Trust in Numbers

Porter, Theodore M.

doi:10.1515/9781400821617

Cited by 1,247 publications

(137 citation statements)

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“…Quantified decision-making relies on numbers to inform or make decisions. Organizations often turn to numbers to bolster the objectivity of their decisions (Porter 1995). In particular, quantification provides "mechanical objectivity", a guarantee that decisions are free from individual biases, and thus defensible against outside criticism.…”

Section: Standardization Quantification Actuarialismmentioning

confidence: 99%

“…This system still excluded many women from access to credit, but rather than justifying the exclusion in terms of their sex, creditors pointed to the complex array of factors that predicted repayment, like length of credit history, that disfavored women. In this sense, actuarial techniques may prove difficult to contest not just because of the mechanical objectivity associated with quantification in general (Porter 1995), but also because their focus on risk factors undercuts mobilization based on socially meaningful categories.…”

Section: Theorizing Standardization and Racial Inequalitymentioning

confidence: 99%

“…The points system was not treated as a mechanically objective tool for making decisions (Porter 1995), but an ad hoc system that too coarsely advantaged minority students without clearly linking that advantage to the nebulous goal of promoting diversity. The Gratz majority opinion stated the "automatic distribution of 20 points has the effect of making 'the factor of race… decisive' for virtually every minimally qualified underrepresented minority applicant."…”

Section: Affirmative Action In College Admissionsmentioning

confidence: 99%

“…Standardization and objectivity are often conflated concepts in discussions about the need for more mechanical forms of decision-making, and both are often conflated with quantification. Quantification seems to provide a rational, objective tool for standardizing decisions (Porter 1995). But there is more than one way to use numbers to make a given decision, numbers are not value free, and not all numbers carry the same air of objectivity and rationality (Hirschman et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Standardizing Biases: Selection Devices and the Quantification of Race

Hirschman¹,

Bosk²

2017

Preprint

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Abstract:Racial inequality persists despite major advances in formal, legal equality. Scholars and policymakers argue that individual biases (both explicit and implicit) combine with subjective organizational decision-making practices to perpetuate racial inequality. The standardization of decision-making offers a potential solution, promising to eliminate the subjectivity that biases consequential decisions. We ask, under what conditions may standardization reduce racial inequality? Drawing on research in science studies and law and society, we argue that standardization must be understood as a heterogeneous practice capable of producing very different outcomes depending on the details of the standard and the organizational infrastructure surrounding its use. We compare "selection devices" -simple quantified tools for making allocation decisions -in undergraduate admissions and child welfare to highlight the complex relationships between race and standardization. Child welfare agencies adopted a colorblind actuarial device that attempted to predict which children were most at risk, and then make decisions based on those predictions. In contrast, the University of Michigan's points system explicitly considered and valued race, with the goal of increasing minority student enrollments in the context of promoting student body diversity. Comparing these cases demonstrates how actuarial standardization practices, including those adopted with the intention of reducing racial inequality, tend to reinforce an unequal status quo by ideologically reconfiguring mutable social structures into immutable individual risk factors. In contrast, non-actuarial practices that explicitly promote racial equality are vulnerable to political challenges, as they violate norms of colorblindness and cannot be defended in terms of their predictive validity.Acknowledgements:

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Section: Standardization Quantification Actuarialismmentioning

confidence: 99%

Section: Theorizing Standardization and Racial Inequalitymentioning

confidence: 99%

Section: Affirmative Action In College Admissionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Standardizing Biases: Selection Devices and the Quantification of Race

Hirschman¹,

Bosk²

2017

Preprint

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“…1986-1991), et conduisit à l'explicitation d'un ensemble de grandeurs mesurables devant servir d'étalons pour évaluer l'état d'avancement des différentes phases du projet [4]. En ce qui concerne l'analyse des indicateurs statistiques, les sciences sociales ont permis de se démarquer des modèles hérités des sciences de la nature et de montrer que la définition de métrolo-gies recouvre un ensemble de représentations situées socialement et idéologiquement [5]. En retour, leur usage revêt un contenu normatif par leur capacité performative à valider implicitement et à naturaliser les conventions à partir desquelles elles sont formées.…”

Section: Démultiplication Et Usage Des Indicateurs Statistiques Commeunclassified

De la gestion à la logique de la complexité

Ramillon

2012

Med Sci (Paris)

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Climate Change Attribution: When Is It Appropriate to Accept New Methods?

Lloyd

Орескес

2018

Earth's Future

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The most common approaches to detection and attribution (D&A) of extreme weather events using fraction of attributable risk or risk ratio answer a particular form of research question, namely “What is the probability of a certain class of weather events, given global climate change, relative to a world without?” In a set of recent papers, Trenberth et al. (2015, https://doi.org/10.1038/nclimate2657) and Shepherd (2016, https://doi.org/10.1007/s40641-016-0033-y) have argued that this is not always the best tool for analyzing causes, or for communicating with the public about climate events and extremes. Instead, they promote the idea of a “storyline” approach, which asks complementary questions, such as “How much did climate change affect the severity of a given storm?” From the vantage of history and philosophy of science, a proposal to introduce a new approach or to answer different research questions—especially those of public interest—does not appear particularly controversial. However, the proposal proved highly controversial, with the majority of D&A scientists reacting in a very negative and even personal manner. Some suggested the proposed alternatives amount to a weakening of standards, or an abandonment of scientific method. Here, we address the question: Why is this such a controversial proposition? We argue that there is no “right” or “wrong” approach to D&A in any absolute sense, but rather that in different contexts, society may have a greater or lesser concern with errors of a particular type. How we view the relative risk of overestimation versus underestimation of harm is context‐dependent.

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Trust in Numbers

Cited by 1,247 publications

References 0 publications

Standardizing Biases: Selection Devices and the Quantification of Race

Standardizing Biases: Selection Devices and the Quantification of Race

De la gestion à la logique de la complexité

Climate Change Attribution: When Is It Appropriate to Accept New Methods?

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