Risky Reforms - A Sociotechnical Analysis of Algorithms as Tools for Social Change - Dissertation abstract and contents

Sep 16, 2026

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This thesis examines how algorithmic interventions aimed at addressing social problems produce social and political consequences. Despite common optimism that algorithms can drive reform and improve society, rigorous analysis of how specific deployments yield intended outcomes is often limited, and harms can arise from well-intentioned uses. Focusing on U.S. criminal justice “risk assessments,” it treats algorithm deployment as sociotechnical reform and develops an algorithm-in-the-loop framework to study unexpected behaviors. The work further studies risk concepts and responses, including structural violence factors and non-punitive prevention.

LanguageEnglish

CategoryPaper Templates

FormatPDF

Pages252

Reading Time1 h 28 min

AccessFree to download

Abstract

Contents

Introduction

Criminal Justice Risk Assessments

Algorithmic Fairness

Outline

Interaction

An Algorithm-in-the-Loop Approach to Decision-Making

Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments

The Principles and Limits of Algorithm-in-the-Loop Decision-Making

Algorithmic Risk Assessments Can Distort Human Decision-Making in High-Stakes Government Contexts

Risk and Response

Predictions and Policing

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 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 behaviors 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. 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 Limitation

FAQs

What relationship does the thesis study between algorithms and social reform outcomes?

It analyzes how efforts to address social problems using algorithms interact with and shape the social impacts of those interventions.

Why focus on “risk assessments” in U.S. criminal justice?

They provide a concrete example of machine learning being used as a tool for social change, making their social and political consequences central to the analysis.

What is the core purpose of the algorithm-in-the-loop approach described in Part I?

It evaluates real-world algorithm impacts through experiments that reveal unexpected behaviors when people collaborate with risk assessments.

Sep 16, 2026

Upload by Elsa