Ethics and bias mitigation in AI systems: Technical and regulatory trends

Ética y mitigación de sesgos en sistemas de IA: Tendencias técnicas y regulatorias

Published 2024-12-30

Open | Download
PDF (Spanish) FLIP
Issue
Vol. 2 No. 25 (2024): Revista Hashtag 2024B
Section
Artículos
How to Cite
Tovar Cardozo, G. (2024). Ethics and bias mitigation in AI systems: Technical and regulatory trends. #ashtag, 2(25), 28-39. https://doi.org/10.52143/2346139X.1073
DOI

https://doi.org/10.52143/2346139X.1073
Dimensions
PlumX
Citations
Crossref
Citations in Crossref
Scopus Citations in Scopus
Google Scholar Google Sholar Citation
Europe PMC
Citations in Europe PMC

How to Cite

Tovar Cardozo, G. (2024). Ethics and bias mitigation in AI systems: Technical and regulatory trends. #ashtag, 2(25), 28-39. https://doi.org/10.52143/2346139X.1073

Download Citation

Ginna Tovar Cardozo
Universidad de la Amazonia. Florencia, Colombia
Sin roles de crédito asignados.
https://orcid.org/0000-0001-9705-5961
Show authors biography

This article critically analyzes technical and regulatory strategies to mitigate biases in artificial intelligence (AI) systems, an urgent challenge given the social impact of these technologies. Through a documentary review of articles in Scopus (2018–2022) in Spanish and English, four thematic axes were identified: bias detection techniques, mitigation methods (data rebalancing, adversarial debiasing), international regulatory frameworks (EU, U.S., OECD), and challenges in real-world implementation (trade-offs between equity and performance). The results reveal that, despite advances in fairness-aware algorithms, gaps persist between theory and practice, especially in industrial contexts. It is concluded that ethics in AI requires multidisciplinary approaches that integrate technical solutions with adaptable governance, community participation, and continuous audits

Article visits 79 | PDF visits 62

Downloads

Download data is not yet available.
  1. Addey, C. (2021). Passports to the Global South, UN flags, favourite experts: understanding the interplay between UNESCO and the OECD within the SDG4 context. Globalisation, Societies and Education, 19, 593 - 604. https://doi.org/10.1080/14767724.2020.1862643
  2. Akintola, B., Jagboro, G., Ojo, G., & Odediran, S. (2020). Effectiveness of Mechanisms for Enforcement of Ethical Standards in the Construction Industry. Journal of Construction Business and Management, 4(1), 1–12. https://doi.org/10.15641/JCBM.4.1.530
  3. Auld, E., Rappleye, J., & Morris, P. (2018). PISA for Development: how the OECD and World Bank shaped education governance post-2015. Comparative Education, 55, 197 - 219. https://doi.org/10.1080/03050068.2018.1538635
  4. Balabin, A. (2019). The Implementation of Corporate Governance Standards in Large Russian Companies. Proceedings of the International Scientific Conference "Far East Con" (ISCFEC 2018). https://doi.org/10.2991/iscfec-18.2019.24
  5. Baros, J., Sotola, V., Bilik, P., Martínek, R., Jaros, R., Danys, L., & Simoník, P. (2022). Review of Fundamental Active Current Extraction Techniques for SAPF. Sensors (Basel, Switzerland), 22. https://doi.org/10.3390/s22207985
  6. Baykurt, B. (2022). Algorithmic accountability in U. S. cities: Transparency, impact, and political economy. Big Data & Society, 9. https://doi.org/10.1177/20539517221115426
  7. Borges Machín, A. Y. y González Bravo, Y. L. (2022). Educación comunitaria para un envejecimiento activo: experiencia en construcción desde el autodesarrollo. Región Científica, 1(1), 202212. https://doi.org/10.58763/rc202213
  8. Busuioc, M. (2020). Accountable Artificial Intelligence: Holding Algorithms to Account. Public Administration Review, 81, 825 - 836. https://doi.org/10.1111/puar.13293
  9. Carter, E., Onyeador, I., & Lewis, N. (2020). Developing & delivering effective anti-bias training: Challenges & recommendations. Behavioral Science & Policy, 6, 57 - 70. https://doi.org/10.1177/237946152000600106
  10. Chalmers, P. (2018). Model-Based Measures for Detecting and Quantifying Response Bias. Psychometrika, 83, 696 - 732. https://doi.org/10.1007/s11336-018-9626-9
  11. Cruz, I., Troffaes, M., Lindström, J., & Sahlin, U. (2022). A robust Bayesian bias‐adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis. Statistics in Medicine, 41, 3365 - 3379. https://doi.org/10.1002/sim.9422
  12. Czarnowska, P., Vyas, Y., & Shah, K. (2021). Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics. Transactions of the Association for Computational Linguistics, 9, 1249-1267. https://doi.org/10.1162/tacl_a_00425
  13. De Paolis Kaluza, M., Jain, S., & Radivojac, P. (2022). An Approach to Identifying and Quantifying Bias in Biomedical Data. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 28, 311 - 322. https://doi.org/10.1142/9789811270611_0029
  14. Delobelle, P., Tokpo, E., Calders, T., & Berendt, B. (2022). Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models. , 1693-1706. https://doi.org/10.18653/v1/2022.naacl-main.122
  15. Dobler, C. C., Morrow, A. S., & Kamath, C. C. (2019). Clinicians' cognitive biases: a potential barrier to implementation of evidence-based clinical practice. BMJ evidence-based medicine, 24(4), 137–140. https://doi.org/10.1136/bmjebm-2018-111074Fernández-Castilla, B., Declercq, L., Jamshidi, L., Beretvas, S., Onghena, P., & Van Den Noortgate, W. (2019). Detecting Selection Bias in Meta-Analyses with Multiple Outcomes: A Simulation Study. The Journal of Experimental Education, 89, 125 - 144. https://doi.org/10.1080/00220973.2019.1582470
  16. Floridi, L. (2019). Establishing the rules for building trustworthy AI. Nature Machine Intelligence, 1, 261-262. https://doi.org/10.1038/S42256-019-0055-Y
  17. Goldfarb-Tarrant, S., Marchant, R., Sánchez, R., Pandya, M., & Lopez, A. (2020). Intrinsic Bias Metrics Do Not Correlate with Application Bias. ArXiv, abs/2012.15859. https://doi.org/10.18653/v1/2021.acl-long.150
  18. Gómez Cano, C. A. (2022). Ingreso, permanencia y estrategias para el fomento de los Semilleros de Investigación en una IES de Colombia. Región Científica, 1(1), 20226. https://doi.org/10.58763/rc20226
  19. Gómez Miranda, O. M. (2022). La franquicia: de la inversión al emprendimiento. Región Científica, 1(1), 20229. https://doi.org/10.58763/rc20229
  20. Gómez-Cano, C. y Sánchez-Castillo, V. (2021). Evaluación del nivel de madurez en la gestión de proyectos de una empresa prestadora de servicios públicos. Económicas CUC, 42(2), 133-144. https://doi.org/10.17981/econcuc.42.2.2021.Org.7
  21. Gray, C. (2022). Overcoming Political Fragmentation: The Potential of Meso-Level Mechanisms. International Journal of Health Policy and Management, 12. https://doi.org/10.34172/ijhpm.2022.7075
  22. Guzmán, D. L., Gómez-Cano, C. y Sánchez-Castillo, V. (2022). Construcción del Estado a partir de la participación ciudadana. Revista Academia & Derecho, 14(25). https://doi.org/10.18041/2215-8944/academia.25.10601
  23. Han, X., Baldwin, T., & Cohn, T. (2022). Towards Equal Opportunity Fairness through Adversarial Learning. ArXiv, abs/2203.06317. https://doi.org/10.48550/arXiv.2203.06317
  24. Heiden, B., Tonino-Heiden, B., Obermüller, T., Loipold, C., & Wissounig, W. (2020). Rising from systemic to industrial artificial intelligence applications (AIA) for predictive decision making (PDM): Four examples. En Y. Bi, R. Bhatia & S. Kapoor (Eds.), Intelligent systems and applications. IntelliSys 2019 (Advances in Intelligent Systems and Computing, 1038, pp. 1222–1233). Springer. https://doi.org/10.1007/978-3-030-29513-4_94Higuera Carrillo, E. L. (2022). Aspectos clave en agroproyectos con enfoque comercial: Una aproximación desde las concepciones epistemológicas sobre el problema rural agrario en Colombia. Región Científica, 1(1), 20224. https://doi.org/10.58763/rc20224
  25. Hoyos Chavarro, Y. A., Melo Zamudio, J. C., & Sánchez Castillo, V. (2022). Sistematización de la experiencia de circuito corto de comercialización estudio de caso Tibasosa, Boyacá. Región Científica, 1(1), 20228. https://doi.org/10.58763/rc20228
  26. Kelly, C., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17. https://doi.org/10.1186/s12916-019-1426-2
  27. Kimura, A., Antón-Oldenburg, M., & Pinderhughes, E. (2021). Developing and Teaching an Anti-Bias Curriculum in a Public Elementary School: Leadership, K-1 Teachers’, and Young Children’s Experiences. Journal of Research in Childhood Education, 36, 183 - 202. https://doi.org/10.1080/02568543.2021.1912222
  28. Kinavey, H., & Cool, C. (2019). The Broken Lens: How Anti-Fat Bias in Psychotherapy is Harming Our Clients and What To Do About It. Women & Therapy, 42, 116 - 130. https://doi.org/10.1080/02703149.2018.1524070
  29. Langenkamp, M., Costa, A., & Cheung, C. (2020). Hiring Fairly in the Age of Algorithms. ArXiv, abs/2004.07132. https://doi.org/10.2139/ssrn.3723046
  30. Ledesma, F. y Malave-González, B. E. (2022). Patrones de comunicación científica sobre E-commerce: un estudio bibliométrico en la base de datos Scopus. Región Científica, 1(1), 202214. https://doi.org/10.58763/rc202214
  31. Lin, L., & Chu, H. (2018). Quantifying publication bias in meta‐analysis. Biometrics, 74. https://doi.org/10.1111/biom.12817
  32. Lyu, Y., Lu, H., Lee, M., Schmitt, G., & Lim, B. (2022). IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality. IEEE Transactions on Visualization and Computer Graphics, 30, 3749-3766. https://doi.org/10.1109/TVCG.2023.3239909
  33. Madaio, M., Egede, L., Subramonyam, H., Vaughan, J., & Wallach, H. (2021). Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support. Proceedings of the ACM on Human-Computer Interaction, 6, 1 - 26. https://doi.org/10.1145/3512899
  34. Mazen, J., & Tong, X. (2020). Bias Correction for Replacement Samples in Longitudinal Research. Multivariate Behavioral Research, 56, 805 - 827. https://doi.org/10.1080/00273171.2020.1794774
  35. McGregor, L., Murray, D., & Ng, V. (2019). International human rights law as a framework for algorithmic accountability. International and Comparative Law Quarterly, 68, 309 - 343. https://doi.org/10.1017/S0020589319000046
  36. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2019). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys (CSUR), 54, 1 - 35. https://doi.org/10.1145/3457607
  37. Miroshnikov, A., Kotsiopoulos, K., Franks, R., & Kannan, A. (2020). Wasserstein-based fairness interpretability framework for machine learning models. Machine Learning, 111, 3307 - 3357. https://doi.org/10.1007/s10994-022-06213-9
  38. Mogrovejo Andrade, J. M. (2022). Estrategias resilientes y mecanismos de las organizaciones para mitigar los efectos ocasionados por la pandemia a nivel internacional. Región Científica, 1(1), 202211. https://doi.org/10.58763/rc202211
  39. Mökander, J., Juneja, P., Watson, D., & Floridi, L. (2022). The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?. Minds and Machines, 32, 751 - 758. https://doi.org/10.1007/s11023-022-09612-y
  40. Morgan, A., Chaiyachati, K., Weissman, G., & Liao, J. (2018). Eliminating Gender-Based Bias in Academic Medicine: More Than Naming the “Elephant in the Room”. Journal of General Internal Medicine, 33, 966-968. https://doi.org/10.1007/s11606-018-4411-0
  41. Ngxande, M., Tapamo, J., & Burke, M. (2019). Bias Remediation in Driver Drowsiness Detection Systems Using Generative Adversarial Networks. IEEE Access, 8, 55592-55601. https://doi.org/10.1109/ACCESS.2020.2981912
  42. Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., Kompatsiaris, I., Kinder-Kurlanda, K., Wagner, C., Karimi, F., Fernández, M., Alani, H., Berendt, B., Kruegel, T., Heinze, C., Broelemann, K., Kasneci, G., Tiropanis, T., & Staab, S. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10. https://doi.org/10.1002/widm.1356
  43. Orozco Castillo, E. A. (2022). Experiencias en torno al emprendimiento femenino. Región Científica, 1(1), 20227. https://doi.org/10.58763/rc20225
  44. Pérez Gamboa, A. J., García Acevedo, Y. y García Batán, J. (2019). Proyecto de vida y proceso formativo universitario: un estudio exploratorio en la Universidad de Camagüey. Trasnsformación, 15(3), 280-296. http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S2077-29552019000300280
  45. Pérez-Gamboa, A. J., Gómez-Cano, C., & Sánchez-Castillo, V. (2022). Decision making in university contexts based on knowledge management systems. Data & Metadata, 2, 92. https://doi.org/10.56294/dm202292
  46. Peters, U. (2022). Algorithmic Political Bias in Artificial Intelligence Systems. Philosophy & Technology, 35. https://doi.org/10.1007/s13347-022-00512-8
  47. Petersen, J., Ranker, L., Barnard-Mayers, R., Maclehose, R., & Fox, M. (2021). A systematic review of quantitative bias analysis applied to epidemiological research. International journal of epidemiology. https://doi.org/10.1093/ije/dyab061
  48. Petrenko, A. (2020). OECD acts as instruments of soft international law. Law Review of Kyiv University of Law. https://doi.org/10.36695/2219-5521.3.2020.74
  49. Pospisil, D., & Bair, W. (2022). Accounting for Bias in the Estimation of r2 between Two Sets of Noisy Neural Responses. The Journal of Neuroscience, 42, 9343 - 9355. https://doi.org/10.1523/JNEUROSCI.0198-22.2022
  50. Ricardo Jiménez, L. S. (2022). Dimensiones de emprendimiento: Relación educativa. El caso del programa cumbre. Región Científica, 1(1), 202210. https://doi.org/10.58763/rc202210
  51. Ringe, W., & Ruof, C. (2020). Regulating Fintech in the EU: the Case for a Guided Sandbox. European Journal of Risk Regulation, 11, 604 - 629. https://doi.org/10.1017/err.2020.8
  52. Rodríguez-Torres, E., Gómez-Cano, C., & Sánchez-Castillo, V. (2022). Management information systems and their impact on business decision making. Data & Metadata, 1, 21. https://doi.org/10.56294/dm202221
  53. Royal, K. (2019). Survey research methods: A guide for creating post-stratification weights to correct for sample bias. Education in the Health Professions, 2, 48 - 50. https://doi.org/10.4103/EHP.EHP_8_19
  54. Rus, C., Luppes, J., Oosterhuis, H., & Schoenmacker, G. (2022). Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation. ArXiv, abs/2209.09592. https://doi.org/10.48550/arXiv.2209.09592
  55. Sanabria Martínez, M. J. (2022). Construir nuevos espacios sostenibles respetando la diversidad cultural desde el nivel local. Región Científica, 1(1), 20222. https://doi.org/10.58763/rc20222
  56. Shen, A., Han, X., Cohn, T., Baldwin, T., & Frermann, L. (2022). Does Representational Fairness Imply Empirical Fairness? 81-95. https://doi.org/10.18653/v1/2022.findings-aacl.8
  57. Sherman, L., Cantor, A., Milman, A., & Kiparsky, M. (2020). Examining the complex relationship between innovation and regulation through a survey of wastewater utility managers. Journal of environmental management, 260, 110025. https://doi.org/10.1016/j.jenvman.2019.110025
  58. Simpson, A., & Dervin, F. (2019). Global and intercultural competences for whom? By whom? For what purpose?: an example from the Asia Society and the OECD. Compare: A Journal of Comparative and International Education, 49, 672 - 677. https://doi.org/10.1080/03057925.2019.1586194
  59. Thompson, J. (2021). Mental Models and Interpretability in AI Fairness Tools and Code Environments. In: Stephanidis, C., et al. HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence. HCII 2021. Lecture Notes in Computer Science, vol 13095. Springer, Cham. https://doi.org/10.1007/978-3-030-90963-5_43
  60. Vaccari, V., & Gardinier, M. (2019). Toward one world or many? A comparative analysis of OECD and UNESCO global education policy documents. International Journal of Development Education and Global Learning. https://doi.org/10.18546/IJDEGL.11.1.05
  61. Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viégas, F., & Wilson, J. (2019). The What-If Tool: Interactive Probing of Machine Learning Models. IEEE Transactions on Visualization and Computer Graphics, 26, 56-65. https://doi.org/10.1109/TVCG.2019.2934619
  62. Yam, J., & Skorburg, J. (2021). From human resources to human rights: Impact assessments for hiring algorithms. Ethics and Information Technology, 23, 611 - 623. https://doi.org/10.1007/s10676-021-09599-7
  63. Yarborough, M. (2021). Moving towards less biased research. BMJ Open Science, 5. https://doi.org/10.1136/bmjos-2020-100116
  64. Zahid, A., Khan, M., Khan, A., Kamiran, F., & Nasir, B. (2020). Modeling, Quantifying and Visualizing Media Bias on Twitter. IEEE Access, 8, 81812-81821. https://doi.org/10.1109/ACCESS.2020.2990800
  65. Zapp, M. (2020). The authority of science and the legitimacy of international organisations: OECD, UNESCO and World Bank in global education governance. Compare: A Journal of Comparative and International Education, 51, 1022 - 1041. https://doi.org/10.1080/03057925.2019.1702503
Tutorials

Autors:

How send article?

Evaluator pair

Most read in the last 30 days

Information

QR code

Keywords

Sistema OJS 3.4.0.9 - Metabiblioteca |