The Eye's Mind: Perceptual Process and Epistemic Norms

Munton, Jessie

doi:10.1111/phpe.12105

Cited by 7 publications

(7 citation statements)

References 71 publications

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“…I propose that the notion that perception is inferential in this way-that it relies on antecedent mental states to produce a perceptual state, in a way that accords to a normative rule of inference-is epistemologically relevant (see also Clark, 2018;Ghijsen, 2018;Munton, 2018;Vance, 2015). My overall point can be captured in four interrelated claims.…”

Section: Footnote 2 (Continued)mentioning

confidence: 98%

Perceptual justification in the Bayesian brain: a foundherentist account

Gładziejewski

2021

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In this paper, I use the predictive processing (PP) theory of perception to tackle the question of how perceptual states can be rationally involved in cognition by justifying other mental states. I put forward two claims regarding the epistemological implications of PP. First, perceptual states can confer justification on other mental states because the perceptual states are themselves rationally acquired. Second, despite being inferentially justified rather than epistemically basic, perceptual states can still be epistemically responsive to the mind-independent world. My main goal is to elucidate the epistemology of perception already implicit in PP. But I also hope to show how it is possible to peacefully combine central tenets of foundationalist and coherentist accounts of the rational powers of perception while avoiding the well-recognized pitfalls of either.

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Section: Footnote 2 (Continued)mentioning

confidence: 98%

Perceptual justification in the Bayesian brain: a foundherentist account

Gładziejewski

2021

Synthese

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“…What I have said so far is purposefully noncommittal with respect to the exact architecture of perceptual justification that falls out of PP (for some proposals, see Ghijsen [2018]; Gładziejewski [2017]; Munton [2018]). For present purposes, it suffices to establish that perceptual representations of objects are rationally produced.…”

Section: Paweł Gładziejewskimentioning

confidence: 99%

Un-debunking Ordinary Objects with the Help of Predictive Processing

Gładziejewski¹

2023

The British Journal for the Philosophy of Science

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“…38 See also Douglas 2003Douglas , 2009. Arguments from inductive risk in philosophy of science share important structural similarities with arguments for moral or pragmatic encroachment in epistemology (Stanley 2005, Fantl and Mcgrath 2007, Moss 2018, Basu 2018, Bolinger 2018, Gardiner 2018, and Munton 2017, 2019a. One could thus use this section as a model for how to likewise extend a theory of moral/pragmatic encroachment to algorithmic decision-making.…”

Section: The Argument From Inductive Riskmentioning

confidence: 99%

“… See Stanley 2005, Fantl and Mcgrath 2007, Moss 2018, Basu 2018, Bolinger 2018, Gardiner 2018, and Munton 2017, 2019a SeeRudner 1953, Levi 1960, Douglas 2000, 2009, Rooney 1992, and Longino 1995, among others.6 Here and throughout I use 'machine learning programs', 'algorithmic decision-making', and 'algorithms' to pick out a broad class of automated programs that function by capitalizing on or "learning" from patterns manifest in the data on which they are trained in order to build a predictive model. This includes a wide range of machine learning programs, including supervised, unsupervised, and reinforcement learning programs.…”

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confidence: 99%

Are Algorithms Value-Free?

Johnson

2023

J. Moral Philos.

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As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments from feminist philosophy of science to machine learning programs to make the case that the resources required to respond to these inductive challenges render critical aspects of their design constitutively value-laden. I demonstrate these points specifically in the case of recidivism algorithms, arguing that contemporary debates concerning fairness in criminal justice risk-assessment programs are best understood as iterations of traditional arguments from inductive risk and demarcation, and thereby establish the value-laden nature of automated decision-making programs. Finally, in light of these points, I address opportunities for relocating the value-free ideal in machine learning and the limitations that accompany them.

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The Eye's Mind: Perceptual Process and Epistemic Norms

Cited by 7 publications

References 71 publications

Perceptual justification in the Bayesian brain: a foundherentist account

Perceptual justification in the Bayesian brain: a foundherentist account

Un-debunking Ordinary Objects with the Help of Predictive Processing

Are Algorithms Value-Free?

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