Recognize the bias? News media partisanship shapes the coverage of facial recognition technology in the United States

Abstract

Media exists as the primary route through which the public learns about new technologies and thus plays an important role in shaping public sentiments. This article examines the influence of news media partisanship on the coverage of the controversial artificial intelligence (AI) technology facial recognition. A mixed-methods content analysis of news articles (N = 451) from 23 US-based news outlets highlights the emergence of several frames in coverage of facial recognition pertaining to issues of privacy and surveillance, bias, technology’s ability to provide solutions, and its problematic development and implementation. Coverage was differentiated by partisanship, whereby left-leaning media focused more on ethical problems associated with the technology compared to their right-leaning peers who highlighted its abuses by foreign governments. Right-leaning media also referred more to technology’s positive uses, such as helping law enforcement, compared to left-leaning media. Finally, AI companies were the most dominant suppliers of information to the media regarding the technology.

Keywords

Content analysis coverage facial recognition news media partisan

Get full access to this article

View all access options for this article.

References

Ad Fontes Media (2020) The media bias chart. Available at: https://www.adfontesmedia.com/interactive-media-bias-chart-2/

Allyn

(2020) “The computer got it wrong”: how facial recognition led to false arrest of Black man. NPR, 24 June. Available at: https://www.npr.org/2020/06/24/882683463/the-computer-got-it-wrong-how-facial-recognition-led-to-a-false-arrest-in-michig

Arceneaux

Schmitz Weiss

(2010) Seems stupid until you try it: press coverage of Twitter, 2006-9. New Media & Society 12(8): 1262–1279.

Benjamin

(2016) Catching our breath: critical race STS and the carceral imagination. Engaging Science, Technology, and Society 2: 145–156.

Bennett

(1990) Toward a theory of press-state relations. Journal of Communication 40(2): 103–125.

Bennett

(2015) Indexing theory. In: Mazzoleni

(ed.) The International Encyclopedia of Political Communication. Wiley-Blackwell, pp. 1–5.

Bennett

Pfetsch

(2018) Rethinking political communication in a time of disrupted public spheres. Journal of Communication 68(2): 243–253.

Bourgonjon

Vandermeersche

De Wever

, et al. (2016) Players’ perspectives on the positive impact of video games: a qualitative content analysis of online forum discussions. New Media & Society 18(8): 1732–1749.

Buolamwini

Gebru

(2018) Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on fairness, accountability and transparency, January, pp. 77–91. New York: ACM.

10.

Bracho-Sanchez

(2019, April 10) Technology that “never sleeps, never gives up” looking for missing and exploited children. CNN. Available at: https://www.cnn.com/2019/04/10/health/missing-exploited-childrentechnology/index.html

11.

Byrne

(2020) Facial recognition scanners at US airports under scrutiny as Philadelphia launches pilot program. Fox News, 29 January. Available at: https://www.foxnews.com/tech/facial-recognition-machines-airports-under-scrutiny

12.

Calo

(2019) How we talk about AI (and why it matters). In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society.

13.

Cappella

Jamieson

(1997) The Cognitive Bases for Framing Effects. In: Cappella

Jamieson

(eds) Spiral of Cynicism: The press and the public good. New York: Oxford University Press,pp.58-86.

14.

Cave

Craig

Dihal

, et al. (2018) Portrayals and Perceptions of AI and Why They Matter. The Royal Society. Available at: https://royalsociety.org/-/media/policy/projects/ai-narratives/AI-narratives-workshop-findings.pdf

15.

Cayford

Pieters

van Gelder

PHAJM

(2020) Wanting it all–public perceptions of the effectiveness, cost, and privacy of surveillance technology. Journal of Information, Communication and Ethics in Society 18: 10–27.

16.

Chuan

Tsai

WHS

Cho

(2019) Framing Artificial Intelligence in American Newspapers. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. Association of Computing Machinery, pp. 339–344.

17.

De Vreese

(2005) News framing: theory and typology. Information Design Journal & Document Design 13(1): 48–59.

18.

Duffy

(2020) Microsoft president calls for federal regulation of facial recognition technology. CNN, 18 June. Available at: https://www.cnn.com/2020/06/18/tech/brad-smith-microsoft-facial-recognition/index.html

19.

Eireiner

(2020) Imminent dystopia? Media coverage of algorithmic surveillance at Berlin-Südkreuz. Internet Policy Review 9(1): 1–19.

20.

Fast

Horvitz

(2016) Long-term trends in the public perception of artificial intelligence. arXiv preprint arXiv:1609.04904.

21.

Garvey

Maskal

(2020) Sentiment analysis of the news media on artificial intelligence does not support claims of negative bias against artificial intelligence. OMICS: A Journal of Integrative Biology 24(5): 286–299.

22.

Gates

(2011) Our Biometric Future. New York: New York University Press.

23.

Goldberg

(2014) Bias: A CBS Insider Exposes How the Media Distort the News. Washington, DC: Regnery Publishing.

24.

Grind

McMillan

Mathews

(2020) To track virus, governments weigh surveillance tools that push privacy limits. WSJ, 17 March. Available at: https://www.wsj.com/articles/to-track-virus-governments-weigh-surveillance-tools-that-push-privacy-limits-11584479841

25.

Grother

Ngan

Hanaoka

(2019) Face recognition vendor test (FRVT) part 3: demographic effects. National Institute of Standards and Technology, Tech. Rep. NISTIR 8280. Gaithersburg, MD: National Institute of Standards and Technology.

26.

Harder

Sevenans

Van Aelst

(2017) Intermedia agenda setting in the social media age: how traditional players dominate the news agenda in election times. The International Journal of Press/Politics 22(3): 275–293.

27.

Heldebrandt

(2019) How a popular media bias chart determines what news can be trusted. Gateway Journalism Review 48(355): 20–22.

28.

Hill

(2020) The secretive company that might end privacy as we know it. The New York Times, 18 January. Available at: https://www.nytimes.com/2020/01/18/technology/clearview-privacy-facial-recognition.html

29.

Hsieh

H-F

Shannon

(2005) Three approaches to qualitative content analysis. Qualitative Health Research 15(9): 1277–1288.

30.

Iyengar

(2020) Apple suspends controversial facial recognition app Clearview AI from its developer program. CNN, 28 February. Available at: https://www.cnn.com/2020/02/28/tech/clearview-ai-apple-account-disabled/index.html

31.

Klein

(2020) Under cover of mass death, Andrew Cuomo calls in the billionaires to build a high-tech dystopia. The Intercept, 8 May. Available at: https://theintercept.com/2020/05/08/andrew-cuomo-eric-schmidt-coronavirus-tech-shock-doctrine/

32.

Kuypers

(Ed.) (2009) Rhetorical Criticism: Perspectives in Action. Lexington Books.

33.

Lapperruque

(2020) No, Clearview AI’s creepy plan to spy on us is not “free speech.” The Guardian, 14 February. Available at: http://www.theguardian.com/commentisfree/2020/feb/14/clearview-ai-facial-recognition-technology

34.

Larcinese

Puglisi

Snyder

Jr (2011) Partisan bias in economic news: evidence on the agenda-setting behavior of US newspapers. Journal of Public Economics 95(9–10): 1178–1189.

35.

Llenas

(2020) New facial recognition app promises to solve crimes but critics say it means end of privacy. Fox News, 14 February. Available at: https://www.foxnews.com/us/new-facial-recognition-app-promises-to-solve-crimes-critics-say-its-the-end-of-privacy

36.

Lyon

Zureik

(1996) Surveillance, privacy, and the new technology. In: Lyon

Zureik

(eds) Computers, Surveillance, and Privacy. University of Minnesota Press, pp. 1–18.

37.

Lyons

(2020) ICE just signed a contract with facial recognition company. Clearview AI, 14 August. Available at: https://www.theverge.com/2020/8/14/21368930/clearview-ai-ice-contract-privacy-immigration

38.

McCombs

(2005) A look at agenda-setting: past, present and future. Journalism Studies 6: 543–557.

39.

McCombs

(2018) Setting the Agenda: Mass Media and Public Opinion. Hoboken, NJ: John Wiley & Sons.

40.

Mayring

(2004) Qualitative content analysis. In: Flick

von Kardoff

Steinke

(eds) A Companion to Qualitative Research. London: SAGE, pp. 159–176.

41.

Marr

(2019) Facial recognition technology: here are the important pros and cons. Forbes Magazine, 19 August. Available at: https://www.forbes.com/sites/bernardmarr/2019/08/19/facial-recognition-technology-here-are-the-important-pros-and-cons/

42.

Merkley

(2019) Partisan bias in economic news content: New evidence. American Politics Research 47(6): 1303–1323.

43.

National Academies of Sciences, Engineering and Medicine, Division of Behavioral and Social Sciences and Education, & Committee on the Science of Science Communication: A Research Agenda (2017) Communicating Science Effectively: A Research Agenda. Washington, DC: National Academies Press.

44.

Nisbet

Cooper

Garrett

(2015) The partisan brain: how dissonant science messages lead conservatives and liberals to (dis) trust science. The ANNALS of the American Academy of Political and Social Science 658(1): 36–66.

45.

Nolan

(2020) Creepy facial recognition company Clearview admits entire client list stolen. Breitbart, 27 February. Available at: https://www.breitbart.com/tech/202/02/27/creepy-facial-recognition-company-clearview-admits-entire-client-list-stolen/

46.

Ouchchy

Coin

Dubljević

(2020) AI in the headlines: the portrayal of the ethical issues of artificial intelligence in the media. AI & SOCIETY 4: 1–10.

47.

Porter

(2020) A black man was wrongfully arrested because of facial recognition. The Verge, 24 June. Available at: https://www.theverge.com/2020/6/24/21301759/facial-recognition-detroit-police-wrongful-arrest-robert-williams-artificial-intelligence

48.

Rössler

(2001) Between online heaven and cyberhell: the framing of the internet by traditional media coverage in Germany. New Media & Society 3(1): 49–66.

49.

Sarrafzadeh

Alexander

Dadgostar

, et al. (2006) See me, teach me: facial expression and gesture recognition for intelligent tutoring systems. In: 2006 Innovations in Information Technology, Dubai, United Arab Emirates, 19–21 November, pp. 1–5. New York: IEEE.

50.

Scheufele

(2000) Agenda-setting, priming, and framing revisited: another look at cognitive effects of political communication. Mass Communication and Society 3(2–3): 297–316.

51.

Scheufele

Lewenstein

(2005) The public and nanotechnology: how citizens make sense of emerging technologies. Journal of Nanoparticle Research 7(6): 659–667.

52.

Stupp

(2020) The humble office ID badge is about to be unrecognizable. The Wall Street Journal, 6 January. Available at: https://www.wsj.com/articles/the-humble-office-id-badge-is-about-to-be-unrecognizable-11578333651

53.

Sun

Zhai

Shen

, et al. (2020) Newspaper coverage of artificial intelligence: a perspective of emerging technologies. Telematics and Informatics 53: 101433.

54.

Vaismoradi

Jones

Turunen

, et al. (2016) Theme development in qualitative content analysis and thematic analysis. Journal of Nursing Education and Practice 6(5): 100.

55.

Vargo

Guo

(2017) Networks, big data, and intermedia agenda setting: an analysis of traditional, partisan, and emerging online U.S. news. Journalism & Mass Communication Quarterly 94(4): 1031–1055.

56.

Weaver

Lively

Bimber

(2009) Searching for a frame: news media tell the story of technological progress, risk, and regulation. Science Communication 31(2): 139–166.