Real Talk on the Metaphysics of Gender

Dembroff, Robin

doi:10.5840/philtopics201846212

Cited by 50 publications

(40 citation statements)

References 23 publications

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“…If not just a demographic variable in a dataset and not just an individual characteristic, how then should we think about the concepts underlying demographic data like race or gender? In this work, we draw from long histories of scholarship that interrogate categorization schemas and the social harms that can stem from their uncritical adoption [17,32,44,45,48,54,60,126]. Stemming from these understandings, we see demographic data, and demographic categories more broadly, as an attempt to collapse complex social concepts into categorical variables based on observable or selfidentifiable characteristics.…”

Section: Defining Demographic Datamentioning

confidence: 99%

“…As individuals come to understand the differences that form the basis for categorization, they can start to interpret their own actions through the lens of the category they are assigned to, in turn influencing their future decisions, a process that philosopher Ian Hacking dubbed the "looping effect" [45]. For example, when individuals are made more acutely aware of what factors lead to them being perceived as "a woman" or as "queer, " they are incentivized to change their behavior either to increase the likelihood of their preferred classification or to simply live in a way they may now see as more aligned with their identity [32]. Though this type of risk is not likely to be the most salient when collecting demographic data only to assess unequal outcomes or treatment, it is important to be mindful of when asking users for their demographics on platforms with content recommendations that are increasingly tailored to users based on the other information they provide, such as YouTube and TikTok.…”

Section: Miscategorization and Identity Misrepresentationmentioning

confidence: 99%

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Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness

Andrus¹,

Villeneuve²

2022

Preprint

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Most proposed algorithmic fairness techniques require access to demographic data in order to make performance comparisons and standardizations across groups, however this data is largely unavailable in practice, hindering the widespread adoption of algorithmic fairness. Through this paper, we consider calls to collect more data on demographics to enable algorithmic fairness and challenge the notion that discrimination can be overcome with smart enough technical methods and sufficient data. We show how these techniques largely ignore broader questions of data governance and systemic oppression when categorizing individuals for the purpose of fairer algorithmic processing. In this work, we explore under what conditions demographic data should be collected and used to enable algorithmic fairness methods by characterizing a range of social risks to individuals and communities. For the risks to individuals we consider the unique privacy risks of sensitive attributes, the possible harms of miscategorization and misrepresentation, and the use of sensitive data beyond data subjects' expectations. Looking more broadly, the risks to entire groups and communities include the expansion of surveillance infrastructure in the name of fairness, misrepresenting and mischaracterizing what it means to be part of a demographic group, and ceding the ability to define what constitutes biased or unfair treatment. We argue that, by confronting these questions before and during the collection of demographic data, algorithmic fairness methods are more likely to actually mitigate harmful treatment disparities without reinforcing systems of oppression. Towards this end, we assess privacy-focused methods of data collection and use and participatory data governance structures as proposals for more responsibly collecting demographic data.CCS Concepts: • Security and privacy → Social aspects of security and privacy; Privacy protections; Economics of security and privacy; • Social and professional topics → User characteristics; Gender; Sexual orientation.

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Section: Defining Demographic Datamentioning

confidence: 99%

Section: Miscategorization and Identity Misrepresentationmentioning

confidence: 99%

Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness

Andrus¹,

Villeneuve²

2022

Preprint

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“…Recognizing the phenomenon of ontic injustice helps to open up a different avenue: instead of accepting the metaphysical constraint, we should acknowledge the possibility that gender kinds at present are set up in ways that exclude some trans people from membership in the gender kind with which they identify (and include them in other gender kinds) and that this social construction is itself wrongful (Dembroff [forthcoming] adopts a similar stance on cases such as this). For example, in many contexts at present, genital status is treated as determining people's gender for social purposes, meaning that some trans people are prevented from navigating gendered social spaces in a way that is congruent with their gender identity (Bettcher 2007).…”

Section: The Theoretical Value Of Ontic Injusticementioning

confidence: 99%

“…In some cases of ontic injustice, the set of constraints and enablements that (partly) constitute kind membership impose limitations that are sufficiently systematic, significant and pervasive to qualify as oppressive. I use the term ontic oppression to describe this type of ontic injustice (see also Dembroff [forthcoming] for exploration of a similar idea). Ontic oppression should be understood as a subspecies of ontic injustice: not every case of ontic injustice is an instance of ontic oppression, for some cases of ontic injustice will feature only trivial limitations in a very narrow domain, which cannot be said to amount to oppression.…”

Section: The Theoretical Value Of Ontic Injusticementioning

confidence: 99%

Ontic Injustice

Jenkins¹

2020

J. of the Am. Philos. Assoc.

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In this article, I identify a distinctive form of injustice—ontic injustice—in which an individual is wronged by the very fact of being socially constructed as a member of a certain social kind. To be a member of a certain social kind is, at least in part, to be subject to certain social constraints and enablements, and these constraints and enablements can be wrongful to the individual who is subjected to them, in the sense that they inflict a moral injury. The concept of ontic injustice is valuable in three main ways: (1) it draws our attention to the role played by social kinds in enacting wrongful constraints and enablements; (2) it clarifies our options for developing accounts of the ontology of particular social kinds, such as gender kinds; and (3), along with the related concept of ‘ontic oppression’, it helps us to understand and respond to oppression.

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“…2323On this point, see also Dembroff (Forthcoming) and Laskowski (2020). Dembroff argues that facts about gender kinds in contexts of oppression should not settle gender classification practices; Laskowski criticizes attempts to make the semantics of gender terms line up with moral facts about how we ought to treat each other.…”

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confidence: 99%

The gender puzzles

Fileva

2020

European J of Philosophy

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What is gender and how do we know what our gender is? These are the questions I propose to answer here. I review and reject several hypotheses: gender as sex or—a more careful version of the view—as subjective experiences that arise from sexual characteristics; gender as brain configuration; and gender as a historical kind. I express sympathy with an existentialist conception of gender but argue that such a conception, even according to its proponents, cannot help solve the problems of what gender is and how we know what our gender is. I then advance a new view.

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Real Talk on the Metaphysics of Gender

Cited by 50 publications

References 23 publications

Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness

Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness

Ontic Injustice

The gender puzzles

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