Creating non-discriminatory Artificial Intelligence systems: balancing the tensions between code granularity and the general nature of legal rules

• Alba Soriano Arnanz ^[1]
  1. [1] Universitat de València

     Universitat de València

     Valencia, España

     
• Localización: IDP: revista de Internet, derecho y política = revista d'Internet, dret i política, ISSN-e 1699-8154, Nº. 38, 2023
• Idioma: español
• DOI: 10.7238/idp.v0i38.403794
• Enlaces
  • Texto completo (pdf)
• Dialnet Métricas: 3 Citas
• Resumen

  • Over the past decade, concern has grown regarding the risks generated by the use of artificial intelligence systems. One of the main problems associated with the use of these systems is the harm they have been proven to cause to the fundamental right to equality and non-discrimination. In this context, it is vital that we examine existing and proposed regulatory instruments that aim to address this particular issue, especially taking into consideration the difficulties of applying the abstract nature that typically characterises legal instruments and, in particular, the equality and non-discrimination legal framework, to the specific instructions that are needed when coding an artificial intelligence instrument that aims to be non-discriminatory. This paper focuses on examining how article 10 of the new EU Artificial Intelligence Act proposal may be the starting point for a new form of regulation that adapts to the needs of algorithmic systems.

• Referencias bibliográficas
  • BAROCAS, S.; SELBST, A. D. (2016). “Big data’s disparate impact”. California Law Review, vol. 104, no. 3, pp. 671-732. DOI: https://doi.org/10.2139/ssrn.2477899
  • BAROCAS, S.; SELBST, A. D. (2018). “The intuitive appeal of explainable machines”. Fordham Law Review, vol. 87, no. 3, pp. 1085-1139. DOI:...
  • BENT, J. R. (2020). “Is algorithmic affirmative action legal?”. The Georgetown Law Journal, vol. 108, pp. 803-853.
  • BERK, R.; HEIDARI, H.; JABBARI, S.; KEARNS, M.; ROTH, A. (2018). “Fairness in criminal justice risk assessments: the state of the art”. Sociological...
  • CERRILLO I MARTÍNEZ, A. (2020). “El impacto de la inteligencia artificial en el derecho administrativo ¿nuevos conceptos para nuevas realidades...
  • CHOULDECHOVA, A. (2016). “Fair prediction with disparate impact: a study of bias in recidivism prediction instruments”. arXiv [online]. [Accessed:...
  • CORBETT-DAVIES, S.; PIERSON, E.; GOEL, S. (2015, October). “A computer program used for bail and sentencing decisions was labeled biased against...
  • CORBETT-DAVIES, S.; GOEL, S. (2018). “The measure and mismeasure of fairness: a critical review of fair machine learning”. ArXiv [online]....
  • FRIEDLER, S. A.; SCHEIDEGGER, C. E.; VENKATASUBRAMANIAN, S.; CHOUDHARY, S.; HAMILTON, E. P.; ROTH, D. (2018). “A Comparative Study of Fairness-Enhancing...
  • GERARDS, J.; XENIDIS, R. (2021). Algorithmic discrimination in Europe: Challenges and opportunities for gender equality and non-discrimination...
  • HACKER, P. (2018). “Teaching fairness to artificial intelligence: existing and novel strategies against algorithmic discrimination under EU...
  • HUERGO LORA, A. (2020). “Una aproximación a los algoritmos desde el Derecho administrativo”. In: HUERGO LORA. A. (dir.) and Díaz González,...
  • HUNT, B. (2005). “Redlining”. Encyclopedia of Chicago, 2005 [online]. [Accessed: 6 September 2022]. Available at: http://www.encyclopedia.chicagohistory.org/
  • KIM, P. T. (2017). “Data-driven discrimination at work”. William & Mary Law Review, vol. 58, pp. 857-936.
  • LESSIG, L. (2006). Code: Version 2.0. New York: Basic books.
  • MAKKONEN, T. (2007). Measuring discrimination: data collection and EU equality law. Luxembourg: Office for Official Publications of the European...
  • PLEISS, G.; RAGHAVAN, M.; WU, F.; KLEINBERG, J.; WEINBERGER, K. Q. (2017). “On Fairness and Calibration”. Advances in Neural Information Processing...
  • RENAN BARZILAY, A.; BEN-DAVID, A. (2017). “Platform inequality: gender in the gig economy”. Seton Hall Law Review, vol. 47, no. 393, pp. 393-431....
  • SORIANO ARNANZ, A. (2020). Posibilidades actuales y futuras para la regulación de la discriminación producida por algoritmos. Doctoral tesis...
  • SORIANO ARNANZ, A. (2021a). “La propuesta de Reglamento de Inteligencia Artificial de la Unión Europea y los sistemas de alto riesgo”. Revista...
  • SORIANO ARNANZ, A. (2021b). “La situación de las mujeres en el empleo público: análisis y propuestas”. IgualdadES, no. 4, pp. 87-121. DOI:...
  • SORIANO ARNANZ, A. (2021c). “Decisiones automatizadas: problemas y soluciones jurídicas. Más allá de la protección de datos”. Revista de Derecho...
  • US Equal Employment Opportunity Commission. (1979). “Questions and Answers to Clarify and Provide a Common Interpretation of the Uniform Guidelines...
  • VALERO TORRIJOS, J. (2020). “The legal guarantees of artificial intelligence in administrative activity: reflections and contributions from...
  • WACHTER, S. (2020). “Affinity Profiling and Discrimination by Association in Online Behavioural Advertising”. Berkeley Technology Law Journal,...
  • WACHTER, S.; MITTELSTADT, B.; RUSSELL, C. (2021). “Why fairnesss cannot be automated: bridging the gap between EU non-discrimination law and...
  • ŽLIOBAITĖ, I. (2015). “A survey on measuring indirect discrimination in machine learning”. arXiv [online]. [Accessed: 6 September 2022]. DOI:...
  • ŽLIOBAITĖ, I.; CUSTERS, B. (2016). “Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models”....