↳ Staff+picks

October 24th, 2019

↳ Staff+picks

Exploitation, Cooperation, and Distributive Justice

An interview with John Roemer

Throughout his career, John Roemer's work has been uniquely situated between the fields of microeconomics, game theory, philosophy, and political science. His research makes use of the tools of classical economics to analyze dynamics typically thought to be outside the scope of economics: from notions of fairness and morality, to the possibility of overcoming capitalist social relations. In doing so, it defends those tools against charges that they can’t describe the behaviors we see, at the same time as it renders vital social questions digestible for disciplines that rarely engage them.

Roemer is perhaps best known for his contributions to theories of distributive justice. Within the field of moral philosophy, he is one of a handful of scholars who have sought to formalize distributive theories in order to compare their merits. To moral philosophers, he argues that outright dismissal of consequentialist theories of justice, and their replacement by complicated deontological models, is a mistake. And to the world of economics, he posits that economic theory cannot be divorced from moral philosophy—that the emphasis on reaching equilibrium itself necessarily carries moral assumptions.

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October 17th, 2019

Disparate Causes, pt. II

On the hunt for the correct counterfactual

An accurate understanding of the nature of race in our society is a prerequisite for an adequate normative theory of discrimination. If, as part one of this post suggests, limiting discrimination to only direct effects of race misunderstands the nature of living as a raced subject in a raced society, then perhaps the extension of the scope of discrimination to also include indirect effects of race would better approximate the social constructivist view of race.

Recent approaches to causal and counterfactual fairness seek fair decision procedures “achieved by correcting the variables that are descendants of the protected attribute along unfair pathways.”1 The method, thus, cancels out certain effects that are downstream of race in the diagram, thereby retaining only those path-specific effects of race that are considered fair. Despite the expanded scope of what counts as a discriminatory effect, the logic of the Path-Specific Effects method follows that of the original Pearlian causal counterfactual model of discrimination: race, as a sensitive attribute, is toggled white or black atop a causal diagram, and its effect cascades down various paths leading to the outcome variable. But, this time, the causal fairness technician does more than measure and limit the direct effect of race on the final outcome; she now also measures effects of race that are mediated by other attributes, keeping only those effects carried along paths deemed “fair.”

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October 11th, 2019

Disparate Causes, pt. I

The shortcomings of causal and counterfactual thinking about racial discrimination

Legal claims of disparate impact discrimination go something like this: A company uses some system (e.g., hiring test, performance review, risk assessment tool) in a way that impacts people. Somebody sues, arguing that it has a disproportionate adverse effect on racial minorities, showing initial evidence of disparate impact. The company, in turn, defends itself by arguing that the disparate impact is justified: their system sorts people by characteristics that—though incidentally correlated with race—are relevant to its legitimate business purposes. Now, the person who brought the discrimination claim is tasked with coming up with an alternative—that is, a system with less disparate impact and still fulfills the company’s legitimate business interest. If the plaintiff finds such an alternative, it must be adopted. If they don’t, the courts have to, in theory, decide how to tradeoff between disparate impact and legitimate business purpose.

Much of the research in algorithmic fairness, a discipline concerned with the various discriminatory, unfair, and unjust impacts of algorithmic systems, has taken cues from this legal approach—hence, the deluge of parity-based “fairness” metrics mirroring disparate impact that have received encyclopedic treatment by computer scientists, statisticians, and the like in the past few years. Armed with intuitions closely linked with disparate impact litigation, scholars further formalized the tradeoffs between something like justice and something like business purpose—concepts that crystallized in the literature under the banners of “fairness” and “efficiency.”

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July 18th, 2019

Student Debt & Racial Wealth Inequality

How student debt cancellation affects the racial wealth gap

The effect of cancelling student debt on various measures of individual and group-level inequality has been a matter of controversy, especially given presidential candidates’ recent and high-profile proposals to eliminate outstanding student debt. In this work, I attempt to shed light on the policy counterfactual by analyzing the Survey of Consumer Finances for 2016, the most recent nationally-representative dataset that gives a picture of the demographics of student debt.

When we test the effects of cancelling student debt on the racial wealth gap, we conclude that across all samples, across all quantiles, the racial wealth gap narrows when student debt is cancelled, and it narrows more the more student debt is cancelled.

With respect to the two presidential candidates’ plans, this means that the Sanders plan, completely eliminating outstanding student debt, reduces racial wealth inequality more than does the Warren plan, which only forgives $50,000 of debt, and phases that out for high earners. But the difference between the two plans as measured by the reduction in the racial wealth gap is not large. It would be fair to say that the Warren plan achieves the vast majority of the racial wealth equity gains that the Sanders plan achieves, while leaving the student debt held by the highest-income borrowers intact.

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June 13th, 2019

Elections, Social Democracy, and the Neoliberal Shift

An interview with Adam Przeworski

Throughout the 20th century, radical social movements were plagued by their relationship to existing state institutions. Across Western Europe, labor movements found political expression in parties like the Swedish Social Democrats, the German SPD, and the French Socialist Party. In their pursuit of the democratization of wealth and political power, these organizations were criticized for moderating popular demands in favor of cross-party compromise. And while social democratic governments did make significant gains in the postwar period, today's landscape seems to testify against the durability of their reforms.

I met with Adam Przeworski—Professor of Politics at NYU, former member of the September Group of analytical Marxists, and a leading theorist of political economy—to discuss the role of elections in effecting social change, and the political transformations underway today. Over the course of a career spanning thirteen books and over 150 published articles, Przeworski's foremost contributions have been in the study of democratic transitions, distributional politics, and the determinants of economic growth.

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May 16th, 2019

Feminist Theory, Gender Inequity, and Basic Income

An interview with Almaz Zelleke

Feminist and women's movements in the mid-20th century developed demands for an unconditional basic income that emerged out of concrete experiences with the welfare state. What can the current discussion around UBI learn from examining this largely sidelined history?

In this conversation with basic income scholar Almaz Zelleke, we look at this history—and examine the reasons for its absence from the dominant intellectual histories of unconditional cash transfers. More broadly, our conversation explores political change and the processes that lead to policy creation. It touches on the movements that have brought basic income into the 2020 election cycle, considers how to focus political will surrounding basic income, and concludes with policy recommendations that will move America incrementally towards an unconditional UBI.

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March 28th, 2019

Experiments for Policy Choice

Randomized experiments have become part of the standard toolkit for policy evaluation, and are usually designed to give precise estimates of causal effects. But, in practice, their actual goal is to pick good policies. These two goals are not the same.

Is this the best way to go about things? Can we maybe make better policy choices, with smaller experimental budgets, by doing things a little differently? This is the question that Anja Sautmann and I address in our new work on “Adaptive experiments for policy choice.” If we wish to pick good policies, we should run experiments adaptively, shifting toward better policies over time. This gives us the highest chance to pick the best policy after the experiment has concluded.

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March 1st, 2019

The Case for an Unconditional Safety Net

Imagine a system where everyone had a right to basic material safety, and could say “no” to abuse and exploitation. Sounds utopian? I argue that it would be quite feasible to get there, and that it would make eminent economic, moral, and political sense.

In my paper, I discuss four sets of arguments why it would make economic, moral, and political sense to transition from the current system of subsidizing low wage work to a system providing an unconditional safety net.

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February 4th, 2019

Cash and Income Studies: A Literature Review of Theory and Evidence

What happens when you give people cash? How do they use the money, and how does it change their lives? Every cash study on this list is different: the studies vary in intervention type, research design, location, size, disbursement amount, and effects measured. The interventions listed here include basic income and proxies--earned income tax credits, negative income tax credits, conditional cash transfers, and unconditional cash transfers. The variety present here prevents us from being able to make broad claims about the effects of universal basic income. But because of its variety, this review provides a sense of the scope of research in the field, capturing what kinds of research designs have been used, and what effects have been estimated, measured, and reported. The review also allows us to draw some revealing distinctions across experimental designs.

If you’re interested in creating a UBI policy, there are roughly three levels of effects (after ODI) that you can examine.

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October 18th, 2018

Machine Ethics, Part One: An Introduction and a Case Study

The past few years have made abundantly clear that the artificially intelligent systems that organizations increasingly rely on to make important decisions can exhibit morally problematic behavior if not properly designed. Facebook, for instance, uses artificial intelligence to screen targeted advertisements for violations of applicable laws or its community standards. While offloading the sales process to automated systems allows Facebook to cut costs dramatically, design flaws in these systems have facilitated the spread of political misinformation, malware, hate speech, and discriminatory housing and employment ads. How can the designers of artificially intelligent systems ensure that they behave in ways that are morally acceptable--ways that show appropriate respect for the rights and interests of the humans they interact with?

The nascent field of machine ethics seeks to answer this question by conducting interdisciplinary research at the intersection of ethics and artificial intelligence. This series of posts will provide a gentle introduction to this new field, beginning with an illustrative case study taken from research I conducted last year at the Center for Artificial Intelligence in Society (CAIS). CAIS is a joint effort between the Suzanne Dworak-Peck School of Social Work and the Viterbi School of Engineering at the University of Southern California, and is devoted to “conducting research in Artificial Intelligence to help solve the most difficult social problems facing our world.” This makes the center’s efforts part of a broader movement in applied artificial intelligence commonly known as “AI for Social Good,” the goal of which is to address pressing and hitherto intractable social problems through the application of cutting-edge techniques from the field of artificial intelligence.

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