Os Algoritmos têm Direitos?
Resumo
A crescente influência dos algoritmos na sociedade digital levanta questões sobre o seu estatuto jurídico e ético. À medida que estes algoritmos tomam decisões que afetam as pessoas, surge a questão de saber se estas devem ter direitos e como esses direitos podem ser protegidos num ambiente tecnológico em constante evolução. A pesquisa realizou uma análise documental exaustiva de literatura, relatórios e documentos legais relevantes sobre a ética dos algoritmos, bem como pesquisas sobre seus impactos sociais em áreas como privacidade, discriminação e tomada de decisão automatizada. As conclusões revelaram que atualmente não existe um consenso claro sobre se os algoritmos devem ter direitos. No entanto, reconhece-se a necessidade de estabelecer regulamentos e princípios éticos para garantir o seu uso responsável e evitar consequências negativas. Os principais desafios, como a transparência algorítmica, a discriminação e a privacidade dos dados, foram identificados como exigindo atenção adequada. A regulamentação e a transparência são essenciais para garantir que os algoritmos sejam utilizados de forma justa e equitativa, de uma forma que proteja os direitos individuais e sociais.
Palavras-chave
direitos, algoritmos, privacidade, transparência, autonomia
Referências
- Balkin, J. M. (2016). The Three Laws of Robotics in the Age of Big Data. Washington Law Review, 91(4), 1005-1051.
- Barocas, S. & Selbst, A. D. (2016). Fairness in Machine Learning: Lessons from Political Philosophy. arXiv preprint, 1609.07236.
- BBC. (2018). 5 claves para entender el escándalo de Cambridge Analytica que hizo que Facebook perdiera US$37.000 millones en un día. https://www.bbc.com/mundo/noticias-43472797
- Benthall, S. (2019). The Moral Economy of Algorithms. In Dubber, Markus D., Frank Pasquale, & Sunit Das (eds.), The Oxford Handbook of Ethics of AI (pp. 91-112). Oxford University Press.
- Berkeley News. (2020). Algorithmic Bias: UC Berkeley School of Information Study Finds Discrimination in Online Ad Delivery. https://news.berkeley.edu/2020/02/28/algorithmic-bias-uc-berkeley-school-of-information-study-finds-discrimination-in-online-ad-delivery/.
- Bostrom, N. & Yudkowsky, E. (2014). The ethics of artificial intelligence. In Keith Frankish & William Ramsey (eds.), Cambridge handbook of artificial intelligence (pp. 316-334). Cambridge University Press. DOI: https://doi.org/10.1017/CBO9781139046855.020
- Boyd, D. (2017). The ethics of big data: Confronting the challenges of an algorithmic society. Data & Society Research Institute.
- Boyd, D. & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679. DOI: https://doi.org/10.1080/1369118X.2012.678878
- Boyd, D., Crawford, K., Keller, E., Gangadharan, S. P. & Eubanks, V. (2019). AI in the public interest: Seven principles for ethical AI in society. AI & Society, 34(1), 1-14.
- Bracha, O. (2012). Owning ideas: A history of Anglo-American intellectual property. MIT Press.
- Brundage, M. et al. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention and Mitigation. Future for Humanity Institute, Oxford University, Centre for the Study of Existential Risk, University of Cambridge, Centre for a New American Security, Electronic Frontier Foundation, Open AI. arXiv preprint, 1802.07228.
- Bryson, J. J. (2018). Robots should be slaves. In The ethics of artificial intelligence (pp. 123-138). MIT Press.
- Bryson, J. J. & Winfield, A. F. (2018). Standardizing ethical design for artificial intelligence and autonomous systems. Computer, 51(5), 116-119. DOI: https://doi.org/10.1109/MC.2017.154
- Burrell, J. (2016). How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithm. Big Data & Society, 3(1). https://doi.org/10.2139/ssrn.2660674. DOI: https://doi.org/10.1177/2053951715622512
- Calo, R. (2013a). Digital Market Manipulation. George Washington Law Review, 81(3), 725-772. DOI: https://doi.org/10.2139/ssrn.2309703
- Calo, R. (2013b). The drone as privacy catalyst. Stanford Law Review, 64(2), 29-71.
- Calo, R. (2017c). Artificial intelligence policy: A primer and roadmap. Policy Research Working Paper, World Bank Group. DOI: https://doi.org/10.2139/ssrn.3015350
- Chun, W. H. K. (2011). Programmed visions: Software and memory. MIT Press. DOI: https://doi.org/10.7551/mitpress/9780262015424.001.0001
- Citron, D. K. (2014). Hate crimes in cyberspace. Harvard University Press. DOI: https://doi.org/10.4159/harvard.9780674735613
- Cormen, T. H., Leiserson, C. E., Rivest, R. L. & Stein, C. (2009). Introduction to Algorithms. MIT Press.
- Crawford, K. (2013). The Hidden Biases in Big Data. Harvard Business Review. https://hbr.org/2013/04/the-hidden-biases-in-big-data.
- Crawford, K. (2016). Can an algorithm be agonistic? Ten scenes from life in calculated publics. Science, Technology & Human Values, 41(1), 77-92. https://doi.org/10.1177/0162243915608947 DOI: https://doi.org/10.1177/0162243915589635
- Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. DOI: https://doi.org/10.12987/9780300252392
- Crawford, K. & Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
- Crawford, K. et al. (2019). AI Now 2019 Report. AI Now Institute. https://ainowinstitute.org/AI_Now_2019_Report.pdf
- Dasgupta, S., Papadimitriou, C. H. & Vazirani, U. V. (2006). Algorithms. McGraw-Hill Education.
- Data & Society Research Institute. (2018). Principles for Accountable Algorithms and a Social Impact Statement for Algorithms.
- Diakopoulos, N. (2015). Algorithmic accountability: Journalistic investigation of computational power structures. Digital Journalism, 3(3), 398-415. DOI: https://doi.org/10.1080/21670811.2014.976411
- Diakopoulos, N. (2019). Algorithmic Accountability Reporting: On the Investigation of Black Boxes. In The Oxford Handbook of Journalism and AI (pp. 141-162). Oxford University Press.
- Dignum, V. (2017). Ethics in the design and use of artificial intelligence. Springer.
- Dignum, V. (2020). Responsible artificial intelligence: How to develop and use AI in a responsible way. Springer. DOI: https://doi.org/10.1007/978-3-030-30371-6
- Doctorow, C. (2019). Radicalized. Tor Books.
- Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press.
- Finn, E. & Selwyn, N. (2017). The Ethics of Algorithms: Mapping the Debate. International Journal of Communication, 11, 2787-2805.
- Floridi, L. (2013). The Ethics of Information. Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780199641321.001.0001
- Floridi, L. (2014). The fourth revolution: How the infosphere is reshaping human reality. Oxford University Press.
- Floridi, L. & Taddeo, M. (2016). What is data ethics? Philosophical transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), e20160360. https://doi.org/10.1098/rsta.2016.0360 DOI: https://doi.org/10.1098/rsta.2016.0360
- Floridi, L. (2018). Artificial Intelligence's Fourth Revolution. Philosophy & Technology, 31(2), 317-321. DOI: https://doi.org/10.1007/s13347-018-0325-3
- Floridi, L. (2019a). Soft Ethics and the Governance of the Digital. Philosophy & Technology, 32(2), 185-187. DOI: https://doi.org/10.1007/s13347-019-00354-x
- Floridi, L. (2019b). The logic of digital beings: on the ontology of algorithms, bots, and chatbots. Philosophy & Technology, 32(2), 209-227.
- Floridi, L. & Sanders, J. W. (2004). On the morality of artificial agents. Minds and Machines, 14(3), 349-379. DOI: https://doi.org/10.1023/B:MIND.0000035461.63578.9d
- Hart, H. L. A. (2012). The concept of law. Oxford University Press. DOI: https://doi.org/10.1093/he/9780199644704.001.0001
- Hartzog, W. (2012). Privacy’s outsourced dilemma: analyzing the effectiveness of current approaches to regulating, “Notice and Choice”. Loyola of Los Angeles Law Review, 46(2), 413-468.
- Hartzog, W. (2016). The case for a duty of loyalty in privacy law. North Carolina Law Review, 94(4), 1151-1203.
- Hildebrandt, M. (2013). Smart technologies and the end(s) of law: Novel entanglements of law and technology. Edward Elgar Publishing.
- Jobin, A., Ienca, M. & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. DOI: https://doi.org/10.1038/s42256-019-0088-2
- Kaye, D. (2018). Report of the Special Rapporteur on the promotion and protection of the right to freedom of opinion and expression. United Nations General Assembly, A/73/348.
- Kerr, O. S. (2019). The fourth amendment in the digital age. Harvard Law Review, 127(6), 1672-1756.
- Kleinberg, J. & Tardos, E. (2005). Algorithm Design. Pearson Education.
- Knuth, D. E. (1997). The Art of Computer Programming. Addison-Wesley.
- Mittelstadt, B. (2019). AI ethics, oversight and accountability: A mapping report. Big Data & Society, 6(1), 2053951718823039.
- Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S. & Floridi, L. (2016). The Ethics of Algorithms: Mapping the Debate. Big Data & Society, 3(2), 2053951716679679. DOI: https://doi.org/10.1177/2053951716679679
- Mittelstadt, B.D., Russell, C. & Wachter, S. (2019). Exploring the Impact of Artificial Intelligence: Transparency, Fairness, and Ethics. AI & Society, 34(4), 787-793.
- Moor, J. H. (2007). Why we need better ethics for emerging technologies. Ethics and Information Technology, 9(2), 111-119. DOI: https://doi.org/10.1007/s10676-006-0008-0
- O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
- Pasquale, F. (2015). The Black Box Society: The Secret Algorithms that Control Money and Information. Harvard University Press. DOI: https://doi.org/10.4159/harvard.9780674736061
- Powles, J. & Nissenbaum, H. (2017). The seductive diversion of ‘solving’ bias in AI. Harvard Law Review, 131(6), 1641-1677.
- ProPublica. (2016). Machine Bias: There’s Software Used across the Country to Predict Future Criminals. And It’s Biased Against Blacks. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
- Radbruch, G. (1946). Cinco minutos de filosofía del derecho. https://www.infojus.gob.ar/sites/default/files/5minutosderechoradbruch.pdf
- Roberts, S. T. (2019). Behind the screen: Content moderation in the shadows of social media. Yale University Press. DOI: https://doi.org/10.12987/9780300245318
- Sedgewick, R. & Wayne, K. (2011). Algorithms (Fourth Edition). Addison-Wesley Professional.
- Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S. & Vertesi, J. (2019). Fairness and Abstraction in Sociotechnical Systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*), 59-68. DOI: https://doi.org/10.1145/3287560.3287598
- Sweeney, L. (2013). Discrimination in online ad delivery. Communications of the ACM, 56(5), 44-54. DOI: https://doi.org/10.1145/2447976.2447990
- Taddeo, M., & Floridi, L. (2018b). Regulate Artificial Intelligence to avert cyber arms race. Nature, 556(7701), 296-298. DOI: https://doi.org/10.1038/d41586-018-04602-6
- Tufekci, Z. (2014). Engineering the public: Big data, surveillance and computational politics. First Monday, 19(7). DOI: https://doi.org/10.5210/fm.v19i7.4901
- Turing, A. M. (1937). On computable numbers, with an application to the Entscheidung’s problem. https://www.cs.virginia.edu/~robins/TuringPaper1936.pdf
- United Nations Human Rights Committee (2019). General Comment No. 37 on the right of peaceful assembly under the International Covenant on Civil and Political Rights. United Nations General Assembly, CCPR/C/GC/37.
- Vallor, S. (2021). Technology and the virtues: A philosophical guide to a world worth wanting. Oxford University Press. DOI: https://doi.org/10.1201/9781003278290-12
- Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197-208. DOI: https://doi.org/10.24908/ss.v12i2.4776
- Wachter, S., Mittelstadt, B. & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the general data protection regulation. International Data Privacy Law, 7(2), 76-99. https://doi.org/10.1093/idpl/ipx017 DOI: https://doi.org/10.1093/idpl/ipx005
- Wallach, W. & Allen, C. (2019). Moral machines 2.0: Teaching robots right from wrong. Oxford University Press.
- Zarsky, T. (2016). The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision-making. Science, Technology, & Human Values, 41(1), 118-132. DOI: https://doi.org/10.1177/0162243915605575
- Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.