Red Algorithms: A Scoping Review on the Impact and Integration of Socialist Principles in AI Technologies
Artificial intelligence (AI) is often designed to maximize profit and efficiency, but what if it were built to serve the public good instead? This project explores how AI can be developed with socialist principles, focusing on fairness, collective benefit, and social welfare. Through a systematic review of existing research, it identifies key trends, challenges, and real-world applications of AI designed for public interest rather than corporate profit.
By analyzing historical and modern examples, the study examines how AI can be used to improve public services, reduce inequality, and strengthen community governance. It draws on theories that explore the relationship between technology, society, and economic systems.
The project will result in academic publications, policy recommendations, and public discussions through webinars and workshops. By shifting the conversation beyond profit-driven AI, it provides a foundation for alternative approaches that prioritize people and society over market interests.
Main Research Topics
- Artificial intelligence and Ideology
- Socialist AI and Governance
- Political Economy of Technology
- Digital Labor and Automation
- Algorithmic Equity
Curriculum Vitae
- Public Health Officer, WHO Country Office, Croatia (2024–Present)
- Project Coordinator, Novi Sindikat (2023–2024)
- Postdoctoral Researcher, Digit-HeaL Lab, Catholic University of Croatia (Digit-HeaL) (2020–2022)
- Program Director and Researcher, ARETE Institute for Sustainable Prosperity, FEAST Horizon project HORIZON-CL6-2021-FARM2FORK-01-15 (2019–2024)
- Postdoctoral Researcher, University College London (2018–2019)
- Marie Curie PhD Fellow, European Commission, Sapienza University of Rome (2014–2017)
Publications and Presentations
- Tomičić, A., & Gjorgjioska, M. A. (2024). Epistemic inequality in the digital era: Unpacking biases in digital mental health. Theory & Psychology, 34(5), 789–805. https://doi.org/10.1177/09593543241279131
- Ali, S. M., Paragi, B., Daly, A. C., Gjorgjioska, A., Hespanhol, L., Kerasidou, X., Kouadri Mostéfaoui, S., Oyeniji, O., & Tomičić, A. (2024). The (Un)bearable whiteness of AI ethics. In D. J. Gunkel (Ed.), Handbook on the ethics of artificial intelligence (p. 218). Edward Elgar Publishing.
- Tomičić, A., Malešević, A., & Čartolovni, A. (2022). Ethical, legal, and social issues of digital phenotyping as a future solution for present-day challenges: A scoping review. Science and Engineering Ethics, 28(1), 1–25. https://doi.org/10.1007/s11948-021-00354-1
- Gupta, S., Campos Zeballos, J., del Río Castro, G., Tomičić, A., Andrés Morales, S., Mahfouz, M., Osemwegie, I., Phemia Comlan Sessi, V., Schmitz, M., Mahmoud, N., & Inyaregh, M. (2023). Operationalizing digitainability: Encouraging mindfulness to harness the power of digitalization for sustainable development. Sustainability, 15(8), 6844. https://doi.org/10.3390/su15086844
- Čartolovni, A., Tomičić, A., & Lazić-Mosler, E. (2022). Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review. International Journal of Medical Informatics, 169, 104904. https://doi.org/10.1016/j.ijmedinf.2022.104904