- Submit
-
Browse
- All Categories
- Metaphysics and Epistemology
- Value Theory
- Science, Logic, and Mathematics
- Science, Logic, and Mathematics
- Logic and Philosophy of Logic
- Philosophy of Biology
- Philosophy of Cognitive Science
- Philosophy of Computing and Information
- Philosophy of Mathematics
- Philosophy of Physical Science
- Philosophy of Social Science
- Philosophy of Probability
- General Philosophy of Science
- Philosophy of Science, Misc
- History of Western Philosophy
- Philosophical Traditions
- Philosophy, Misc
- Other Academic Areas
- More
Switch to: References
Citations of:
Add citations
You must login to add citations.
|
|
|
This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to explain (...) |
|
|
Philosophy Compass, Volume 17, Issue 6, June 2022. |
|
|
We argue that the trend toward providing users with feasible and actionable explanations of AI decisions—known as recourse explanations—comes with ethical downsides. Specifically, we argue that recourse explanations face several conceptual pitfalls and can lead to problematic explanation hacking, which undermines their ethical status. As an alternative, we advocate that explanations of AI decisions should aim at understanding. |
|
|
This paper develops an account of how the implementation of ML models into healthcare settings requires revising the methodological apparatus of philosophical bioethics. On this account, ML models are cognitive interventions that provide decision-support to physicians and patients. Due to reliability issues, opaque reasoning processes, and information asymmetries, ML models pose inferential problems for them. These inferential problems lay the grounds for many ethical problems that currently claim centre-stage in the bioethical debate. Accordingly, this paper argues that the best way (...) |
|
|
|
|
|
This paper explores a neglected normative dimension of algorithmic opacity in the workplace and the labor market. It argues that explanations of algorithms and algorithmic decisions are of noninstrumental value. That is because explanations of the structure and function of parts of the social world form the basis for reflective clarification of our practical orientation toward the institutions that play a central role in our life. Using this account of the noninstrumental value of explanations, the paper diagnoses distinctive normative defects (...) |
|
|
1. Virginia Eubanks (2018, Chapter 4) tells the story of Pat Gordan, an intake screener in the Department of Human Services in Allegheny County, Pennsylvania. The Department deploys a risk assessme... |
|
|
Mobility robots may soon be among us, triggering a need for safety regulation. Robot safety regulation, however, remains underexplored, with only a few articles analyzing what regulatory approaches could be feasible. This article offers an account of the available regulatory strategies and attempts to theorize the effects of simulation-based safety regulation. The article first discusses the distinctive features of mobility robots as regulatory targets and argues that emergent behavior constitutes the key regulatory concern in designing robot safety regulation regimes. In (...) |
|
|
Recent algorithmic technologies have challenged law’s anthropocentric assumptions. In this article, we develop a set of theoretical tools drawn from new materialisms and the philosophy of information to unravel the complex intra-actions between law and computer code. Accordingly, we first propose a framework for understanding the enmeshing of law and code based on a diffractive reading of Barad’s agential realism and Simondon’s theory of information. We argue that once law and code are understood as material entities that intra-act through in-formation, (...) |
|
|
Criminal justice systems have traditionally relied heavily on human decision-making, but new technologies are increasingly supplementing the human role in this sector. This paper considers what general limits need to be placed on the use of algorithms in sentencing decisions. It argues that, even once we can build algorithms that equal human decision-making capacities, strict constraints need to be placed on how they are designed and developed. The act of condemnation is a valuable element of criminal sentencing, and using algorithms (...) |
|
|
In response to widespread use of automated decision-making technology, some have considered a right to explanation. In this paper I draw on insights from philosophical work on explanation to present a series of challenges to this idea, showing that the normative motivations for access to such explanations ask for something difficult, if not impossible, to extract from automated systems. I consider an alternative, outcomes-focused approach to the normative evaluation of automated decision-making, and recommend it as a way to pursue the (...) |
|
|
Social media data hold considerable potential for predicting health-related conditions. Recent studies suggest that machine-learning models may accurately predict depression and other mental health-related conditions based on Instagram photos and Tweets. In this article, it is argued that individuals should have a sui generis right not to be subjected to AI profiling based on publicly available data without their explicit informed consent. The article (1) develops three basic arguments for a right to protection of personal data trading on the notions (...) |
|
|
Public debates on linguistic integration as a socially desired outcome often share a prevailing sentiment that newcomers ought to “learn the language.” But the intensity of that sentiment is rarely accompanied by an equally robust understanding of what, precisely, it means in practice. This results in a notion of linguistic integration with an inbuilt tension between a seemingly pragmatic and commonsensical appearance, on the one hand, and a minimal action‐guidance capacity, on the other hand. This paper explores this intriguing tension, (...) |
|
|
As machine learning informs increasingly consequential decisions, different metrics have been proposed for measuring algorithmic bias or unfairness. Two popular “fairness measures” are calibration and equality of false positive rate. Each measure seems intuitively important, but notably, it is usually impossible to satisfy both measures. For this reason, a large literature in machine learning speaks of a “fairness tradeoff” between these two measures. This framing assumes that both measures are, in fact, capturing something important. To date, philosophers have seldom examined (...) |
|
|
The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts (...) |
|
|
Many AI systems that make important decisions are black boxes: how they function is opaque even to their developers. This is due to their high complexity and to the fact that they are trained rather than programmed. Efforts to alleviate the opacity of black box systems are typically discussed in terms of transparency, interpretability, and explainability. However, there is little agreement about what these key concepts mean, which makes it difficult to adjudicate the success or promise of opacity alleviation methods. (...) |
|
|
This paper begins with the thought that there is something out of place about offloading inquiry into one's own mind to AI. The paper's primary goal is to articulate the unease felt when considering cases of doing so. It draws a parallel between the use of algorithms in the criminal law: in both cases one feels entitled to be treated as an exception to a verdict made on the basis of a certain kind of evidence. Then it identifies an account (...) |
|
|
The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a detailed investigation of what we will call (...) |
|
|
|
|
|
|
PhilPapers logo by Andrea Andrews and Meghan Driscoll.
This site uses cookies and Google Analytics (see our terms & conditions for details regarding the privacy implications). Use of this site is subject to terms & conditions.
All rights reserved by The PhilPapers Foundation
Server: philpapers-web-64dd777598-g9b9l uwo