20-Dissertation.Pdf

HARVARD UNIVERSITY Graduate School of Arts and Sciences DISSERTATION ACCEPTANCE CERTIFICATE The undersigned, appointed by the Harvard John A. Paulson School of Engineering and Applied Sciences have examined a dissertation entitled: “Risky Reforms: A Sociotechnical Analysis of Algorithms as Tools for Social Change” presented by: Ben Green candidate for the degree of Doctor of Philosophy and here by Signature Typed name: Professor Y. Chen Signature Typed name: Professor F. Doshi-Velez Signature Mary Gray Typed name: Professor M. Gray Signature: Typed name: Professor A. Papachristos August 25, 2020 Risky Reforms: A Sociotechnical Analysis of Algorithms as Tools for Social Change a dissertation presented by Ben Green to The School of Engineering and Applied Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Applied Mathematics Harvard University Cambridge, Massachusetts August 2020 ©2020 – Ben Green All rights reserved. Thesis advisor: Professor Yiling Chen Ben Green Risky Reforms: A Sociotechnical Analysis of Algorithms as Tools for Social Change Abstract This thesis considers the relationship between efforts to address social problems using algorithms and the social impacts of these interventions. Despite widespread optimism about algorithms as tools to promote reform and improve society, there is often little rigorous analysis regarding how algorithmic interventions will lead to particular desired outcomes. In turn, many well-intentioned applications of algorithms have led to social harm. In this thesis, I focus on the use of “risk assessments” in the U.S. criminal justice as a notable example of machine learning algorithms being used as tools for social change. Treating these algorithmic interventions as sociotechnical and political reform efforts rather than primarily technical projects, I center my analyses of risk assessments around their social and political consequences. In Part I (Interaction), I introduce a new “algorithm-in-the-loop” framework for evaluating the impacts of algorithms in practice, using experiments to uncover unexpected be- haviors that occur when people collaborate with risk assessments. In Part II (Risk and Response), I interrogate typical conceptions of risk and how to respond to it, developing a novel machine learning method to analyze structural factors of violence and to support non-punitive and public health-inspired violence prevention efforts. In Part III (Reform), I place these technical studies in the broader context of social and political reform, describing the limits of risk assessments as a tool for criminal justice reform and articulating a new mode of practice—“algorithmic realism”—that synthesizes computer science, law, STS, and political theory in order to equip computer scientists to work more rigorously in the service of social change. By expanding the scope of questions asked of risk assessments, this dissertation sheds new light on how risk assessments represent a “risky” strategy for achieving criminal justice reform. Through these analyses, I chart the beginnings of a more interdisciplinary and rigorous approach to evaluating and developing algorithms as tools for social change. iii Contents 1 Introduction 1 1.1 Criminal Justice Risk Assessments ................................ 3 1.2 Algorithmic Fairness ....................................... 5 1.3 Outline ............................................... 7 I Interaction 9 2 An Algorithm-in-the-Loop Approach to Decision-Making 10 2.1 Introduction ............................................ 10 2.2 Related Work ........................................... 12 2.3 The Algorithm-in-the-Loop Framework ............................. 15 2.4 Experimental Approach ...................................... 18 2.5 Limitations ............................................. 26 3 Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments 28 3.1 Introduction ............................................ 28 3.2 Methods .............................................. 30 3.3 Results ............................................... 34 3.4 Discussion ............................................. 46 4 The Principles and Limits of Algorithm-in-the-Loop Decision-Making 50 4.1 Introduction ............................................ 50 4.2 Principles for Algorithm-in-the-Loop Decision-Making ..................... 53 4.3 Methods .............................................. 55 4.4 Results ............................................... 61 4.5 Discussion ............................................. 70 5 Algorithmic Risk Assessments Can Distort Human Decision-Making in High-Stakes Gov- ernment Contexts 76 5.1 Introduction ............................................ 76 5.2 Methods .............................................. 78 5.3 Results ............................................... 90 5.4 Alternative Explanations ..................................... 103 5.5 Discussion ............................................. 106 iv II Risk and Response 108 6 Predictions and Policing 109 6.1 Predictive Policing ......................................... 109 6.2 Responding to Predictions .................................... 114 7 Modeling Contagion Through Social Networks to Explain and Predict Gunshot Violence 118 7.1 Introduction ............................................ 118 7.2 Methods .............................................. 123 7.3 Results ............................................... 143 7.4 Discussion ............................................. 150 III Reform 153 8 The False Promise of Risk Assessments: Epistemic Reform and the Limits of Fairness 154 8.1 Introduction ............................................ 154 8.2 Objectivity ............................................. 157 8.3 Criminal Justice Reform ...................................... 162 8.4 Epistemic Reform ......................................... 169 8.5 Discussion: Algorithmic Fairness and Social Change ...................... 177 9 Algorithmic Realism: Expanding the Boundaries of Algorithmic Thought 181 9.1 Introduction ............................................ 181 9.2 Algorithmic Formalism ...................................... 186 9.3 Formalist Incorporation ...................................... 194 9.4 Methodological Reform: From Formalism to Realism in the Law ................ 196 9.5 Algorithmic Realism ........................................ 201 9.6 Discussion ............................................. 208 10 Conclusion 210 References 214 v Acknowledgments I first want to thank my advisor, Yiling Chen, for all of her support and mentorship. Yiling graciously took me on as a student halfway through my PhD and has been an incredible and empowering advisor, teaching me how to develop a research agenda, supporting all of my interdisciplinary interests and side projects, funding me to attend conferences and workshops, and helping me navigate the job market. I have learned a great deal from her about how to be a good scholar, collaborator, and advisor, and look forward to calling on her wisdom and advice for many years. I am also grateful to the rest of my committee. Andrew Papachristos has been a wonderful mentor and collaborator dating back to my undergraduate days. I have learned so much from Mary Gray’s commitment to rigorous interdisciplinary scholarship and the deep care for others that she brings to her work. Finale Doshi- Velez inspires me with her ability to bridge machine learning with other disciplines and her steadfast support for her students. I also want to thank my initial advisor, Radhika Nagpal. Radhika taught me a great deal about how to conduct research and lead a research group during my first few years in graduate school. She responded with remarkable grace and support as my research interests diverged from hers and has continued to be a trusted mentor. I am grateful to the many friends who have made my years at Harvard so fun and intellectually stimulating, in particular Rediet Abebe, Sarah Balakrishnan, Eric Balkanski, Blake Dickson, Will Holub-Moorman, Thibaut Horel, Lily Hu, Jean Pouget-Abadie, Berk Ustun, and Zach Wehrwein. The Berkman Klein Center for Internet & Society and the Harvard Graduate Students Union have also been wonderful hubs of friendship and inspiration. My time at Harvard would have been far less fun and interesting without you. vi My parents, brothers, and grandparents have supported me and my academic pursuits for as long as I can remember, from providing math lessons and book recommendations when I was young to reviewing paper drafts today. I am grateful to have been able to spend my graduate school years so close to my family. Salomé Viljoen has been a wellspring of ideas, inspiration, and support. This dissertation would be nowhere near as fun to have produced nor interesting to read if not for her. The work in this thesis is based on joint work with Yiling Chen, Thibaut Horel, Andrew Papachristos, and Salomé Viljoen and was supported by the National Science Foundation. I have also been fortunate to learn from and collaborate with Paul Bardunias, Lily Hu, J. Scott Turner, Radhika Nagpal, and Justin Werfel. vii Chapter 1 Introduction Following recent advances in the quality and accessibility of machine learning algorithms, the public sector in- creasingly uses machine learning as a central tool to distribute resources and make important decisions [194]. Similarly, much of the work in computer science labs and technology companies

Load more