Here's a paper about school choice in New Orleans, organized around interviews with New Orleans "Experts and Administrators" on the one hand and "Activists and Educators" on the other.
Akchurin, Maria, and Gabriel Chouhy. "Designing Better Access to Education? Unified Enrollment, School Choice, and the Limits of Algorithmic Fairness in New Orleans School Admissions." Qualitative Sociology (2024): 1-43.
Abstract: "Economic sociologists have long recognized that markets have moral dimensions, but we know less about how everyday moral categories like fairness are reconciled with competing market principles like efficiency, especially in novel settings combining market design and algorithmic technologies. Here we explore this tension in the context of education, examining the use of algorithms alongside school choice policies. In US urban school districts, market design economists and computer scientists have applied matching algorithms to build unified enrollment (UE) systems. Despite promising to make school choice both fair and efficient, these algorithms have become contested. Why is it that algorithmic technologies intended to simplify enrollment and create a fairer application process can instead contribute to the perception they are reproducing inequality? Analyzing narratives about the UE system in New Orleans, Louisiana, USA, we show that experts designing and implementing algorithm-based enrollment understand fairness differently from the education activists and families who use and question these systems. Whereas the former interpret fairness in narrow, procedural, and ahistorical terms, the latter tend to evaluate fairness with consequentialist reasoning, using broader conceptions of justice rooted in addressing socioeconomic and racial inequality in Louisiana, and unfulfilled promises of universal access to quality schools. Considering the diffusion of “economic styles of reasoning” across local public education bureaucracies, we reveal how school choice algorithms risk becoming imbued with incommensurable meanings about fairness and justice, compromising public trust and legitimacy. The study is based on thirty interviews with key stakeholders in the school district’s education policy field, government documents, and local media sources."
"Designing and implementing algorithm-based UE systems entails complex moral and political considerations, including questions about how to operationalize what is fair when giving priority to some students over others. The designers and supporters of these systems argue that, by automating and randomizing assignments to oversubscribed schools, UE algorithms are not only efficient, but also impartial and, therefore, value-neutral. Yet as policy instruments, their use is explicitly predicated on normative grounds: centralized enrollment platforms seek to make choice more transparent and fair, which in practice means weakening the influence of social privileges in access to educational opportunities. But even if UE systems constitute powerful technologies that deliver simple and efficient enrollment across the board, providing greater access to school choice, is it possible that they still end up eroding public trust and contributing to the perception they are reproducing inequality? And if so, why?
,,,
"We argue that a crucial reason why technically irreproachable policy instruments like UE algorithms may fall short of eliciting sufficient moral consensus and become enmeshed in political disputes is that core values like fairness are defined and interpreted differently across the contexts where such instruments are created and used.
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"We examine the multivalent meanings of algorithmic fairness through a study of OneApp, the unified enrollment system developed more than a decade ago in New Orleans, Louisiana (NOLA).Footnote1 Well-known as a national exemplar of market-based school reform, New Orleans is unique in that all the city’s public-school students now attend charter schools, a radical experiment widely celebrated by the school reform movement that has nevertheless elicited heated debate. Our study shows that a paradigmatic clash has emerged between how fairness in the enrollment process is understood “from above” and “from below.” Fairness tends to be interpreted in narrow, procedural, and ahistorical terms by education experts who design and shepherd UE through implementation, even if many do imbue UE with the normative purpose of limiting the influence of social privilege in access to school choice. By contrast, education activists tend to evaluate fairness with consequentialist reasoning and in terms of broader conceptions of justice rooted in addressing the history of socioeconomic and racial inequality in New Orleans, and the unfulfilled promise of access to quality schools for all. From a top-down perspective, then, UE algorithms are seen as a positive step towards making the school system a more equitable marketplace. In this view, an algorithm-based enrollment system plays a critical role in the democratization of choice. Seen from below by those left out, however, the same algorithms legitimize an inherently unjust market system where chance still determines (unequal) access to educational opportunity. Moreover, the fact that parents need to participate in an algorithmic process instead of directly enrolling their kids in a good-quality neighborhood school signals the absence of real equity.
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"promoting choice options such as charter schools has yielded benefits to both students who enroll in them and—via competitive effects—those who attend schools nearby (Berends 2015; Jabbar et al. 2022) in some (but not all) cases. On the other hand, researchers have also warned that choice policies can exacerbate existing inequalities, insofar as access to valued information, social networks, and resources are crucial for capitalizing on the new opportunities that become available in a more competitive marketplace
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“market design” is perhaps the specialization area that most enthusiastically embodies the “performative” aspect of economics practice—the idea that economists not only describe markets but also perform them through sociotechnical devices (Caliskan and Callon 2009; Callon 1998; MacKenzie and Millo 2003). ... For too long, the design of “fair”, “efficient”, and “transparent” UE algorithms has remained a technical matter in the hands of experts, not an object of study worth analyzing from sociological, political, or even philosophical standpoints.
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"Interviews consisted of a semi-structured component following an interview guide and a component relying on vignettes designed to compare how our interviewees conceptualize fairness across the same four scenarios. After the first part of the interview, we typically took turns reading vignettes aloud and asking the same set of follow-up questions to our respondents. For example, the first scenario describes Malcolm, a hypothetical student whose family uses OneApp to apply to elementary school and he gets his fifth-choice school, which is rated a C. We then ask respondents to evaluate whether Malcolm has been treated fairly, gradually adding new information about his socioeconomic status, racial background, and disability status.
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"In this study, we do not rely on statistical sampling logic and do not seek to make generalizable claims about perceptions of fairness regarding OneApp among all administrators or all NOLA families using this UE system. Instead, we aim to show how studying an algorithmic tool reveals how experts and community leaders embedded in the same education policy field have different ways of conceptualizing and talking about fairness.
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"For instance, when we described the experience of a hypothetical student, Malcolm, whose parents used OneApp to apply to elementary school last year, many respondents rejected the notion of procedural fairness outright. In the scenario, Malcolm and his family had secured a spot in a school with the letter grade C that was their fifth preference. When we asked our respondents whether Malcolm had been treated fairly, one respondent from an education justice organization replied, “No, I don’t think it’s fair and it makes me wonder what is a better way because [the explanation we hear is], ‘We need more quality seats.’ I’m like, ‘Oh really? How are we going to get there?’ Because we want more quality seats”
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The article goes on to point out that the school district hasn't published the algorithm code or flow charts, which adds to suspicions of unfairness. My inclination is that such things should be in the public domain, which might help the discussion focus on the very different issues of how schools are assigned, and why not all schools are first rate.
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