes experience. Forbes. https://www.forbes.com/sites/forbesproductgroup/2018/07/11/entering-the-next-century-with-a-new-forbes-experience/. Accessed 22 June 2021
Zheng Y, Zhong B, Yang F (2018) When algorithms meet journalism: The user perception to automated news in a cross-cultural context. Comput Hum Behav 86:266–275. https://doi.org/10.1016/j.chb.2018.04.046
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Owsley, C.S., Greenwood, K. Awareness and perception of artificial intelligence operationalized integration in news media industry and society. AI & Soc (2022). https://doi.org/10.1007/s00146-022-01386-2