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AI-Generated Hate Speech In Industrial Enterprises: A Systematic Review Of Challenges And Mitigation Strategies In The Industry 5.0 Era

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Języki publikacji
EN
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EN
This article presents a systematic review of the literature on AI-generated hate speech in industrial enterprises, considering the transition from Industry 4.0 to Industry 5.0. The study aims to identify key challenges related to the ethical, legal, and technological aspects of AI implementation in the industrial sector and proposes mitigation strategies to address the risks associated with AI-generated harmful content. The research highlights that AI systems, particularly those based on language models, can unintentionally propagate discriminatory or offensive content, posing risks to corporate reputation, organizational culture, and regulatory compliance. The study underscores the increasing role of regulations, such as the EU’s AI Act, which classifies AI-generated content systems as high-risk applications requiring stringent oversight. The article outlines various strategies to mitigate the effects of AI-generated hate speech, including bias detection in AI models, real-time content moderation mechanisms, and human oversight in AI interactions. Transparency, interdisciplinary collaboration, and adherence to ethical guidelines are emphasized as essential components of responsible AI deployment in industrial settings. In conclusion, the study highlights the necessity for further exploration of AI’s impact on digital communication within enterprises and the development of global regulatory standards to effectively manage AI-generated content in the Industry 5.0 era.
Wydawca
Rocznik
Tom
Strony
554--561
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
  • Czestochowa University of Technology Faculty of Management ul. Dabrowskiego 69, 42-201 Czestochowa, Poland
Bibliografia
  • 1. B. Azgin and S. Kiralp, “Surveillance, Disinformation, and Legislative Measures in the 21st Century: AI, Social Media, and the Future of Democracies,” Social Sciences, vol. 13, no. 10, p. 510, 2024, doi: 10.3390/socsci13100510.
  • 2. M.E. Cortés-Cediel, A. Segura-Tinoco, I. Cantador, and M.P. Rodríguez Bolívar, “Trends and challenges of e-government chatbots: Advances in exploring open government data and citizen participation content,” Government Information Quarterly, vol. 40, no. 4, p. 101877, 2023, doi: 10.1016/j.giq.2023.101877.
  • 3. R.Ó. Fathaigh, T. Dobber, F. Zuiderveen Borgesius, and J. Shires, “Microtargeted propaganda by foreign actors: An interdisciplinary exploration,” Maastricht Journal of European and Comparative Law, vol. 28, no. 6, pp. 856-877, 2021, doi: 10.1177/1023263X211042471.
  • 4. K. Kertysova, “Artificial Intelligence and Disinformation,” Secur. Hum. Rights, vol. 29, 1-4, pp. 55-81, 2018, doi: 10.1163/18750230-02901005.
  • 5. S. Saniuk, S. Grabowska, and A. Thibbotuwawa, “Challenges of industrial systems in terms of the crucial role of humans in the Industry 5.0 environment,” Production Engineering Archives, vol. 30, no. 1, pp. 94-104, 2024, doi: 10.30657/pea.2024.30.9.
  • 6. H. Iftikhar, M. Qureshi, J. Zywiołek, J.L. López-Gonzales, and O. Albalawi, “Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives,” Front. Environ. Sci., vol. 12, 2024, doi: 10.3389/fenvs.2024.1442644.
  • 7. L.Y. Hunter, C.D. Albert, J. Rutland, K. Topping, and C. Hennigan, “Artificial intelligence and information warfare in major power states: how the US, China, and Russia are using artificial intelligence in their information warfare and influence operations,” Defense & Security Analysis, vol. 40, no. 2, pp. 235-269, 2024, doi: 10.1080/14751798.2024.2321736.
  • 8. J.A. Goldstein, J. Chao, S. Grossman, A. Stamos, and M. Tomz, “How persuasive is AI-generated propaganda?,” PNAS Nexus, vol. 3, no. 2, pgae034, 2024, doi: 10.1093/pnasnexus/pgae034.
  • 9. J. Gonçalves, I. Weber, G.M. Masullo, M. Da Torres Silva, and J. Hofhuis, “Common sense or censorship: How algorithmic moderators and message type influence perceptions of online content deletion,” New Media & Society, vol. 25, no. 10, pp. 2595-2617, 2023, doi: 10.1177/14614448211032310.
  • 10. Wenzelburger, “Between Technochauvinism and Human-Centrism: Can Algorithms Improve Decision-Making in Democratic Politics?,” European Political Science, vol. 2022, p. 1, 2022.
  • 11. M.A. Khan et al., “Security and Privacy Issues and Solutions for UAVs in B5G Networks: A Review,” IEEE Trans. Netw. Serv. Manage., p. 1, 2024, doi: 10.1109/TNSM.2024.3487265.
  • 12. M. Husnain, Q. Zhang, M. Usman, K. Hayat, K. Shahzad, and M. W. Akhtar, “How Chatbot negative experiences damage consumer-brand relationships in hospitality and tourism? A mixed-method examination,” International Journal of Hospitality Management, vol. 126, p. 104076, 2025, doi: 10.1016/j.ijhm.2024.104076.
  • 13. Galantino, “How Will the EU Digital Services Act Affect the Regulation of Disinformation?,” SCRIPTed, vol. 20, p. 89, 2023.
  • 14. T. Baviera, L. Cano-Orón, and D. Calvo, “Tailored Messages in the Feed? Political Microtargeting on Facebook during the 2019 General Elections in Spain,” Journal of Political Marketing, pp. 1-20, 2023, doi: 10.1080/15377857.2023.2168832.
  • 15. T. Ahmad, E.A. Aliaga Lazarte, and S. Mirjalili, “A Systematic Literature Review on Fake News in the COVID-19 Pandemic: Can AI Propose a Solution?,” Applied Sciences, vol. 12, no. 24, p. 12727, 2022, doi: 10.3390/app122412727.
  • 16. F. Filgueiras, “The politics of AI: democracy and authoritarianism in developing countries,” Journal of Information Technology & Politics, vol. 19, no. 4, pp. 449-464, 2022, doi: 10.1080/19331681.2021.2016543.
  • 17. I. Hussain, M. Qureshi, M. Ismail, H. Iftikhar, J. Zywiołek, and J.L. López-Gonzales, “Optimal features selection in the high dimensional data based on robust technique: Application to different health database,” Heliyon, vol. 10, no. 17, e37241, 2024, doi: 10.1016/j.heliyon.2024.e37241.
  • 18. D. Helbing, Towards Digital Enlightenment. Cham: Springer International Publishing.
  • 19. J. Żywiołek, “Knowledge-Driven Sustainability: Leveraging Technology for Resource Management in Household Operations,” ECKM, vol. 25, no. 1, pp. 974-982, 2024, doi: 10.34190/eckm.25.1.2375.
  • 20. J. Żywiołek, “Empirical examination of AI-powered decision support systems: ensuring trust and transparency in information and knowledge security,” SPSUTOM, vol. 2024, no. 197, pp. 679-695, 2024, doi: 10.29119/1641-3466.2024.197.37.
  • 21. J. Żywiołek, A. Szymonik, T. Smal, Navigating Supply Chain Turbulence. New York: Productivity Press, 2025, https://doi.org/10.4324/9781003561736.
  • 22. H. Tumber and S. Waisbord, The Routledge Companion to Media Disinformation and Populism: Routledge.
  • 23. E. Fetahi, M. Hamiti, A. Susuri, J. Ajdari, and X. Zenuni, “AI-Based Hate Speech Detection in Albanian Social Media: New Dataset and Mobile Web Application Integration,” Int. J. Interact. Mob. Technol., vol. 18, no. 24, pp. 190-208, 2024, doi: 10.3991/ijim.v18i24.50851.
  • 24. J. Żywiołek, K. Mathiyazhagan, U. Shahzad, X. Zhao, and T. Saikouk, “Enhancing cognitive metrics in supply chain management through information and knowledge exchange,” IJLM, 2025, doi: 10.1108/IJLM-04-2024-0243.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e156e0e9-e7a8-4cf5-9315-1f9d2aef3d61
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