Trabalharemos questões de ética e privacidade envolvendo data science, big data, data analytics e afins neste exercício. Existem diversos textos na internet tanto de cunho jornalístico quanto acadêmico (ou às vezes uma mistura dos dois) que abordam problemas de ética e privacidade no contexto de data science. Eu filtrei alguns trabalhos que julguei serem mais relevantes/interessantes para nossa aula.
O exercício consistirá em cada aluno fazer um resumo e apresentação dos textos selecionados para si conforme detalho a seguir. A entrega desse exercício também será via GitHub. Cada aluno deverá subir seu resumo e apresentação, ambos em formato original e pdf, ao seu repositório. O repositório deve ser público, tendo todos os alunos acesso ao conteúdo. No dia da apresentação, o aluno deverá indicar o link do repositório.
O resumo deve descrever os textos trabalhados. Ele deve permitir que os demais alunos compreendam o contexto do(s) artigo(s) lidos, quais foram os métodos ou metodologia usada, quais conclusões e, também, uma análise crítica do conteúdo. O resumo deve ser feito em Times, 12pt, com espaçamento 1,5. O título deve ser ilustrativo do conteúdo lido. O resumo deve ter entre 1500 e 4000 caracteres (+/- de uma a duas páginas). As margens devem ser
- superior: 2,5 cm
- inferior: 1,5 cm
- direita: 1,5 cm
- esquerda: 2,5 cm
A apresentação será de 10min seguida de 5min de discussões. As apresentações serão feitas durante o horário das aulas nos dias 29 de maio, 5 e 7 de junho. O cronograma é apresentado mais abaixo. Todos devem chegar pontualmente no horário da aula para carregarem suas apresentações no computador local no início. Os atrasos serão penalizados com a perda 0,5 ponto pelos primeiros 10min e mais 0,05 por cada minuto adicional. Haverá uma tolerância inicial de 10min. As ausências em qualquer apresentação serão penalizadas com a perda integral dos pontos do exercício. Essa medida está sendo tomada para valorizar o trabalho dos colegas. Como todos irão se esforçar para fazerem seus resumos e apresentações, é importante que os demais estejam presentes para que seus esforços valham. Será admitida ausência justificada, devendo essa ser aprovada com antecedência pelo professor.
Esse exercício vale 2 pontos.
Encontra-se abaixo a lista dos alunos com seus respectivos identificadores de artigos. Os números listados após os nomes correspondem a um grupo de textos elencados mais abaixo. Os textos foram alocados aleatoriamente usando um gerador de números aleatórios. A quantidade de textos foi feita de forma a balancear a carga de leitura de cada aluno. Alguns artigos são mais densos e maiores, logo, apenas um texto foi alocado. Em outros casos, os textos são muito simples e, assim, vários foram alocados. Notem que os textos mais simples podem, na verdade, ser mais difíceis de serem apresentados e resumidos. O aluno deverá se esforçar ainda mais para que seu resumo tenha unidade.
- Alberto Santos de Souza 15
- Allyson Manoel Nascimento Venceslau 11
- Andreza Fabiola Vieira de Abreu 12
- Antonio Carlos Portela Rodrigues 7
- arthur 1
- Bruno César Gomes Sampaio 22
- Eduardo Luiz Silva 18
- Felipe Bormann 3
- Filipe Cordeiro de Medeiros Azevêdo 16
- Karla Falcão 8
- Leonardo Alves dos Santos 6
- Lucas Florencio 17
- Luiz Felipe Véras Gonçalves 9
- Marcos da Silva Barreto 13
- Milton Vasconcelos da Gama Neto 5
- Pedro Henrique Sousa de Moraes 18
- Pedro Vitor Baptista de Moura 19
- Renan Freitas 20
- Rielson Leandro 4
- Thiago Aquino dos Santos 10
- Thiago Augusto dos Santos Martins 2
- vinícius emanuel miranda silva 14
- Wellington Barbosa de Almeida 23
Computer science faces an ethics crisis. The Cambridge Analytica scandal proves it. Yonatan Zunger. The Boston Globe. https://www.bostonglobe.com/ideas/2018/03/22/computer-science-faces-ethics-crisis-the-cambridge-analytica-scandal-proves/IzaXxl2BsYBtwM4nxezgcP/story.html
Cambridge Analytica scandal: legitimate researchers using Facebook data could be collateral damage. Annabel Latham. The Conversation. https://theconversation.com/cambridge-analytica-scandal-legitimate-researchers-using-facebook-data-could-be-collateral-damage-93600
Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in AI. Sarah Bird, Solon Barocas, Kate Crawford, Fernando Diaz, Hanna Wallach. Workshop on Fairness, Accountability, and Transparency in Machine Learning, 2016. https://www.microsoft.com/en-us/research/wp-content/uploads/2017/10/SSRN-id2846909.pdf
Economic Models of (Algorithmic) Discrimination. Bryce W. Goodman NIPS Symposium on Machine Learning and the Law. http://www.mlandthelaw.org/papers/goodman2.pdf
Rise of the racist robots – how AI is learning all our worst impulses. Stephen Buranyi. The Guardian, 2017. https://www.theguardian.com/inequality/2017/aug/08/rise-of-the-racist-robots-how-ai-is-learning-all-our-worst-impulses
Machine Bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner. ProPublica, 2016. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Google says sorry for racist auto-tag in photo app. Jana Kasperkevic. The Guardian, 2015. https://www.theguardian.com/technology/2015/jul/01/google-sorry-racist-auto-tag-photo-app
Can an Algorithm Hire Better Than a Human? Claire Cain Miller. The New York Times, 2015. https://www.nytimes.com/2015/06/26/upshot/can-an-algorithm-hire-better-than-a-human.html
When Algorithms Discriminate Claire Cain Miller. The New York Times, 2015. https://www.nytimes.com/2015/07/10/upshot/when-algorithms-discriminate.html
A survey on measuring indirect discrimination in machine learning. https://arxiv.org/pdf/1511.00148.pdf Is it ethical to avoid error analysis?. https://arxiv.org/pdf/1706.10237.pdf
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Cathy O’Neil. Times Higher Education, 2016. https://www.timeshighereducation.com/books/review-weapons-of-math-destruction-cathy-o-neil-allen-lane
Review: Weapons of Math Destruction. Evelyn Lamb. Scientific American, 2016. https://blogs.scientificamerican.com/roots-of-unity/review-weapons-of-math-destruction/
Does GDPR require Machine Learning algorithms to explain their output? Probably not, but experts disagree and there is enough ambiguity to keep lawyers busy. Gregory Piatetsky. KDnuggets, 2018. https://www.kdnuggets.com/2018/03/gdpr-machine-learning-illegal.html
Towards accountable AI in Europe? Sandra Wachter. The Alan Turing Institute, 2017. https://www.turing.ac.uk/media/opinion/towards-accountable-ai-europe/
General Data Protection Regulation (GDPR) and Data Science Thomas Dinsmore. Cloudera, 2017. https://vision.cloudera.com/general-data-protection-regulation-gdpr-and-data-science/
There is a blind spot in AI research. Kate Crawford and Ryan Calo. Nature 538, 311–313, 2016. doi:10.1038/538311a https://www.nature.com/news/there-is-a-blind-spot-in-ai-research-1.20805
Debugging data: Microsoft researchers look at ways to train AI systems to reflect the real world. John Roach. The AI Blog, Microsoft Research, 2017. https://blogs.microsoft.com/ai/debugging-data-microsoft-researchers-look-ways-train-ai-systems-reflect-real-world/
20 lessons on bias in machine learning systems by Kate Crawford at NIPS 2017. Aarthi Kumaraswamy. Packt Hub, 2017. https://hub.packtpub.com/20-lessons-bias-machine-learning-systems-nips-2017/
Big Data, Machine Learning, and the Social Sciences: Fairness, Accountability, and Transparency. Hanna Wallach. 2014. https://medium.com/@hannawallach/big-data-machine-learning-and-the-social-sciences-927a8e20460d
Locating ethics in data science: responsibility and accountability in global and distributed knowledge production systems Sabina Leonelli Phil. Trans. R. Soc. A 2016 374 20160122; DOI: 10.1098/rsta.2016.0122. Published 14 November 2016 http://rsta.royalsocietypublishing.org/content/374/2083/20160122
How the machine ‘thinks’: Understanding opacity in machine learning algorithms. 2016. Jenna Burrell. Big Data & Society https://doi.org/10.1177/2053951715622512
Unique in the shopping mall: On the reidentifiability of credit card. Yves-Alexandre de Montjoye, Laura Radaelli, Vivek Kumar Singh, Alex “Sandy” Pentland. Science 30 Jan 2015: Vol. 347, Issue 6221, pp. 536-539 DOI: 10.1126/science.1256297. Ver também material suplementar para detalhes http://science.sciencemag.org/content/sci/suppl/2015/01/28/347.6221.536.DC1/ deMontjoye.SM.pdf
Unique in the Crowd: The privacy bounds of human mobility. Yves-Alexandre de Montjoye, César A. Hidalgo, Michel Verleysen & Vincent D. Blondel. Scientific Reports 3, Article number: 1376 (2013) doi:10.1038/srep01376
Big data security problems threaten consumers’ privacy http://theconversation.com/big-data-security-problems-threaten-consumers-privacy-54798
How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/#6e8b5d816668
Welcome To The Surveillance State: China's AI Cameras See All. Ryan Grenoble. The Huffington Post, 2017. https://www.huffpostbrasil.com/entry/china-surveillance-camera-big-brother_us_5a2ff4dfe4b01598ac484acc
Why big data has made your privacy a thing of the past https://www.theguardian.com/technology/2013/oct/06/big-data-predictive-analytics-privacy
Control use of data to protect privacy. Susan Landau. Science 30 Jan 2015: Vol. 347, Issue 6221, pp. 504-506 DOI: 10.1126/science.aaa4961
What the “right to be forgotten” means for privacy in a digital age. Abraham L. Newman. Science 30 Jan 2015: Vol. 347, Issue 6221, pp. 507-508 DOI: 10.1126/science.aaa4603
Metcalf, Jacob, Emily F. Keller, and danah boyd. 2017. “Perspectives on Big Data, Ethics, and Society.” Council for Big Data, Ethics, and Society. Accessed May 28, 2017. http://bdes.datasociety.net/council-output/perspectives-on-big-data-ethics-and-society/.
D. E. O'Leary, "Ethics for Big Data and Analytics," in IEEE Intelligent Systems, vol. 31, no. 4, pp. 81-84, July-Aug. 2016. doi: 10.1109/MIS.2016.70
What is data ethics? Luciano Floridi, Mariarosaria Taddeo. Published 14 November 2016.DOI: 10.1098/rsta.2016.0360
The ethics of algorithms: Mapping the debate Brent Daniel Mittelstadt, Patrick Allo, Mariarosaria Taddeo,Sandra Wachter, Luciano Floridi, Big Data & Society Vol 3, Issue 2 http://journals.sagepub.com/doi/abs/10.1177/2053951716679679
Kraemer, F., Van Overveld, K., & Peterson, M. (2011). Is there an ethics of algorithms?. Ethics and Information Technology, 13(3), 251-260. https://link.springer.com/article/10.1007/s10676-010-9233-7
A Step Towards Accountable Algorithms?: Algorithmic Discrimination and the European Union General Data Protection. Bryce W. Goodman. NIPS Symposium on Machine Learning and the Law, 2016. http://www.mlandthelaw.org/papers/goodman1.pdf
Reducing discrimination in AI with new methodology. Kush Varshney. IBM Research, 2017. https://www.ibm.com/blogs/research/2017/12/ai-reducing-discrimination/
7 Short-Term AI ethics questions. Orlando Torres. Towards Data Science, 2018. https://towardsdatascience.com/7-short-term-ai-ethics-questions-32791956a6ad
Inverse Privacy. Yuri Gurevich, Efim Hudis, and Jeannette M. Wing. Communications of the ACM 59 (7), 2016. http://www.cs.cmu.edu/~wing/publications/Gurevich-Hudis-Wing16.pdf
Algorithmic accountability reporting: on the investigation of black boxes. Nicholas Diakopoulos. Tow Center for Digital Journalism, 2014. http://towcenter.org/wp-content/uploads/2014/02/78524_Tow-Center-Report-WEB-1.pdf
- 29/05: Artigos 1, 4, 5, 7, 13, 8, 14, 15
- 05/06: Artigos 9, 10, 11, 23, 20, 21, 22
- 07/06: Artigos 2, 3, 6, 12, 16, 17, 18, 19