Building Responsible AI Systems: Ensuring Fairness, Accountability, and Transparency

• Riabokon, Viktoriia ^[1]
  1. [1] Independent Researcher
• Localización: Estudios en derecho a la información, ISSN-e 2594-0082, ISSN 2683-2038, Nº. 22, 2026
• Idioma: inglés
• DOI: 10.22201/iij.25940082e.2026.22.20780
• Títulos paralelos:
  • Creación de sistemas de IA responsables: garantizar la equidad, la rendición de cuentas y la transparencia
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Este artículo analiza la implementación de una IA responsable a través de la aplicación jurídica y normativa de los principios de equidad, rendición de cuentas y transparencia. Mediante un análisis de diversas aplicaciones de IA y de los casos judiciales relacionados con ellas, el artículo examina cómo la Ley de Inteligencia Artificial de la UE y el RGPD se aplican a las aplicaciones de IA en ámbitos como los recursos humanos, las finanzas, los seguros y la administración pública. El artículo investiga en qué aspectos las implementaciones actuales de IA en estos ámbitos no cumplen con dicha normativa. Además, el artículo repasa algunas de las herramientas que se han creado para aumentar la explicabilidad de las decisiones de la IA, como SHAP, LIME y AI Fairness 360, y cómo dichas herramientas pueden ayudar a cumplir los requisitos del RGPD y de la Ley de IA. Por último, el artículo concluye que, si bien la IA responsable se considera a menudo una cuestión ética, se está convirtiendo en un ámbito de la regulación digital que se está aplicando y que las organizaciones deben cumplir.

  • English

    This article examines the implementation of responsible AI through the legal and regulatory implementation of fairness, accountability, and transparency. Through a review of various AI applications and the legal cases that relate to those AI applications, the paper examines how the EU’s Artificial Intelligence Act and GDPR relate to AI applications in areas such as human resources, finance, insurance, and public administration. The article investigates in what ways these regulations are currently not being met by the current AI implementations within these areas. Furthermore, the article reviews some of the tools that have been created to increase explainability of AI decisions, such as SHAP, LIME, and AI Fairness 360, and how those tools may assist in meeting the requirements of the GDPR and AI Act. Finally, the article determines that while responsible AI is often considered to be an ethical consideration, it is becoming a field of digital regulation that is both being enforced and that organizations must comply with.

• Referencias bibliográficas
  • Abbu, H., Mugge, P., & Gudergan, G. (2022, June). Ethical considerations of artificial intelligence: Ensuring fairness, transparency,...
  • Agbadamasi, T. O., Opoku, L. K., Adukpo, T. K., & Mensah, N. (2025). The role of business intelligence in AI ethics: Empowering US companies...
  • Agu, E. E., Abhulimen, A. O., Obiki-Osafiele, A. N., Osundare, O. S., Adeniran, I. A., & Efunniyi, C. P. (2024). Discussing ethical considerations...
  • AI Watch – European Commission monitoring platform (2025). European Commission monitoring platform. https://ai-watch.ec.europa.eu/index_en
  • Alam, A. (2023). Developing a curriculum for ethical and responsible AI: A university course on safety, fairness, privacy, and ethics to prepare...
  • Asif, R., Hassan, S. R., & Parr, G. (2023). Integrating a blockchain-based governance framework for responsible AI. Future Internet, 15(3),...
  • Baker, S., & Xiang, W. (2023). Explainable AI is responsible AI: How explainability creates trustworthy and socially responsible artificial...
  • Balasubramaniam, N., Kauppinen, M., Rannisto, A., Hiekkanen, K., & Kujala, S. (2023). Transparency and explainability of AI systems: From...
  • Bogina, V., Hartman, A., Kuflik, T. et al. (2022). Educating software and AI stakeholders about algorithmic fairness, accountability, transparency...
  • Campos Zabala, F.J. (2023). Responsible AI understanding the ethical and regulatory implications of AI. In: Grow Your Business with AI. Apress,...
  • Cheong, B. C. (2024). Transparency and accountability in AI systems: Safeguarding wellbeing in the age of algorithmic decision-making. Frontiers...
  • Deshpande, A., & Sharp, H. (2022, July). Responsible AI systems: Who are the stakeholders?. In Proceedings of the 2022 AAAI/ACM Conference...
  • Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., de Prado, M. L., Herrera-Viedma, E., & Herrera, F. (2023). Connecting the dots in trustworthy...
  • Ethics Guidelines for Trustworthy AI (2019). https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
  • EU AI Act Compliance Checker (independent initiative) (2024).
  • https://artificialintelligenceact.eu/assessment/eu-ai-act-compliance-checker
  • European Data Protection Board (EDPB) (2023, March). Guidelines 01/2022 on Data Subject Rights – Right of Access. Version 2.0.
  • https://edpb.europa.eu/our-work-tools/our-documents/guidelines/guidelines-012022-data-subject-rights-right-access_en
  • Faheem, M. A. (2024). Ethical AI: Addressing bias, fairness, and accountability in autonomous decision-making systems. World Journal of Advanced...
  • GitHub – SHAP: SHapley Additive Explanations (n.d.). https://github.com/slundberg/shap
  • GitHub – LIME: Local Interpretable Model-Agnostic Explanations (n.d.). https://github.com/marcotcr/lime
  • IBM AI Fairness 360 Toolkit (n.d.). https://ai-fairness-360.org/
  • Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2022). Trustworthy artificial intelligence: A review. ACM Computing Surveys (CSUR),...
  • Memarian, B., & Doleck, T. (2023). Fairness, accountability, transparency, and ethics (FATE) in Artificial Intelligence (AI) and higher...
  • Mensah, G. B. (2023). Artificial intelligence and ethics: A comprehensive review of bias mitigation, transparency, and accountability in AI...
  • Modi, T. B. (2023). Artificial Intelligence Ethics and Fairness: A study to address bias and fairness issues in AI systems, and the ethical...
  • OECD Principles on Artificial Intelligence (2019). https://oecd.ai/en/ai-principles
  • OECD AI Observatory – Publications & Indicators (n.d.).
  • https://oecd.ai/en/ai-publications
  • OECD Artificial Intelligence (n.d.). https://www.oecd.org/en/topics/artificial-intelligence.html
  • OECD Work, Innovation, Productivity and Skills – AI Programme (2024).
  • https://oecd.ai/en/work-innovation-productivity-skills
  • Pujari, T., Goel, A., & Sharma, A. (2024). Ethical and responsible AI: Governance frameworks and policy implications for multi-agent systems....
  • Pujari, T., Goel, A., & Sharma, A. (2024). Ensuring responsible AI: The role of supervised fine-tuning (SFT) in upholding integrity and...
  • Rahaman, S. U. (2024) Ethical AI in data science: Balancing innovation and responsibility in the digital age. IJLRP-International Journal...
  • Raza, S., Qureshi, R., Zahid, A., Fioresi, J., Sadak, F., Saeed, M., ... & Shoman, M. (2025). Who is responsible? The data, models, users...
  • Regulation (EU) 2024/1689 – Artificial Intelligence Act (AI Act). https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
  • Sadek, M., Constantinides, M., Quercia, D., & Mougenot, C. (2024, May). Guidelines for integrating value sensitive design in responsible...
  • Sanderson, C., Lu, Q., Douglas, D., Xu, X., Zhu, L., & Whittle, J. (2022, December). Towards implementing responsible AI. In 2022 IEEE...
  • Sargiotis, D. (2024). Ethical AI in information technology: Navigating bias, privacy, transparency, and accountability. Advantages in Machine...
  • Serhieiev, V., Voronina, Y., Zolotov, A., Akimova, L., Rovynska, K., & Akimov, O. (2025). Innovative competences within public administration...
  • Sydorchuk, O., Kharechko, D., Khomenko, H., Akimova, L., Kosarevych, N., & Akimov, O. (2024). Competencies for sustainable financial and...
  • Thalpage, N. (2023). Unlocking the black box: Explainable artificial intelligence (XAI) for trust and transparency in ai systems. Journal...
  • Todupunuri, A. (2024). Artificial intelligence ethics: Investigating ethical frameworks, bias mitigation, and transparency in AI systems to...
  • Visave, J. (2025). Transparency in AI for emergency management: Building trust and accountability. AI Ethics, 5, 3967-3980. https://doi.org/10.1007/s43681-025-00692-x
  • Vourganas, I., Attar, H., Michala, A.L. (2022). Accountable, responsible, transparent artificial intelligence in ambient intelligence systems...
  • Werder, K., Ramesh, B., & Zhang, R. (2022). Establishing data provenance for responsible artificial intelligence systems. ACM Transactions...
  • Williams, R., Cloete, R., Cobbe, J., Cottrill, C., Edwards, P., Markovic, M., ... & Pang, W. (2022). From transparency to accountability...
  • Yanamala, K. K. R. (2023). Transparency, privacy, and accountability in AI-enhanced HR processes. Journal of Advanced Computing Systems, 3(3),...