Sporting events have been the target of terrorist groups for decades due to their high profile. Since 9/11, a bias against Muslims has been evident in both traditional and social media with Muslims being depicted as likely perpetrators of attacks against sporting events. The 2024 Paris Olympics represents the first major sporting events in the age of generative artificial intelligence (AI) platforms. This study is the first to examine to what extent AI chatbots replicate anti-Islam bias with respect to terrorism and sporting events. The study is based on a series of “conversations” with chatbots on the popular platform “character.ai.” The analysis reveals a persistent anti-Islam bias, with chatbots “explaining” that Islam is more prone to commit attacks because it is more violent in nature than other religions while also depicting Muslims as the most likely culprits of attacks against the Olympic Games. The study indicates that AI chatbots perpetuate the mediated anti-Islamist bias in an even more unanimous and authoritative tone than traditional and social media.
AdiningrumR.MaharaniF.MohammadW. (2023). The perspectives of character AI personas regarding thoughts of user’s suicide obsession. Emika: Journal of Technology and Artificial Intelligence, 1(1), 1–7.
2.
AlattarH.BayoumiK.BinZ. (2022). Western media campaign against Qatar’s Organization of the World Cup 2022: Appearances and causes from the point of view of citizens and residents in the state of Qatar. Multilingual Academic Journal of Education and Social Sciences, 10(1), 81–98.
3.
AliA. E.KingS. (2021). “He could be dangerous”: Orientalism, deradicalization, and the representation of refugee Muslim boxers in TSN’s radical play. Sociology of Sport Journal, 38(4), 335–344.
AtkinsonM. (2015). Researching sport. In GiulianottiR. (Ed.), Routledge handbook of the sociology of sport (pp. 8–17). Routledge.
6.
AtkinsonM.YoungK. (2012). Shadowed by the corpse of war: Sport spectacles and the spirit of terrorism. International Review for the Sociology of Sport, 47(3), 286–306.
7.
BandyJ. (2021). Problematic machine behavior: A systematic literature review of algorithm audits. Proceedings of the ACM on Human-computer Interaction, 5(CSCW1), 1–34. https://doi.org/10.1145/3449148
8.
BandyJ.DiakopoulosN. (2021). Curating quality? How twitter’s timeline algorithm treats different types of news. Social Media + Society, 7(3), 20563051211041648.
9.
BuolamwiniJ.GebruT. (2018, January). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77–91). PMLR.
10.
CableJ.KilvingtonD.MottersheadG. (2022). “Racist behaviour is interfering with the game”: Exploring football fans’ online responses to accusations of racism in football. Soccer & Society, 23(8), 880–893.
CarterM.EglistonB. (2018, January). Audiencing on twitch. In Abstract proceedings of DiGRA 2018 conference: The game is the message (pp. 1–2). Digital Games Research Association.
13.
CastroM.ZonaU.BocciF. (2021, October). Cultures, intersections, networks. The role of algorithms in defining power relations based on gender, race, class, disability. In ATEE spring conference (pp. 42–58). Springer International Publishing.
CowgillB.TuckerC. E. (2020). Algorithmic fairness and economics. Columbia Business School Research Paper. http://dx.doi.org/10.2139/ssrn.3361280
16.
DanksD.LondonA. J. (2017, August). Algorithmic bias in autonomous systems. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017) (pp. 4691–4697). IJCAI.
DaviesC. (2022). From Munich to Christchurch: International sport and the threat of terrorism. Victoria University of Wellington Law Review, 53, 29.
19.
DonovanJ.CaplanR.MatthewsJ.HansonL. (2018). Algorithmic accountability: A primer. Data & Society, 18.
20.
FavarettoM.De ClercqE.ElgerB. S. (2019). Big data and discrimination: Perils, promises and solutions. A systematic review. Journal of Big Data, 6(1), 1–27.
GalilyY.TamirI. (2022). Back to the future: Contemporary thoughts and views on sport and terrorism from a global perspective. Israel Affairs, 28(4), 531–535.
24.
GalilyY.YarchiM.TamirI.Samuel-AzranT. (2016). The Boston game and the ISIS match: Terrorism, media, and sport. American Behavioral Scientist, 60(9), 1057–1067.
25.
GiulianottiR.KlauserF. (2012). Sport mega-events and ‘terrorism’: A critical analysis. International Review for the Sociology of Sport, 47(3), 307–323.
26.
HamiltonM. (2018). The sexist algorithm. Behavioral Sciences & the Law, 37(2), 145–157.
27.
HamzehM. (2015). Jordanian national football Muslim at players: Interrupting Islamophobia in FIFA’s hijab ban. Physical Education and Sport Pedagogy, 20(5), 517–531.
28.
HartmannJ.SchwenzowJ.WitteM. (2023). The political ideology of conversational AI: Converging evidence on ChatGPT’s pro-environmental, left-libertarian orientation. arXiv Preprint, 2023, arXiv:2301.01768.
KellyJ. (2017). The paradox of militaristic remembrance in British sport and popular culture. In ButterworthM. (Ed.), Sport and militarism: Contemporary global perspectives (pp. 149–162). Routledge.
31.
KilvingtonD.HyltonK.LongJ.BondA. (2022). Investigating online football forums: A critical examination of participants’ responses to football related racism and Islamophobia. Soccer & Society, 23(8), 849–864.
32.
KitchinR.LauriaultT.WilsonM. W. (2017). Understanding spatial media. In KitchinR.LauriaultT.WilsonM. W. (Eds.), Understanding spatial media (pp. 1–22). SAGE.
33.
KordzadehN.GhasemaghaeiM. (2022). Algorithmic bias: Review, synthesis, and future research directions. European Journal of Information Systems, 31(3), 388–409.
LumK.IsaacW. (2016). To predict and serve?Significance, 13(5), 14–19.
36.
MaharaniF.AdiningrumR.MohammadW. (2023). Character AI personas’ views on user’s psychological sin statements and self-blame. Arika: Journal of Digital Marketing and Consumer Behavior, 1(1), 1–8.
37.
MalcolmD.BairnerA.CurryG. (2010). “Woolmergate”: Cricket and the representation of Islam and Muslims in the British press. Journal of Sport and Social Issues, 34(2), 215–235.
38.
ManorI. (2023). The AI moves in: ChatGPT’s impact on digital diplomacy. USC Center on Public Diplomacy Blog.
39.
MaysonS. G. (2018). Bias in, bias out. Yale Law Journal, 128, 2218.
40.
MohammadW. (2023). The role of AI Waifu characters in supporting weaboos with posttraumatic relationship syndrome (PTRS). Sinergi International Journal of Psychology, 1(2), 77–95.
41.
NobleS. U. (2018). Algorithms of oppression. In Algorithms of oppression. New York University Press.
42.
NtoutsiE.FafaliosP.GadirajuU.IosifidisV.NejdlW.VidalM-E.RuggieriS.TuriniF.PapadopoulosS.KrasanakisE.KompatsiarisI.Kinder-KurlandaK.WagnerC.KarimiF.FernandezM.AlaniH.BerendtB.KruegelT.HeinzeC.. . .StaabS. (2020). Bias in data-driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356.
43.
O’NeilC. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
RichardsA.FusseyP.SilkeA. (Eds.). (2010). Terrorism and the Olympics: Major event security and lessons for the future. Routledge.
46.
RoselliD.MatthewsJ.TalagalaN. (2019, May). Managing bias in AI. In LiuL.WhiteR. (Eds.), Companion proceedings of the 2019 world wide web conference (pp. 539–544). Association for Computing Machinery.
RutinowskiJ.FrankeS.EndendykJ.DormuthI.RoidlM.PaulyM. (2024). The self-perception and political biases of ChatGPT. Human Behavior and Emerging Technologies, 2024(1), 1–9. https://doi.org/10.48550/arXiv.2304.07333
49.
SamieS. F.SehlikogluS. (2015). Strange, incompetent and out-of-place: Media, Muslim sportswomen and London 2012. Feminist Media Studies, 15(3), 363–381.
50.
SasukeF.TakemotoK. (2023). Revisiting the political biases of ChatGPT. Frontiers in Artificial Intelligence, 6, 1–6.
51.
SchillerK.YoungC. (2010). The 1972 Olympics and the making of modern Germany. University of California Press.
52.
SegevE. (2010). Google and the digital divide: The bias of online knowledge (Chandos Internet Series). Chandos Publishing.
SilesI. (2023). Living with algorithms: Agency and user culture in Costa Rica. MIT Press.
55.
SilkM.FalcousM. (2005). One day in September and a week in February: Mobilizing American (sporting) nationalisms. Sociology of Sport Journal, 22(4), 447–471.
56.
Sullivan-PaulM. (2023). How would ChatGPT vote in a federal election? A study exploring algorithmic political bias in artificial intelligence [Doctoral dissertation, School of Public Policy, University of Tokyo].
57.
SwartK.HussainU. (2023). FIFA 2022 World Cup in Qatar: Under the gaze of western media. In KobayashiK.HorneJ.ChoY.LeeJ. W. (Eds.), Sports mega-events in Asia (pp. 199–222). Springer Nature Singapore.
TooheyK.TaylorT. (2023). Mega events, fear, and risk: Terrorism at the Olympic Games. In GirginovV. (Ed.), The Olympics (pp. 329–343). Routledge.
60.
UlloaM.RothrockB.AhmadF. S.JacobsM. (2022). Invisible clinical labor driving the successful integration of AI in healthcare. Frontiers in Computer Science, 4, 1045704.
61.
WeimannG. (2005). The theater of terror: The psychology of terrorism and the mass media. Journal of Aggression, Maltreatment & Trauma, 9(3–4), 379–390.
