Artificial intelligence (AI) is becoming pervasive across society. However, its deployment appears to be a divisive issue. This research examines aversion to AI across the partisan divide. We analyze partisan media sentiment toward AI, a powerful driver of public opinion toward social issues. We conduct a text analysis of media articles on AI (N = 7,840) from several liberal-leaning and conservative-leaning media outlets. The results demonstrate that liberal-leaning media show a greater aversion to AI than conservative-leaning media. Furthermore, a mediation analysis suggests that liberal-leaning media are more concerned with AI magnifying social biases in society than conservative-leaning media, which drives the partisan media differences. Moreover, the results also show that media sentiment toward AI became more negative after George Floyd’s death, an event that heightened sensitivity about social biases in society. Implications for how these partisan media differences can polarize public opinion and policymaker support toward AI are discussed.
CaliskanA.BrysonJ. J.NarayananA. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186.
4.
ClintonJ. D.EnamoradoT. (2014). The national news media’s effect on Congress: How Fox News affected elites in Congress. The Journal of Politics, 76(4), 928–943.
5.
CookF. L.TylerT. R.GoetzE. G.GordonM. T.ProtessD.LeffD. R.MolotchH. L. (1983). Media and agenda setting: Effects on the public, interest group leaders, policy makers, and policy. Public Opinion Quarterly, 47(1), 16–35.
6.
DellaVignaS.KaplanE. (2007). The Fox News effect: Media bias and voting. The Quarterly Journal of Economics, 122(3), 1187–1234.
7.
DietvorstB. J.BhartiS. (2020). People reject algorithms in uncertain decision domains because they have diminishing sensitivity to forecasting error. Psychological Science, 31(10), 1302–1314.
8.
DuckittJ.WagnerC.Du PlessisI.BirumI. (2002). The psychological bases of ideology and prejudice: Testing a dual process model. Journal of Personality and Social Psychology, 83(1), 75–93.
9.
DunivinZ. O.YanH. Y.InceJ.RojasF. (2022). Black Lives Matter protests shift public discourse. Proceedings of the National Academy of Sciences, USA, 119(10), Article e2117320119.
10.
EstevaA.KuprelB.NovoaR. A.KoJ.SwetterS. M.BlauH. M.ThrunS. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.
11.
FaulF.ErdfelderE.LangA. G.BuchnerA. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191.
12.
FinkelE. J.BailC. A.CikaraM.DittoP. H.IyengarS.KlarS.MasonL.McGrathM. C.NyhanB.RandD. G. (2020). Political sectarianism in America. Science, 370(6516), 533–536.
13.
FrankM. R.AutorD.BessenJ. E.BrynjolfssonE.CebrianM.DemingD. J.FeldmanM.GrohM.LoboJ.MoroE. (2019). Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences, USA, 116(14), 6531–6539.
14.
FreyC. B.OsborneM. A. (2017). The future of employment: How susceptible are jobs to computerisation?Technological Forecasting and Social Change, 114, 254–280.
15.
FrybergS. A.StephensN. M.CovarrubiasR.MarkusH. R.CarterE. D.LaiducG. A.SalidoA. J. (2012). How the media frames the immigration debate: The critical role of location and politics. Analyses of Social Issues and Public Policy, 12(1), 96–112.
16.
GrahamJ.HaidtJ.NosekB. A. (2009). Liberals and conservatives rely on different sets of moral foundations. Journal of Personality and Social Psychology, 96(5), 1029–1046.
HorvitzE.MulliganD. (2015). Data, privacy, and the greater good. Science, 349(6245), 253–255.
20.
IyengarS.HahnK. S. (2009). Red media, blue media: Evidence of ideological selectivity in media use. Journal of Communication, 59(1), 19–39.
21.
JostJ. T.FedericoC. M.NapierJ. L. (2009). Political ideology: Its structure, functions, and elective affinities. Annual Review of Psychology, 60, 307–337.
22.
JostJ. T.GlaserJ.SullowayF. J.KruglanskiA. W. (2018). Political conservatism as motivated social cognition. Routledge.
23.
KayM.MatuszekC.MunsonS. A. (2015, April). Unequal representation and gender stereotypes in image search results for occupations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 3819–3828). Association for Computing Machinery.
24.
KimT. W.DuhachekA. (2020). Artificial intelligence and persuasion: A construal-level account. Psychological Science, 31(4), 363–380.
25.
KingG.SchneerB.WhiteA. (2017). How the news media activate public expression and influence national agendas. Science, 358(6364), 776–780.
26.
KoeneckeA.NamA.LakeE.NudellJ.QuarteyM.MengeshaZ.ToupsC.RickfordJ. R.JurafskyD.GoelS. (2020). Racial disparities in automated speech recognition. Proceedings of the National Academy of Sciences, USA, 117(14), 7684–7689.
27.
KundaA. (1990). The case for motivated reasoning. Psychological Bulletin, 108(3), 480–498.
MadanS.SavaniK.KatsikeasC. (2022). Privacy please: Power distance and people’s responses to privacy breaches across countries. Journal of International Business Studies, 54, 731–754.
30.
MargettsH.DorobantuC. (2019). Rethink government with AI. Nature, 568, 163–165.
31.
MayerJ.MutchlerP.MitchellJ. C. (2016). Evaluating the privacy properties of telephone metadata. Proceedings of the National Academy of Sciences, USA, 113(20), 5536–5541.
32.
McCombsM. E.ShawD. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176–187.
33.
NorbergP. A.HorneD. R.HorneD. A. (2007). The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of Consumer Affairs, 41(1), 100–126.
34.
ObermeyerZ.PowersB.VogeliC.MullainathanS. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
35.
PeckR. (2014). “You say rich, I say job creator””: How Fox News framed the great recession through the moral discourse of producerism. Media, Culture & Society, 36(4), 526–535.
36.
PennebakerJ. W.BoydR. L.JordanK.BlackburnK. (2015). The development and psychometric properties of LIWC2015. University of Texas.
37.
PuntoniS.ReczekR. W.GieslerM.BottiS. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131–151.
SobelM. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312.
40.
TokitaC. K.GuessA. M.TarnitaC. E. (2021). Polarized information ecosystems can reorganize social networks via information cascades. Proceedings of the National Academy of Sciences, USA, 118(50), Article e2102147118.
41.
TurowJ.HennessyM.DraperN.AkanbiO.VirgilioD. (2018). Divided we feel: Partisan politics drive Americans’ emotions regarding surveillance of low-income populations [A Report from the Annenberg School for Communication]. University of Pennsylvania.
42.
WardA. F.OlsenA. S.WegnerD. M. (2013). The harm-made mind: Observing victimization augments attribution of minds to vegetative patients, robots, and the dead. Psychological Science, 24(8), 1437–1445.
43.
ZouJ.SchiebingerL. (2018). AI can be sexist and racist—It’s time to make it fair. Nature, 559, 324–326.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
