Algorithmic discrimination in the employment relationship
economic efficiency, artificial intelligence and employee fragility
DOI:
https://doi.org/10.70405/rtst.v90i2.67Keywords:
Artifcial intelligence, Economic efficiency, Algorithmic discriminationAbstract
The research seeks to verify which peculiarities of the employment relationship would make it more susceptible or fragile to the occurrence of algorithmic discrimination. As a result of the research, it was found that the use of AI depends on the volume, speed and value of the data provided by Big Data, generating results that are as good as the data. Furthermore, it was understood that, despite seeking objectivity, algorithms can cause discrimination through a programming error, generalization, use of sensitive information and limitation of rights. Finally, it was considered that employment relationships have peculiarities that make the employee more susceptible to algorithmic discrimination, for example inequality between the parties; the fragility of the employee, faced with a legitimate power of control; and technological nudity, given the amount of data, including unnecessary ones, held by the employer. As a methodology, descriptive, exploratory and interpretative research was used, of a qualitative nature, through documentary and bibliographical analysis, using the hypothetical-deductive method.
References
ALOISI, Antonio; GRAMANO, Elena. Artificial intelligence is watching you at work. Digital surveillance, employee monitoring and regulatory issues in the EU context. Special Issue of Comparative Labor Law & Policy Journal, “Automation, Artificial Intelligence and Labour Protection”, 2019, p. 105-106. Disponível em: http://salus.adapt.it/wp-content/uploads/2020/07/Gramano-Alois_AI-is-Wathching-you_2019.pdf. Acesso em: 3 set. 2021.
BELMONTE, Alexandre Agra. O monitoramento da correspondência eletrônica nas relações de trabalho. LTr, 2004.
BODIE, Matthew T. et al. The law and policy of people analytics. University of Colorado Law Review, v. 88, 2016, p. 3. Disponível em: https://scholarship.law.slu.edu/cgi/viewcontent.cgi?article=1001&context=faculty. Acesso em: 3 set. 2021.
DELGADO, Mauricio Godinho. O poder empregatício. São Paulo: LTr, 1996.
DOMINGOS, Pedro. O algoritmo mestre: como a busca pelo algoritmo de machine learning definitivo recriará nosso mundo. São Paulo: Novatec Editora, 2017.
DONEDA, Danilo Cesar Maganhoto et al. Considerações iniciais sobre inteligência artificial, ética e autonomia pessoal. Pensar-Revista de Ciências Jurídicas, v. 23, n. 4, p. 1-17, 2018. DOI: https://doi.org/10.5020/2317-2150.2018.8257
ERNST, Ekkehard et al. The economics of artificial intelligence: Implications for the future of work, ILO Future of Work Research paper Series; ILO, 2018.
GARRIDO, Giovanna; SILVEIRA, Rafael Damasco; SILVEIRA, Marco Antonio. People Analytics: uma abordagem estratégica para a gestão do capital humano. Revista Eletrônica de Estratégia & Negócios, v. 11, n. 1, p. 28-52, 2018. DOI: https://doi.org/10.19177/reen.v11e01201828-52
KAPLAN, Andreas; HAENLEIN, Michael. Siri, Siri, in my hand: who's the fairest in the land? On interpretations, illustrations, and implications of artificial intelligence. Business Horizons, [s.I.], v. 62, n. 1, p. 15-25, jan. 2018. Elsevier BV. Disponível em: http://dx.doi.org/10.1016/j.bushor.2018.08.004. Acesso em: 2 set. 2021. DOI: https://doi.org/10.1016/j.bushor.2018.08.004
KROLL, Joshua Alexander. et al. Accountable algorithms. University of Pennsylvania Law Review, v. 165, p. 633-705, 2017.
MAYER-SCHÖNBERGER, V.; CUKIER, K. Big Data: A Revolution That Will Transform How We Live, Work, and Think. New York: First Mariner Books, 2014.
MENDES, Laura Schertel; MATTIUZZO, Marcela. Discriminação Algorítmica: Conceito, Fundamento Legal e Tipologia. Direito Público, v. 16, n. 90, 2019.
MOREIRA, Teresa Coelho. A privacidade dos trabalhadores e as novas tecnologias de informação e comunicação: contributo para um estudo dos limites do poder de controlo electrónico do empregador. Coimbra: Almedina, 2010.
O’NEIL, Cathy. Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Crown, 2016.
PASQUALE, Frank. The black box society. Cambridge: Harvard University Press, 2015. DOI: https://doi.org/10.4159/harvard.9780674736061
SMITH, Adam. A riqueza das nações. 3. ed. São Paulo: Editora WMF Martins Fontes, 2016. V. I.
TURING, Alan M.; HAUGELAND, J. Computing machinery and intelligence. Cambridge, MA: MIT Press, 1950. DOI: https://doi.org/10.1093/mind/LIX.236.433
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