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Chapman, Robert, Carel, Havi. Neurodiversity, epistemic injustice, and the good human life
2022, Journal of Social Philosophy
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Added by: Simon Fokt, Contributed by: Alan Walter Jurgens
Abstract:

Autism has typically been framed as inherently harmful and at odds with both subjective happiness and objective flourishing. In recent decades, however, the view of autism as inherently harmful has been challenged by neurodiversity proponents, who draw on social and relational models of disability to reframe the harm autistic people face as arising out of the interaction between being autistic and disabling environments. Here we build on the neurodiversity perspective by arguing that autistic thriving has been rendered both invisible and unthinkable by interlocking forms of testimonial and hermeneutical injustice. On the view we propose, rather than autism being at odds with the possibility of living a good life as such, We argue that our mainstream conceptions of the good life have excluded autistic manifestations of happiness and flourishing. This leads to an epistemic catch-22-like paradoxical situation whereby one can be recognised as autistic or as thriving, but not both. We then propose four ameliorative strategies that support moving towards broader conceptions of the good human life which will allow us to recognise not just autistic, but also other neurodivergent ways, of living a good human life.

Comment: Provides an overview of epistemic injustice faced by neurodivergent individuals both in their daily lives, but also in research done on neurodiversity. Also discusses issues with the medical model of medical and psychiatric diagnoses.
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Gendler, Tamar Szabó. On the Epistemic Costs of Implicit Bias
2011, Philosophical Studies 156 (1): 33-63.
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Added by: Jie Gao
Summary: Tamar Gendler argues that, for those living in a society in which race is a salient sociological feature, it is impossible to be fully rational: members of such a society must either fail to encode relevant information containing race, or suffer epistemic costs by being implicitly racist.
Comment: In this paper, Gendler argues that there is an epistemic costs for being racists. It is a useful material for teachings on philosophy of bias, social psychology, epistemology and etc. Note that there are two nice comments on this paper: one is Andy Egan (2011) "Comments on Gendler's 'the epistemic costs of implicit bias', the other is Joshua Mugg (2011) "What are the cognitive costs of racism? a reply to Gendler". Those two papers can be used togehter with Gendler's paper in increasing a dynamic of debate.
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Haslanger, Sally. Changing the Ideology and Culture of Philosophy: Not by Reason (Alone)
2007, Hypatia, 23 (2): 210–23.
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Added by: Rebecca Buxton
Abstract: There is a deep well of rage inside of me. Rage about how I as an individual have been treated in philosophy; rage about how others I know have been treated; and rage about the conditions that I'm sure affect many women and minorities in philosophy, and have caused many others to leave. Most of the time I suppress this rage and keep it sealed away. Until I came to MIT in 1998, I was in a constant dialogue with myself about whether to quit philosophy, even give up tenure, to do something else. In spite of my deep love for philosophy, it just didn't seem worth it. And I am one of the very lucky ones, one of the ones who has been successful by the dominant standards of the profession. Whatever the numbers say about women and minorities in philosophy, numbers don't begin to tell the story. Things may be getting better in some contexts, but they are far from acceptable.
Comment (from this Blueprint): In her 2007 paper, Haslanger sets out the situation of women in philosophy with a particular focus on instutional academic settings. This paper discusses how women are excluded from philosophy (both contemporary and historical) as well as thinking about disciplnary boundaries: why is it that feminist philosophy is not often thought of as 'real' philosophy?
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Holroyd, Jules. Responsibility for Implicit Bias
2012, Journal of Social Philosophy 43(3): 274-306.
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Added by: Emily Paul
Introduction: Philosophers who have written about implicit bias have claimed or implied that individuals are not responsible, and therefore not blameworthy, for their implicit biases, and that this is a function of the nature of implicit bias as implicit: below the radar of conscious reflection, out of the control of the deliberating agent, and not rationally revisable in the way many of our reflective beliefs are. I argue that close attention to the findings of empirical psychology, and to the conditions for blameworthiness, does not support these claims. I suggest that the arguments for the claim that individuals are not liable for blame are invalid, and that there is some reason to suppose that individuals are, at least sometimes, liable to blame for the extent to which they are influenced in behaviour and judgment by implicit biases. I also argue against the claim that it is counter-productive to see bias as something for which individuals are blameworthy; rather, understanding implicit bias as something for which we are liable to blame could be constructive.
Comment: A great paper for a feminist philosophy, critical race theory, moral philosophy, applied ethics course or similar. Holroyd lays out 4 different arguments that we might NOT be blameworthy for harbouring implicit biases in premise-conclusion form, before arguing that they are invalid. Could e.g. break students into groups and ask each group to discuss a different argument and Holroyd's treatment of it.
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Holroyd, Jules, Robin Scaife, Tom Stafford. What is Implicit Bias?
2017, Philosophy Compass 12(10).
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Added by: Emily Paul
Abstract: Research programs in empirical psychology over the past few decades have led scholars to posit implicit biases. This is due to the development of innovative behavioural measures that have revealed aspects of our cognitions which may not be identified on self?report measures requiring individuals to reflect on and report their attitudes and beliefs. But what does it mean to characterise such biases as implicit? Can we satisfactorily articulate the grounds for identifying them as bias? And crucially, what sorts of cognitions are in fact being measured; what mental states or processes underpin such behavioural responses? In this paper, we outline some of the philosophical and empirical issues engaged when attempting to address these three questions. Our aim is to provide a constructive taxonomy of the issues, and how they interrelate. As we will see, any view about what implicit bias is may depend on a range of prior theoretical choices.
Comment: Perfect for the start of a unit/course on implicit bias, as this paper provides a clear overview of the phenomenon of implicit bias, the evidence for it, and ways to interpret it.
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Narayanan, Arvind. The Limits of the Quantitative Approach to Discrimination
2022, James Baldwin Lecture Series
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Added by: Tomasz Zyglewicz, Shannon Brick, Michael Greer
Introduction: Let’s set the stage. In 2016, ProPublica released a ground-breaking investigation called Machine Bias. You’ve probably heard of it. They examined a criminal risk prediction tool that’s used across the country. These are tools that claim to predict the likelihood that a defendant will reoffend if released, and they are used to inform bail and parole decisions.
Comment (from this Blueprint): This is a written transcript of the James Baldwin lecture, delivered by the computer scientist Arvind Narayanan, at Princeton in 2022. Narayanan's prior research has examined algorithmic bias and standards of fairness with respect to algorithmic decision making. Here, he engages critically with his own discipline, suggesting that there are serious limits to the sorts of quantitative methods that computer scientists recruit to investigate the potential biases in their own tools. Narayanan acknowledges that in voicing this critique, he is echoing claims by feminist researchers from fields beyond computer science. However, his own arguments, centered as they are on the details of the quantitative methods he is at home with, home in on exactly why these prior criticisms hold up in a way that seeks to speak more persuasively to Narayanan's own peers in computer science and other quantitative fields.
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