ABSTRACT
Although computer scientists are eager to help address social problems, the field faces a growing awareness that many well-intentioned applications of algorithms in social contexts have led to significant harm. We argue that addressing this gap between the field's desire to do good and the harmful impacts of many of its interventions requires looking to the epistemic and methodological underpinnings of algorithms. We diagnose the dominant mode of algorithmic reasoning as "algorithmic formalism" and describe how formalist orientations lead to harmful algorithmic interventions. Addressing these harms requires pursuing a new mode of algorithmic thinking that is attentive to the internal limits of algorithms and to the social concerns that fall beyond the bounds of algorithmic formalism. To understand what a methodological evolution beyond formalism looks like and what it may achieve, we turn to the twentieth century evolution in American legal thought from legal formalism to legal realism. Drawing on the lessons of legal realism, we propose a new mode of algorithmic thinking---"algorithmic realism"---that provides tools for computer scientists to account for the realities of social life and of algorithmic impacts. These realist approaches, although not foolproof, will better equip computer scientists to reduce algorithmic harms and to reason well about doing good.
References
- Philip E. Agre. 1997. Toward a Critical Technical Practice: Lessons Learned in Trying to Reform AI. In Social Science, Technical Systems, and Cooperative Work: Beyond the Great Divide, Geoffrey C. Bowker, Susan Leigh Star, William Turner, and Les Gasser (Eds.).Google Scholar
- Alex Albright. 2019. If You Give a Judge a Risk Score: Evidence from Kentucky Bail Decisions. The John M. Olin Center for Law, Economics, and Business Fellows' Discussion Paper Series 85 (2019).Google Scholar
- Michelle Alexander. 2012. The New Jim Crow: Mass Incarceration in the Age of Colorblindness. The New Press.Google Scholar
- Elizabeth Anderson. 2009. Toward a Non-Ideal, Relational Methodology for Political Philosophy: Comments on Schwartzman's "Challenging Liberalism". Hypatia 24, 4 (2009), 130--145. www.jstor.org/stable/20618184Google Scholar
Cross Ref
- Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine Bias. ProPublica (2016). https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencingGoogle Scholar
- Mariam Asad. 2019. Prefigurative Design As a Method for Research Justice. Proceedings of the ACM on Human-Computer Interaction. 3, CSCW (2019), 200:1--200:18. Google Scholar
Digital Library
- Solon Barocas and Andrew D. Selbst. 2016. Big Data's Disparate Impact. California Law Review 104 (2016), 671--732.Google Scholar
- Dan Baum. 2016. Legalize It All. Harper's Magazine (2016). https://harpers.org/archive/2016/04/legalize-it-all/Google Scholar
- Matthew J. Bauman, Kate S. Boxer, Tzu-Yun Lin, Erika Salomon, Hareem Naveed, Lauren Haynes, Joe Walsh, Jen Helsby, Steve Yoder, and Robert Sullivan. 2018. Reducing Incarceration through Prioritized Interventions. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS '18). ACM, 6:1--6:8. Google Scholar
Digital Library
- Eric P.S. Baumer and M. Six Silberman. 2011. When the implication is not to design (technology). In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '11). ACM, 2271--2274. Google Scholar
Digital Library
- Sebastian Benthall. 2018. Critical reflections on FAT* 2018: a historical idealist perspective. DATACTIVE (2018). https://data-activism.net/2018/04/critical-reflections-on-fat-2018-a-historical-idealist-perspective/Google Scholar
- Dimitris Bertsimas, Arthur Delarue, and Sebastien Martin. 2019. Optimizing schools' start time and bus routes. Proceedings of the National Academy of Sciences 116, 13 (2019), 5943--5948. Google Scholar
Cross Ref
- Justin J. Boutilier and Timothy C.Y. Chan. 2018. Ambulance Emergency Response Optimization in Developing Countries. arXiv preprint arXiv:1801.05402 (2018).Google Scholar
- Geoffrey Bowker, Susan Leigh Star, Les Gasser, and William Turner. 2014. Social Science, Technical Systems, and Cooperative Work: Beyond the Great Divide. Routledge.Google Scholar
- Geoffrey C. Bowker and Susan Leigh Star. 2000. Sorting Things Out: Classification and Its Consequences. MIT Press.Google Scholar
- danah boyd and Kate Crawford. 2012. Critical Questions for Big Data. Information, Communication & Society 15, 5 (2012), 662--679. Google Scholar
Cross Ref
- Robert Brauneis and Ellen P. Goodman. 2018. Algorithmic Transparency for the Smart City. The Yale Journal of Law & Technology 20 (2018), 103--176.Google Scholar
- Meredith Broussard. 2018. Artificial Unintelligence: How Computers Misunderstand the World. MIT Press.Google Scholar
Cross Ref
- Joy Buolamwini and Timnit Gebru. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, A. Friedler Sorelle and Wilson Christo (Eds.), Vol. 81. PMLR, 77--91. http://proceedings.mlr.pressGoogle Scholar
- Paul Butler. 2017. Chokehold: Policing Black Men. The New Press.Google Scholar
- Fidel Cacheda, Diego Fernandez, Francisco J. Novoa, and Victor Carneiro. 2019. Early Detection of Depression: Social Network Analysis and Random Forest Techniques. Journal of Medical Internet Research 21, 6 (2019), e12554. Google Scholar
Cross Ref
- Mary Ann Cain. 1999. Problematizing Formalism: A Double-Cross of Genre Boundaries. College Composition and Communication 51, 1 (1999), 89--95.Google Scholar
Cross Ref
- Nicholas Carr. 2014. The Limits of Social Engineering. MIT Technology Review (2014). https://www.technologyreview.com/s/526561/the-limits-of-social-engineering/Google Scholar
- Alexandra Chouldechova. 2017. Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data 5, 2 (2017), 153--163.Google Scholar
Cross Ref
- Patricia Hill Collins. 2000. Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment. Routledge.Google Scholar
- Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017. Algorithmic Decision Making and the Cost of Fairness. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 797--806. Google Scholar
Digital Library
- Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. 2009. Introduction to Algorithms. MIT Press.Google Scholar
- Sasha Costanza-Chock, Maya Wagoner, Berhan Taye, Caroline Rivas, Chris Schweidler, Georgia Bullen, and the T4SJ Project. 2018. #MoreThanCode: Practitioners reimagine the landscape of technology for justice and equity. (2018). https://t4sj.coGoogle Scholar
- Bo Cowgill. 2018. The Impact of Algorithms on Judicial Discretion: Evidence from Regression Discontinuities. (2018).Google Scholar
- Kimberlé Williams Crenshaw. 1988. Foreword: Towards a Race-Conscious Pedagogy in Legal Education. National Black Law Journal 11, 1 (1988), 1--14.Google Scholar
- Kimberlé Williams Crenshaw. 1988. Race, Reform, and Retrenchment: Transformation and Legitimation in Antidiscrimination Law. Harvard Law Review 101, 7 (1988), 1331--1387.Google Scholar
Cross Ref
- Lorraine Daston and Peter Galison. 2007. Objectivity. Zone Books.Google Scholar
- Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013. Predicting Depression via Social Media. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media. AAAI.Google Scholar
- John Dewey. 1938. Logic: The Theory of Inquiry. H. Holt and Company.Google Scholar
- Jessa Dickinson, Mark Díaz, Christopher A. Le Dantec, and Sheena Erete. 2019. "The Cavalry Ain't Coming in to Save Us": Supporting Capacities and Relationships Through Civic Tech. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 123:1--123:21. Google Scholar
Digital Library
- Paul Dourish. 2004. What we talk about when we talk about context. Personal Ubiquitous Computing 8, 1 (2004), 19--30. Google Scholar
Digital Library
- Paul Dourish. 2016. Algorithms and their others: Algorithmic culture in context. Big Data & Society 3, 2 (2016). Google Scholar
Cross Ref
- Anthony Downs. 1962. The law of peak-hour expressway congestion. Traffic Quarterly 16, 3 (1962), 393--409.Google Scholar
- Anthony Dunne and Fiona Raby. 2001. Design Noir: The Secret Life of Electronic Objects. Springer Science & Business Media.Google Scholar
- Gilles Duranton and Matthew A. Turner. 2011. The Fundamental Law of Road Congestion: Evidence from US Cities. The American Economic Review 101, 6 (2011), 2616--2652.Google Scholar
Cross Ref
- Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 (2017), 115. Google Scholar
Cross Ref
- Virginia Eubanks. 2018. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press.Google Scholar
Digital Library
- Andrew Guthrie Ferguson. 2017. The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement. NYU Press.Google Scholar
- Casey Fiesler, Natalie Garrett, and Nathan Beard. 2020. What Do We Teach When We Teach Tech Ethics? A Syllabi Analysis. In The 51st ACM Technical Symposium on Computer Science Education (SIGCSE '20).Google Scholar
- William R. Frey, Desmond U. Patton, Michael B. Gaskell, and Kyle A. McGregor. 2018. Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data. Social Science Computer Review (2018), 0894439318788314.Google Scholar
- Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumeé III, and Kate Crawford. 2018. Datasheets for Datasets. arXiv preprint arXiv:1803.09010 (2018).Google Scholar
- April Glaser and Will Oremus. 2018. "A Collective Aghastness": Why Silicon Valley workers are demanding their employers stop doing business with the Trump administration. Slate (2018). https://slate.com/technology/2018/06/the-tech-workers-coalition-explains-how-silicon-valley-employees-are-forcing-companies-to-stop-doing-business-with-trump.htmlGoogle Scholar
- Eric Gordon and Stephen Walter. 2016. Meaningful Inefficiencies: Resisting the Logic of Technological Efficiency in the Design of Civic Systems. In Civic Media: Technology, Design, Practice, Eric Gordon and Paul Mihailidis (Eds.). 243.Google Scholar
- Andre Gorz. 1967. Strategy for Labor. Beacon Press.Google Scholar
- Ben Green. 2018. Data Science as Political Action: Grounding Data Science in a Politics of Justice. arXiv preprint arXiv:1811.03435 (2018).Google Scholar
- Ben Green. 2018. 'Fair' Risk Assessments: A Precarious Approach for Criminal Justice Reform. In 5th Workshop on Fairness, Accountability, and Transparency in Machine Learning.Google Scholar
- Ben Green. 2018. Putting the J(ustice) in FAT. Berkman Klein Center Collection - Medium (2018). https://medium.com/berkman-klein-center/putting-the-justice-in-fat-28da2b8eae6dGoogle Scholar
- Ben Green. 2019. The Smart Enough City: Putting Technology in Its Place to Reclaim Our Urban Future. MIT Press.Google Scholar
- Ben Green. 2020. The False Promise of Risk Assessments: Epistemic Reform and the Limits of Fairness. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '20). ACM. Google Scholar
Digital Library
- Ben Green and Yiling Chen. 2019. Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19). ACM, 90--99. Google Scholar
Digital Library
- Ben Green and Yiling Chen. 2019. The Principles and Limits of Algorithm-in-the-Loop Decision Making. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 50:1--50:24. Google Scholar
Digital Library
- Ben Green, Thibaut Horel, and Andrew V. Papachristos. 2017. Modeling Contagion Through Social Networks to Explain and Predict Gunshot Violence in Chicago, 2006 to 2014. JAMA Internal Medicine 177, 3 (2017), 326--333. Google Scholar
Cross Ref
- Ben Green and Lily Hu. 2018. The Myth in the Methodology: Towards a Recontextualization of Fairness in Machine Learning. In Machine Learning: The Debates workshop at the 35th International Conference on Machine Learning.Google Scholar
- Daniel Greene, Anna Lauren Hoffmann, and Luke Stark. 2019. Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning. In Proceedings of the 52nd Hawaii International Conference on System Sciences. 2122--2131.Google Scholar
Cross Ref
- David Singh Grewal, Amy Kapczynski, and Jedediah Purdy. 2017. Law and Political Economy: Toward a Manifesto. Law and Political Economy (2017). https://lpeblog.org/2017/11/06/law-and-political-economy-toward-a-manifesto/Google Scholar
- Robert L. Hale. 1923. Coercion and Distribution in a Supposedly Non-Coercive State. Political Science Quarterly 38, 3 (1923), 470--494. Google Scholar
Cross Ref
- Donna Haraway. 1988. Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective. Feminist Studies 14, 3 (1988), 575--599.Google Scholar
Cross Ref
- Sandra Harding. 1998. Is Science Multicultural?: Postcolonialisms, Feminisms, and Epistemologies. Indiana University Press.Google Scholar
- Christina Harrington, Sheena Erete, and Anne Marie Piper. 2019. Deconstructing Community-Based Collaborative Design: Towards More Equitable Participatory Design Engagements. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 216:1--216:25. Google Scholar
Digital Library
- Nabil Hassein. 2017. Against Black Inclusion in Facial Recognition. Digital Talking Drum (2017). https://digitaltalkingdrum.com/2017/08/15/against-black-inclusion-in-facial-recognition/Google Scholar
- Anna Lauren Hoffmann. 2018. Data Violence and How Bad Engineering Choices Can Damage Society. Medium (2018). https://medium.com/s/story/data-violence-and-how-bad-engineering-choices-can-damage-society-39e44150e1d4Google Scholar
- Anna Lauren Hoffmann. 2019. Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse. Information, Communication & Society 22, 7 (2019), 900--915. Google Scholar
Cross Ref
- Wesley Newcomb Hohfeld. 1913. Some Fundamental Legal Conceptions as Applied in Judicial Reasoning. The Yale Law Journal 23, 1 (1913), 16--59.Google Scholar
Cross Ref
- Sarah Holland, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia Chmielinski. 2018. The dataset nutrition label: A framework to drive higher data quality standards. arXiv preprint arXiv:1805.03677 (2018).Google Scholar
- Oliver Wendell Holmes. 1897. The Path of the Law. Harvard Law Review 10 (1897), 457--478.Google Scholar
Cross Ref
- Matthew Hutson. 2018. Artificial intelligence could identify gang crimes---and ignite an ethical firestorm. Science (2018). https://www.sciencemag.org/news/2018/02/artificial-intelligence-could-identify-gang-crimes-and-ignite-ethical-firestormGoogle Scholar
- Armann Ingolfsson, Susan Budge, and Erhan Erkut. 2008. Optimal ambulance location with random delays and travel times. Health Care Management Science 11 (2008), 262--274.Google Scholar
Cross Ref
- Pretrial Justice Institute. 2017. Pretrial Risk Assessment Can Produce Race-Neutral Results. (2017). https://university.pretrial.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=5cebc2e7-dfa4-65b2-13cd-300b81a6ad7aGoogle Scholar
- C.J. Jagtenberg, Sandjai Bhulai, and R.D. van der Mei. 2017. Optimal Ambulance Dispatching. In Markov Decision Processes in Practice. Springer, 269--291.Google Scholar
- Sheila Jasanoff. 2004. Ordering knowledge, ordering society. In States of Knowledge: The Co-Production of Science and the Social Order, Sheila Jasanoff (Ed.). Routledge, 13--45.Google Scholar
- Sheila Jasanoff. 2007. Making Order: Law and Science in Action. In The Handbook of Science and Technology Studies (third ed.), Edward J. Hackett, Olga Amster-damska, Michael E. Lynch, and Judy Wajcman (Eds.). MIT Press, 761--786.Google Scholar
- Elizabeth Joh. 2017. The Undue Influence of Surveillance Technology Companies on Policing. New York University Law Review (2017).Google Scholar
- Justin Jouvenal. 2016. Police are using software to predict crime. Is it a 'holy grail' or biased against minorities? The Washington Post (2016). https://www.washingtonpost.com/local/public-safety/police-are-using-software-to-predict-crime-is-it-a-holy-grail-or-biased-against-minorities/2016/11/17/525a6649-0472-440a-aae1-b283aa8e5de8_story.htmlGoogle Scholar
- Becky Kazansky, Guillén Torres, Lonneke van der Velden, Kersti Wissenbach, and Stefania Milan. 2019. Data for the Social Good: Toward a Data-Activist Research Agenda. Good Data 4 (2019), 244.Google Scholar
- Duncan Kennedy. 2002. The Critique of Rights in Critical Legal Studies. In Left Legalism/Left Critique, Wendy Brown and Janet Halley (Eds.). Duke University Press, 178--228.Google Scholar
- Duncan Kennedy. 2006. Three Globalizations of Law and Legal Thought: 1850-2000. In The New Law and Economic Eevelopment: A Critical Appraisal, David M. Trubek and Alvaro Santos (Eds.). 19--73.Google Scholar
- David Kennedy and William W. Fisher III. 2006. The Canon of American Legal Thought. Princeton University Press.Google Scholar
- Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Cass R. Sunstein. 2019. Discrimination in the Age of Algorithms. Journal of Legal Analysis 10 (2019), 113--174. Google Scholar
Cross Ref
- Jon Kleinberg and Éva Tardos. 2006. Algorithm Design. Pearson Education, Inc.Google Scholar
- Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster, and Thomas Vetter. 2019. Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.Google Scholar
- Pat Langley. 2011. The changing science of machine learning. Machine Learning 82, 3 (2011), 275--279. Google Scholar
Digital Library
- Philip Leith. 1990. Formalism in AI and Computer Science. Ellis Horwood.Google Scholar
- Lawrence Lessig. 2009. Code. Basic Books.Google Scholar
- Empower LLC. 2018. Who's Behind ICE? The Tech and Data Companies Fueling Deportations. Mijente (2018). https://mijente.net/notechforice/Google Scholar
- Karl N. Llewellyn. 1930. A Realistic Jurisprudence-The Next Step. Columbia Law Review 30 (1930), 431.Google Scholar
Cross Ref
- Karl N. Llewellyn. 1931. Some Realism about Realism: Responding to Dean Pound. Harvard Law Review 44, 8 (1931), 1222--1264.Google Scholar
Cross Ref
- Alexandra Mateescu and Madeleine Clare Elish. 2019. AI in Context: The Labor of Integrating New Technologies. Data & Society (2019). https://datasociety.net/wp-content/uploads/2019/01/DataandSociety_AIinContext.pdfGoogle Scholar
- J. Nathan Matias and Merry Mou. 2018. CivilServant: Community-Led Experiments in Platform Governance. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, 9:1--9:13. Google Scholar
Digital Library
- Amanda Meng and Carl DiSalvo. 2018. Grassroots resource mobilization through counter-data action. Big Data & Society 5, 2 (2018). Google Scholar
Cross Ref
- Michele Merler, Nalini Ratha, Rogerio S. Feris, and John R. Smith. 2019. Diversity in Faces. arXiv preprint arXiv:1901.10436 (2019).Google Scholar
- Jacob Metcalf, Emanuel Moss, and danah boyd. 2019. Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics. Social Research 86, 2 (2019), 449--476.Google Scholar
- Mijente. 2019. 1,200+ Students at 17 Universities Launch Campaign Targeting Palantir. (2019). https://notechforice.com/20190916-2/Google Scholar
- Martha Minow. 1997. The Path as Prologue. Harvard Law Review 110, 5 (1997), 1023--1027. Google Scholar
Cross Ref
- Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model Cards for Model Reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency. ACM, 220--229. Google Scholar
Digital Library
- David Moats and Nick Seaver. 2019. "You Social Scientists Love Mind Games": Experimenting in the "divide" between data science and critical algorithm studies. Big Data & Society 6, 1 (2019), 2053951719833404. Google Scholar
Cross Ref
- George O. Mohler, Martin B. Short, P. Jeffrey Brantingham, Frederic Paik Schoenberg, and George E. Tita. 2011. Self-exciting point process modeling of crime. J. Amer. Statist. Assoc. 106, 493 (2011), 100--108.Google Scholar
Cross Ref
- Evgeny Morozov. 2014. To Save Everything, Click Here: The Folly of Technological Solutionism. PublicAffairs.Google Scholar
- Paul Mozur. 2019. One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority. The New York Times (2019). https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.htmlGoogle Scholar
- Gina Neff, Anissa Tanweer, Brittany Fiore-Gartland, and Laura Osburn. 2017. Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science. Big Data 5, 2 (2017), 85--97. Google Scholar
Cross Ref
- Design Justice Network. 2016. Design Justice Network Principles. (2016). http://designjusticenetwork.org/network-principles/Google Scholar
- Helen Nissenbaum. 2009. Privacy in Context: Technology, Policy, and the Integrity of Social Life. Stanford University Press.Google Scholar
- Safiya Umoja Noble. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.Google Scholar
- Peter D. Norton. 2011. Fighting Traffic: The Dawn of the Motor Age in the American City. MIT Press.Google Scholar
- Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 (2019), 447--453. Google Scholar
Cross Ref
- Cathy O'Neil. 2017. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Broadway Books.Google Scholar
- Mimi Onuoha. 2018. Notes on Algorithmic Violence. (2018). https://github.com/MimiOnuoha/On-Algorithmic-ViolenceGoogle Scholar
- Ginger Adams Otis and Nancy Dillon. 2019. Google using dubious tactics to target people with 'darker skin' in facial recognition project: sources. New York Daily News (2019). https://www.nydailynews.com/news/national/ny-google-darker-skin-tones-facial-recognition-pixel-20191002-5vxpgowknffnvbmy5eg7epsf34-story.htmlGoogle Scholar
- George Packer. 2013. Change the World. The New Yorker (2013). http://www.newyorker.com/magazine/2013/05/27/change-the-worldGoogle Scholar
- Samir Passi and Solon Barocas. 2019. Problem Formulation and Fairness. In Proceedings of the Conference on Fairness, Accountability, and Transparency. ACM, 39--48. Google Scholar
Digital Library
- Samir Passi and Steven Jackson. 2017. Data Vision: Learning to See Through Algorithmic Abstraction. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, 2436--2447. Google Scholar
Digital Library
- Trevor J. Pinch and Wiebe E. Bijker. 1987. The Social Construction of Facts and Artifacts: Or How the Sociology of Science and the Sociology of Technology Might Benefit Each Other. In The Social Construction of Technological Systems, Wiebe E. Bijker, Thomas P. Hughes, and Trevor Pinch (Eds.). MIT Press.Google Scholar
- Christopher S. Porrino. 2017. Attorney General Law Enforcement Directive 2016-6 v3.0. (2017). https://www.nj.gov/lps/dcj/agguide/directives/ag-directive-2016-6_v3-0.pdfGoogle Scholar
- Theodore M. Porter. 1995. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton University Press.Google Scholar
- Richard A. Posner. 1997. The Path Away from the Law. Harvard Law Review 110 (1997), 1039.Google Scholar
Cross Ref
- Roscoe Pound. 1909. Liberty of Contract. Yale Law Journal 18, 7 (1909).Google Scholar
Cross Ref
- Roscoe Pound. 1910. Law in Books and Law in Action. American Law Review 44, 1 (1910), 12--36.Google Scholar
- Inioluwa Deborah Raji and Joy Buolamwini. 2019. Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. ACM, 429--435. Google Scholar
Digital Library
- David Robinson and Logan Koepke. 2016. Stuck in a Pattern. Upturn (2016). https://www.teamupturn.org/reports/2016/stuck-in-a-pattern/Google Scholar
- Kevin Rose. 2019. The Making of a YouTube Radical. The New York Times (2019). https://www.nytimes.com/interactive/2019/06/08/technology/youtube-radical.htmlGoogle Scholar
- Jathan Sadowski and Roy Bendor. 2019. Selling Smartness: Corporate Narratives and the Smart City as a Sociotechnical Imaginary. Science, Technology, & Human Values 44, 3 (2019), 540--563. Google Scholar
Cross Ref
- Pedro Saleiro, Benedict Kuester, Abby Stevens, Ari Anisfeld, Loren Hinkson, Jesse London, and Rayid Ghani. 2018. Aequitas: A Bias and Fairness Audit Toolkit. arXiv preprint arXiv:1811.05577 (2018).Google Scholar
- Frederick Schauer. 1987. Formalism. Yale Law Journal 97, 4 (1987), 509--548.Google Scholar
Cross Ref
- Noam Scheiber and Kate Conger. 2019. Uber and Lyft Drivers Gain Labor Clout, With Help From an App. The New York Times (2019). https://www.nytimes.com/2019/09/20/business/uber-lyft-drivers.html?smid=nytcore-ios-shareGoogle Scholar
- Bruce Schneier. 2018. Click Here to Kill Everybody: Security and Survival in a Hyper-connected World. WW Norton & Company.Google Scholar
- William Schofield. 1907. Christopher Columbus Langdell. The American Law Register 55, 5 (1907), 273--296.Google Scholar
- James C. Scott. 1998. Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. Yale University Press.Google Scholar
- Nick Seaver. 2015. The nice thing about context is that everyone has it. Media, Culture & Society 37, 7 (2015), 1101--1109. Google Scholar
Cross Ref
- Nick Seaver. 2017. Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society 4, 2 (2017), 2053951717738104. Google Scholar
Cross Ref
- Nick Seaver. 2019. Knowing Algorithms. In digitalSTS: A Field Guide for Science & Technology Studies, Janet Vertesi and David Ribes (Eds.). 412--422.Google Scholar
- Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. Fairness and Abstraction in Sociotechnical Systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency. ACM, 59--68. Google Scholar
Digital Library
- Brian Cantwell Smith. 1985. The limits of correctness. ACM SIGCAS Computers and Society 14 (1985), 18--26.Google Scholar
Digital Library
- Thomas Smyth and Jill Dimond. 2014. Anti-oppressive design. Interactions 21, 6 (2014), 68--71.Google Scholar
Digital Library
- Bryan Stevenson. 2019. Why American Prisons Owe Their Cruelty to Slavery. The New York Times Magazine (2019). https://www.nytimes.com/interactive/2019/08/14/magazine/prison-industrial-complex-slavery-racism.htmlGoogle Scholar
- Eliza Strickland. 2019. How IBM Watson Overpromised and Underdelivered on AI Health Care. IEEE Spectrum (2019). https://spectrum.ieee.org/biomedical/diagnostics/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-careGoogle Scholar
- Remi Tachet, Paolo Santi, Stanislav Sobolevsky, Luis Ignacio Reyes-Castro, Emilio Frazzoli, Dirk Helbing, and Carlo Ratti. 2016. Revisiting Street Intersections Using Slot-Based Systems. PLOS ONE 11, 3 (2016), e0149607. Google Scholar
Cross Ref
- Roberto Mangabeira Unger. 1987. False Necessity: Anti-Necessitarian Social Theory in the Service of Radical Democracy. Cambridge University Press.Google Scholar
- U.S. Supreme Court. 1905. Lochner v. New York. 198 U.S. 45.Google Scholar
- Arnold Ventures. 2019. Statement of Principles on Pretrial Justice and Use of Pretrial Risk Assessment. (2019). https://craftmediabucket.s3.amazonaws.com/uploads/Arnold-Ventures-Statement-of-Principles-on-Pretrial-Justice.pdfGoogle Scholar
- James Vincent. 2016. Twitter taught Microsoft's AI chatbot to be a racist asshole in less than a day. The Verge (2016). https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racistGoogle Scholar
- Alan Weir. 2015. Formalism in the Philosophy of Mathematics. In The Stanford Encyclopedia of Philosophy, Edward N. Zalta (Ed.). Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/entries/formalism-mathematics/Google Scholar
- Rebecca Wexler. 2018. Life, Liberty, and Trade Secrets: Intellectual Property in the Criminal Justice System. Stanford Law Review 70 (2018), 1343--1429.Google Scholar
- Tom Wilson and Madhumita Murgia. 2019. Uganda confirms use of Huawei facial recognition cameras. Financial Times (2019). https://www.ft.com/content/e20580de-c35f-11e9-a8e9-296ca66511c9Google Scholar
- Jeanette Wing. 2011. Computational Thinking---What and Why? The Link Magazine (2011), 20--23.Google Scholar
- Langdon Winner. 1986. The Whale and the Reactor: A Search for Limits in an Age of High Technology. University of Chicago Press.Google Scholar
- Jimmy Wu. 2019. Optimize What? Commune (2019). https://communemag.com/optimize-what/Google Scholar
- Bernardo Zacka. 2017. When the State Meets the Street: Public Service and Moral Agency. Harvard University Press.Google Scholar
- Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. Defending Against Neural Fake News. arXiv preprint arXiv:1905.12616 (2019).Google Scholar
- Lu Zhen, Kai Wang, Hongtao Hu, and Daofang Chang. 2014. A simulation optimization framework for ambulance deployment and relocation problems. Computers & Industrial Engineering 72 (2014), 12--23. Google Scholar
Cross Ref
- Eli Zimmerman. 2018. Teachers Are Turning to AI Solutions for Assistance. EdTech Magazine (2018). https://edtechmagazine.com/k12/article/2018/06/teachers-are-turning-ai-solutions-assistanceGoogle Scholar
- James Zou and Londa Schiebinger. 2018. AI can be sexist and racist - it's time to make it fair. Nature 559 (2018), 324--326. https://www.nature.com/articles/d41586-018-05707-8Google Scholar
Cross Ref
- Mark Zuckerberg. 2018. Protecting democracy is an arms race. Here's how Facebook can help. The Washington Post (2018). https://www.washingtonpost.com/opinions/mark-zuckerberg-protecting-democracy-is-an-arms-race-heres-how-facebook-can-help-win-it/2018/09/04/53b3c8ee-b083-11e8-9a6a-565d92a3585d_story.htmlGoogle Scholar
Index Terms
Algorithmic realism




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