384

Подробный анализ следствий такого универсального наблюдения см. в: John Gillion and Torin Monahan, Supervision: An Introduction to the Surveillance Society (Chicago: University of Chicago Press, 2012); Ian G. R. Shaw, Predator Empire: Drone Warfare and Full Spectrum Dominance (Minneapolis: University of Minnesota Press, 2018).

385

Evan Selinger and Woodrow Hartzog, “Opinion: It’s Time for an About-Face on Facial Recognition,” Christian Science Monitor, June 22, 2015, https://www.csmonitor.com/World/Passcode/Passcode-Voices/ 2015/0622/Opinion-It-s-time-for-an-about-face-on-facial-recognition.

386

Amy Hawkins, “Beijing’s Big Brother Tech Needs African Faces,” Foreign Policy, July 24, 2018, https://foreignpolicy.com/2018/o7/24/ beijings-big-brother-tech-needs-african-faces/.

387

Darren Byler, “China’s Hi-Tech War on Its Muslim Minority,” Guardian, April 11, 2019, https://www.theguardian.com/news/2019/apr/11/ china-hi-tech-war-on-muslim-minority-xinjiang-uighurs-surveillance-face-recognition; Chris Buckley, Paul Mozur, and Austin Ramzy, “How China Turned a City into a Prison,” New York Times, April 4, 2019, https://www.nytimes.com/interactive/2019/04/04/w0rld/asia/xinjiang-china-surveillance-prison.html; Mozur, “One Month, 500,000 Face Scans.”

388

Luke Stark, “Facial Recognition Is the Plutonium of Al,” XRDS: Crossroads, The ACM Magazine for Students 25, no. 3 (Spring 2019): 50–55; Woodrow Hartzog and Evan Selinger, “Facial Recognition Is the Perfect Tool for Oppression,” Medium, August 2, 2018, https://medium.com/ s/s tory/facial-recognition-is-the-perfect-tool-for-oppression-bc2ao8fofe66.

389

Robert Pear, “On Disability and on Facebook? Uncle Sam Wants to Watch What You Post,” New York Times, March 10, 2019, https://www. nytimes.com/2019/o3/1o/us/politics/social-security-disability-trump-facebook.html.

390

Julie Е. Cohen, Configuring the Networked Self: Law, Code, and the Play of Everyday Practice (New Haven: Yale University Press, 2012). Концепция Коэн о семантической прерывности применима и в сети, где она также защищает пространства анонимности.

391

Margaret Hu, “Big Data Blacklisting,” Florida Law Review 67 (2016): 1735–1809; Eric J. Mitnick, “Procedural Due Process and Reputational Harm: Liberty as Self-Invention,” University of California Davis Law Review 43 (2009): 79-142.

392

Sam Levin, “Face-Reading Al Will Be Able to Predict Your Politics and IQ, Professor Says,” Guardian, September 12, 2017, https:// www.theguardian.com/technology/2017/sep/12/artificial-intelligence-face-recognition-michal-kosinski.

393

Sam Biddle, “Troubling Study Says Artificial Intelligence Can Predict Who Will Be Criminals Based on Facial Features,” Intercept, November 18, 2016, https://theintercept.com/2016/11/18/troubling-study-says-artificial-intelligence-can-predict-who-will-be-criminals-based-on-facial-features/.

394

Matt McFarland, “Terrorist or Pedophile? This Start-Up Says It Can Out Secrets by Analyzing Faces,” Washington Post, May 24, 2016, htt-ps://www.washingtonpost.com/news/innovations/wp/20i6/o5/24/ terrorist-or-pedophile-this-start-up-says-it-can-out-secrets-by-analyzing-faces/?noredirect=on&utm_term=.5ao60615a547. Компания Faception намеревается также классифицировать людей и в более позитивных категориях. См.: Faception, “Our Technology,” https://www.faception.com/our-technology.

395

Dan McQuillan, “People’s Councils for Ethical Machine Learning,” Social Media + Society, April-June 2018: 1-10.

396

Judea Pearl and Dana McKenzie, The Book of Why: The New Science of Cause and Effect (New York: Basic Books, 2018).

397

Frank Pasquale and Glyn Cashwell, “Prediction, Persuasion, and the Jurisprudence of Behaviourism,” University of Toronto Law Journal 68 supp. (2018): 63–81. Также мы должны с осторожностью относиться к данным, основанным на практиках, которые, как было доказано, являются дискриминационными. См.: Rashida Richardson, Jason М. Schultz, and Kate Crawford, “Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice, ” New York University Law Review 94 (2019): 192–233.

398

Fabio Ciucci, “Al (Deep Learning) Explained Simply,” Data Science Central (blog), November 20, 2018, https://www.datasciencecentral. com/profiles/blogs/ai-deep-learning-explained-simply («Методы машинного обучения дают сбой, когда прежние данные достаточно быстро становятся менее значимыми или просто ложными, что бывает часто. Задача или выученные правила должны оставаться теми же, самое большее – изредка обновляться, чтобы вы могли заново провести обучение»).

399

Kiel Brennan-Marquez, “Plausible Cause: Explanatory Standards in the Age of Powerful Machines,” Vanderbilt Law Review 70 (2017): 1249–1302.

400

Robert Н. Sloan and Richard Wagner, “Avoiding Alien Intelligence: A Plea for Caution in the Use of Predictive Systems,” https://papers. ssrn.com/s0l3/papers.cfm?abstract_id=3i63664; Guido Noto La Die-ga, “Against the Dehumanization of Decision-Making: Algorithmic Decisions at the Crossroads of Intellectual Property, Data Protection, and Freedom of Information,” Journal of Intellectual Property, Information Technology andE-commerce Law 9, no. 1 (2018): https://www.jipitec. eu/issues/jipitec-9-1-2018/4677.

401

Patrick Tucker, “The US Military Is Creating the Future of Employee Monitoring,” Defense One, March 26, 2019, https://www.defen-seone.com/technology/2019/o3/us-military-creating-future-employee-monitoring/155 8 2 4/.

402

О ненадежности полиграфа и его запрете в контекстах найма в США см.: Joseph Stromberg, “Lie Detectors: Why They Don’t Work, and Why Police Use Them Anyway,” Vox, Dec 15, 2014, https://www. vox.com/2014/8/14/5999119/polygraphs-lie-detectors-do-they-work.

403

Цит. по ее интервью в: Martin Ford, Architects of Intelligence: The Truth about Al from the People Building It (Birmingham, UK: Packt Publishing, 2018), 217; Мартин Форд, Архитекторы интеллекта: вся правда об искусственном интеллекте от его создателей (Санкт-Петербург: Питер, 2020), 176 (перевод исправлен. – Прим. пер.}.

404

Ifeoma Ajunwa, Kate Crawford, and Jason Schulz, “Limitless Worker Surveillance,” California Law Review 105 (2017): 735–776.

405

Marika Cifor, Patricia Garcia, T. L. Cowan, Jasmine Rault, Tonia Sutherland, Anita Say Chan, Jennifer Rode Anna Lauren Hoffmann, Niloufar Salehi, and Lisa Nakamura, Feminist Data Manifest-No, htt-ps://www.manifestno.com/.

406

Danielle Keats Citron and Frank Pasquale, “The Scored Society: Due Process for Automated Predictions,” Washington Law Review 89 (2014): 1-34. Одним из наиболее амбициозных предложений, нацеленных на устранение угроз конфиденциальности, обсуждавшихся в этой главе, является закон «О подотчетности и прозрачности данных» (Data Accountability and Transparency Act) 2020 г., внесенный на рассмотрение сенатором США Шерродом Брауном.

407

Privacy International, “Case Study: Fintech and the Financial Exploitation of Customer Data,” August 30, 2017, https://www.pri-vacyinternational.org/case-studies/757/case-study-fintech-and-f inancial-exploitation-customer-data.

408

Ibid.

409

Rachel Botsman, “Big Data Meets Big Brother as China Moves to Rate its Citizens,” Wired, Oct. 21, 2017. For a scholarly analysis of SCS’s, see