Toward ethical AI. Addressing bias, privacy, and accountability in data-driven systems
DOI:
https://doi.org/10.34767/SIIP.2025.01.04Keywords:
AI ethics, data-driven systems, algorithmic bias, privacy, accountability, ethical governanceAbstract
This article investigates the ethical implications of AI and data-driven systems, with a focus on how bias, privacy, and security concerns shape the deployment of AI technologies across various domains. The primary problem addressed is the tendency of AI algorithms to amplify societal and technical biases, thereby undermining fairness and potentially harming marginalized groups. However, algorithmic and AI decision-making can also help reduce bias by making processes more transparent and fact-based, provided the systems are developed ethically. To explore these challenges, the research adopts a conceptual and qualitative approach, synthesizing insights from existing policy frameworks, case studies, and scholarly literature on AI ethics and data science. The core hypothesis posits that by integrating ethical principles, robust oversight, and multidisciplinary collaboration into the design and development of AI systems, it is possible to mitigate harmful biases, protect privacy, and enhance public trust. This hypothesis is examined by analyzing how different stakeholders – AI developers, policymakers, and end-users – contribute to the emergence or reduction of bias in algorithmic processes. The article concludes that comprehensive regulatory standards, improved transparency, and regular model updates are essential for minimizing risks, while active engagement by both experts and the broader public is critical to ensure AI technologies operate in a manner consistent with democratic and humanistic values.
References
Almutairi, Z., & Elgibreen, H. (2022). A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions. Algorithms, 15(5), 155. https://doi.org/10.3390/a15050155
Alpaydin, E. (2016). Machine Learning: The New AI. Cambridge Mass.: MIT Press.
Angwin, J. (2016). Sample: COMPAS Risk Assessment, COMPAS “CORE”. DocumentCloud. https://www.documentcloud.org/documents/2702103-Sample-Risk-Assessment- COMPAS-CORE (accessed: 16.03.2023).
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016, May 23). Machine bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing (accessed: 16.03.2023).
Baldridge, J. (2015, August 2). Machine learning and human bias: An uneasy pair. TechCrunch. https://techcrunch.com/2015/08/02/machine-learning-and-human-bias-an-uneasy-pair (accessed: 11.03.2025).
Boden, M.A. (2016). AI: Its Nature and Future. Oxford: Oxford University Press.
Bostrom, N. (2016). Superintelligence. Oxford: Oxford University Press.
Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81, 1–15.
Cataleta, M.S. (2020). Humane Artificial Intelligence: The Fragility of Human Rights Facing AI. East-West Center. http://www.jstor.org/stable/resrep25514
Chesney, R., & Citron, D. (2019). Deepfakes and the New Disinformation War: The Coming Age of Post-Truth Geopolitics. Foreign Affairs, 98(1), 147–155.
Chun, W.H.K. (2021). Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition. Cambridge Mass.: The MIT Press.
Coeckelbergh, M. (2020). AI Ethics. Cambridge Mass: The MIT Press.
Collins, A., & Ebrahimi, T. (2021). Risk governance and the rise of deepfakes. International Risk Governance Center, 1–4. https://doi.org/10.5075/epflirgc-285637
Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4(1), 1–5. https://doi.org/10.1126/sciadv.aao5580
D’Ignazio, C., & Klein, L.F. (2020). Data Feminism. Cambridge Mass.: The MIT Press.
European Commission AI HLEG (High-Level Expert Group on Artificial Intelligence) (2019). Ethics Guidelines for Trustworthy AI. Brussels, European Commission. https://ec.europa.eu/futurium/en/ai-alliance-consultation/guidelines (accessed: 12.04.2023).
Floridi, L. (2023). The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities. Oxford: Oxford University Press.
Fry, H. (2018). Hello World: How to Be Human in the Age of the Machine (1st ed.). London: Transworld Publishers.
Fuchs, C. (2013). Digital prosumption labour on social media in the context of the capitalist regime of time. Time & Society, 23(1), 97–123. https://doi.org/10.1177/0961463x13502117
Houser, K.A. (2019). Can AI Solve the Diversity Problem in the Tech Industry? Mitigating Noise and Bias in Employment Decision-Making. The Stanford Technology Law Review, 290, 290–354.
Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62(11), 917–926. https://doi.org/10.1002/ajim.23037
IBM Data and AI Team (2023, October 12). Types of Artificial Intelligence. https://www.ibm.com/think/topics/artificial-intelligence-types (accessed: 8.06.2024).
IEEE (N/A.). General Principles. The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. https://standards.ieee.org/wp-content/uploads/import/documents/other/ead1e_general_principles.pdf (accessed: 16.03.2024).
Iman, M., Arabnia, H.R., & Maribe Branchinst, R. (2022). Pathways to Artificial General Intelligence: A Brief Overview of Developments and Ethical Issues via Artificial Intelligence, Machine Learning, Deep Learning, and Data Science. In: H.R. Arabnia et al. (eds.), Advances in Artificial Intelligence and Applied Cognitive Computing (pp. 73–87). Berlin: Springer Nature.
Jagtiani, J., & Lemieux, C. (2019, January). The Roles of Alternative Data and Machine Learning in Fintech Lending: Evidence from the LendingClub Consumer Platform. Working Paper (Federal Reserve Bank of Philadelphia). https://doi.org/10.21799/frbp.wp.2018.15
Jemielniak, D., & Przegalińska, A.K. (2020). Collaborative Society. Cambridge Mass.: MIT Press.
Kahneman, D., Sibony, O., & Sunstein, C.R. (2021). Noise: A Flaw in Human Judgment. New York, NY: Littlle, Brown Spark.
Kelleher, J.D., & Tierney, B. (2018). Privacy and Ethics. In: eidem, Data Science (pp. 181–218). Cambridge Mass.: MIT Press.
Kilbertus, N., Gascon, A., Kusner, M., Veale, M., Gummadi, K., & Weller, A. (2018). Blind Justice: Fairness with Encrypted Sensitive Attributes. Proceedings of the 35th International Conference on Machine Learning, Proceedings of Machine Learning Research, 80, 2630–2639. https://proceedings.mlr.press/v80/kilbertus18a.html (accessed: 20.05.2023).
Kronmal, R.A. (1993). Spurious Correlation and the Fallacy of the Ratio Standard Revisited. Journal of the Royal Statistical Society. Series A (Statistics in Society), 156(3), 379–392. https://doi.org/10.2307/2983064
Lipińska, I. (2022). Etyka sztucznej inteligencji w dokumentach Unii Europejskiej w latach 2017–2020. Edukacja Filozoficzna, 73, 11–38. https://doi.org/10.14394/edufil.2022.0001
Lopez, P. (2021). Bias Does Not Equal Bias: A Socio-Technical Typology of Bias in Data-Based Algorithmic Systems. Internet Policy Review, 10(4). https://doi.org/10.14763/2021.4.1598
Madon, S., Willard, J., Guyll, M., & Scherr, K.C. (2011). Self‐Fulfilling Prophecies: Mechanisms, Power, and Links to Social Problems. Social and Personality Psychology Compass, 5(8), 578–590. https://doi.org/10.1111/j.1751-9004.2011.00375.x
McIntyre, L.C. (2018). Post-truth. Cambridge Mass.: MIT Press.
Mamak-Zdanecka, M., Stojkow, M., & Żuchowska-Skiba, D. (2019). Społeczny wymiar algorytmizacji. Humanizacja Pracy, 3(297), 9–20.
Mazurek, G. (2023). Sztuczna inteligencja, prawo i etyka. Krytyka Prawa, 15(1), 7–10. https://doi.org/10.7206/kp.2080-1084.567
Mejias, U.A., & Couldry, N. (2019). Datafication. Internet Policy Review, 8(4). https://doi.org/10.14763/2019.4.1428
Microsoft (2018). The Future Computed: Artificial Intelligence and Its Role in Society. Redmond: Microsoft Corporation.
O’Neil, C. (2016). Weapons of Math Destruction. New York: Crown Publishing Group.
Rai, N. (2021). Why ethical audit matters in artificial intelligence? AI and Ethics, 2(1), 209–218. https://doi.org/10.1007/s43681-021-00100-0
Sadok, H., Sakka, F., & El Hadi El Maknouzi, M. (2022). Artificial Intelligence and Bank Credit Analysis: A Review. Cogent Economics & Finance, 10(1), 2023262. https://doi.org/10.1080/23322039.2021.2023262
Searle, J.R. (1983). Can Computers Think? In: idem, Minds, Brains, and Science (pp. 28–41). Cambridge Mass.: Harvard University Press.
Smith, B., & Browne, C.A. (2021). Tools and Weapons: The Promise and the Peril of the Digital Age. London: Penguin Books.
Tencent Keen Security Lab (2019). Experimental security research of Tesla Autopilot [Technical report]. https://keenlab.tencent.com/en/whitepapers/Experimental_Security_Research_of_Tesla_Autopilot.pdf (accessed: 11.03.2025).
Vigen, T. (2015). Spurious Correlations. New York: Hachette Books.
Whittaker, L., Letheren, K., & Mulcahy, R. (2021). The Rise of Deepfakes: A Conceptual Framework and Research Agenda for Marketing. Australasian Marketing Journal, 29(3), 204–214. https://doi.org/10.1177/1839334921999479
