Published October 8, 2025
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Algoritamska pristrasnost u UI: Ključni pojmovi, implikacije, i rješenja
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Description
Ovaj otvoreni obrazovni resurs (OER) istražuje algoritamske pristrasnosti u umjetnoj inteligenciji (UI), s posebnim fokusom na ključne pojmove, implikacije i moguća rješenja. Materijal objašnjava kako sistemske greške u podacima za učenje i u programiranju mogu dovesti do diskriminatornih obrazaca i nejednakih rezultata. Kroz primjere iz zdravstva, zapošljavanja i pravosudnog sistema prikazuju se posljedice pristrasnosti i naglašava potreba za kritičkim razumijevanjem tehnologije. Kao dio projekta GEDIS – Gender Diversity in Information Science, ovaj OER doprinosi razvoju kritičkog i pedagoškog pristupa razumijevanju rodnih i društvenih dimenzija umjetne inteligencije.
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Related works
- Is derived from
- Lesson: 10.5281/zenodo.17067091 (DOI)
References
- Bandara, R. J., K. Biswas, S. Akter, S. Shafique, and M. Rahman. "Addressing Algorithmic Bias in AI-Driven HRM Systems: Implications for Strategic HRM Effectiveness." Human Resource Management Journal, May 2025. https://doi.org/10.1111/1748-8583.12609.
- Jonker, Alexandra, and Julie Rogers. "What Is Algorithmic Bias?" IBM Think, September 20, 2024. https://www.ibm.com/think/topics/algorithmic-bias.
- Kalantzis, Mary, and Bill Cope. "Literacy in the Time of Artificial Intelligence." Reading Research Quarterly 60, no. 1 (November 23, 2024). https://doi.org/10.1002/rrq.591.
- Norori, Natalia, Qiyang Hu, Florence Marcelle Aellen, Francesca Dalia Faraci, and Athina Tzovara. "Addressing Bias in Big Data and AI for Health Care: A Call for Open Science." Patterns 2, no. 10 (2021): 100347. https://doi.org/10.1016/j.patter.2021.100347.
- Sheikh, Haroon, Corien Prins, and Erik Schrijvers. "Artificial Intelligence: Definition and Background." Research for Policy, January 2023, 15–41. https://doi.org/10.1007/978-3-031-21448-6_2.