Results for ' Accuracy-Fairness'

981 found
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  1. “Just” accuracy? Procedural fairness demands explainability in AI‑based medical resource allocation.Jon Rueda, Janet Delgado Rodríguez, Iris Parra Jounou, Joaquín Hortal-Carmona, Txetxu Ausín & David Rodríguez-Arias - 2022 - AI and Society:1-12.
    The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because (...)
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  2.  71
    Diving into Fair Pools: Algorithmic Fairness, Ensemble Forecasting, and the Wisdom of Crowds.Rush T. Stewart & Lee Elkin - forthcoming - Analysis.
    Is the pool of fair predictive algorithms fair? It depends, naturally, on both the criteria of fairness and on how we pool. We catalog the relevant facts for some of the most prominent statistical criteria of algorithmic fairness and the dominant approaches to pooling forecasts: linear, geometric, and multiplicative. Only linear pooling, a format at the heart of ensemble methods, preserves any of the central criteria we consider. Drawing on work in the social sciences and social epistemology on (...)
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  3. Adversarial Sampling for Fairness Testing in Deep Neural Network.Tosin Ige, William Marfo, Justin Tonkinson, Sikiru Adewale & Bolanle Hafiz Matti - 2023 - International Journal of Advanced Computer Science and Applications 14 (2).
    In this research, we focus on the usage of adversarial sampling to test for the fairness in the prediction of deep neural network model across different classes of image in a given dataset. While several framework had been proposed to ensure robustness of machine learning model against adversarial attack, some of which includes adversarial training algorithm. There is still the pitfall that adversarial training algorithm tends to cause disparity in accuracy and robustness among different group. Our research is (...)
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  4. Disagreement, Drugs, etc.: from Accuracy to Akrasia.David Christensen - 2016 - Episteme 13 (4):397-422.
    We often get evidence concerning the reliability of our own thinking about some particular matter. This “higher-order evidence” can come from the disagreement of others, or from information about our being subject to the effects of drugs, fatigue, emotional ties, implicit biases, etc. This paper examines some pros and cons of two fairly general models for accommodating higher-order evidence. The one that currently seems most promising also turns out to have the consequence that epistemic akrasia should occur more frequently than (...)
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  5. Informational richness and its impact on algorithmic fairness.Marcello Di Bello & Ruobin Gong - 2025 - Philosophical Studies 182 (1):25-53.
    The literature on algorithmic fairness has examined exogenous sources of biases such as shortcomings in the data and structural injustices in society. It has also examined internal sources of bias as evidenced by a number of impossibility theorems showing that no algorithm can concurrently satisfy multiple criteria of fairness. This paper contributes to the literature stemming from the impossibility theorems by examining how informational richness affects the accuracy and fairness of predictive algorithms. With the aid of (...)
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  6. A Fairness Doctrine for the Twenty-First Century.Julian Friedland - 2021 - Areo.
    Michael Goldhaber, who popularized the term the attention economy, said of the US Capitol insurrection: “It felt like an expression of a world in which everyone is desperately seeking their own audience and fracturing reality in the process. I only see that accelerating.” If we don’t do something about this, American democracy may not survive. For when there is no longer any common ground of evidence and reason, history shows that misinformation will eventually overwhelm public discourse and authoritarianism can take (...)
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  7.  29
    Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set.Gordon Dai, Pavan Ravishankar, Rachel Yuan, Daniel B. Neill & Emily Black - manuscript
    When selecting a model from a set of equally performant models, how much unfairness can you really reduce? Is it important to be intentional about fairness when choosing among this set, or is arbitrarily choosing among the set of “good” models good enough? Recent work has highlighted that the phenomenon of model multiplicity—where multiple models with nearly identical predictive accuracy exist for the same task—has both positive and negative implications for fairness, from strengthening the enforcement of civil (...)
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  8. Disciplining Deliberation: A Sociotechnical Perspective on Machine Learning Trade-offs.Sina Fazelpour - forthcoming - British Journal for the Philosophy of Science.
    This paper examines two prominent formal trade-offs in artificial intelligence (AI)---between predictive accuracy and fairness, and between predictive accuracy and interpretability. These trade-offs have become a central focus in normative and regulatory discussions as policymakers seek to understand the value tensions that can arise in the social adoption of AI tools. The prevailing interpretation views these formal trade-offs as directly corresponding to tensions between underlying social values, implying unavoidable conflicts between those social objectives. In this paper, I (...)
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  9. Decision Time: Normative Dimensions of Algorithmic Speed.Daniel Susser - forthcoming - ACM Conference on Fairness, Accountability, and Transparency (FAccT '22).
    Existing discussions about automated decision-making focus primarily on its inputs and outputs, raising questions about data collection and privacy on one hand and accuracy and fairness on the other. Less attention has been devoted to critically examining the temporality of decision-making processes—the speed at which automated decisions are reached. In this paper, I identify four dimensions of algorithmic speed that merit closer analysis. Duration (how much time it takes to reach a judgment), timing (when automated systems intervene in (...)
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  10. Just Machines.Clinton Castro - 2022 - Public Affairs Quarterly 36 (2):163-183.
    A number of findings in the field of machine learning have given rise to questions about what it means for automated scoring- or decisionmaking systems to be fair. One center of gravity in this discussion is whether such systems ought to satisfy classification parity (which requires parity in accuracy across groups, defined by protected attributes) or calibration (which requires similar predictions to have similar meanings across groups, defined by protected attributes). Central to this discussion are impossibility results, owed to (...)
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  11.  45
    Disciplining Deliberation: A Socio-technical Perspective on Machine Learning Trade-Offs.Sina Fazelpour - forthcoming - British Journal for the Philosophy of Science.
    This paper examines two prominent formal trade-offs in artificial intelligence (AI)---between predictive accuracy and fairness, and between predictive accuracy and interpretability. These trade-offs have become a central focus in normative and regulatory discussions as policymakers seek to understand the value tensions that can arise in the social adoption of AI tools. The prevailing interpretation views these formal trade-offs as directly corresponding to tensions between underlying social values, implying unavoidable conflicts between those social objectives. In this paper, I (...)
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  12. Performance vs. competence in human–machine comparisons.Chaz Firestone - 2020 - Proceedings of the National Academy of Sciences 41.
    Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach “human-level” accuracy in an astounding variety of domains, and even predict human brain activity—raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines fail in strange and “unhumanlike” ways, threatening their status as models of our minds. How can we know when human–machine behavioral differences reflect deep disparities in their underlying capacities, vs. when such (...)
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  13. Machine learning in bail decisions and judges’ trustworthiness.Alexis Morin-Martel - 2023 - AI and Society:1-12.
    The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a (...)
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  14. Procedural justice.Lawrence B. Solum - 2004 - Southern California Law Review 78:181.
    "Procedural Justice" offers a theory of procedural fairness for civil dispute resolution. The core idea behind the theory is the procedural legitimacy thesis: participation rights are essential for the legitimacy of adjudicatory procedures. The theory yields two principles of procedural justice: the accuracy principle and the participation principle. The two principles require a system of procedure to aim at accuracy and to afford reasonable rights of participation qualified by a practicability constraint. The Article begins in Part I, (...)
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  15. Synthetic Health Data: Real Ethical Promise and Peril.Daniel Susser, Daniel S. Schiff, Sara Gerke, Laura Y. Cabrera, I. Glenn Cohen, Megan Doerr, Jordan Harrod, Kristin Kostick-Quenet, Jasmine McNealy, Michelle N. Meyer, W. Nicholson Price & Jennifer K. Wagner - 2024 - Hastings Center Report 54 (5):8-13.
    Researchers and practitioners are increasingly using machine‐generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while—potentially—mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic data is (...)
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  16. Conflicting Aims and Values in the Application of Smart Sensors in Geriatric Rehabilitation: Ethical Analysis.Christopher Predel, Cristian Timmermann, Frank Ursin, Marcin Orzechowski, Timo Ropinski & Florian Steger - 2022 - JMIR mHealth and uHealth 10 (6):e32910.
    Background: Smart sensors have been developed as diagnostic tools for rehabilitation to cover an increasing number of geriatric patients. They promise to enable an objective assessment of complex movement patterns. -/- Objective: This research aimed to identify and analyze the conflicting ethical values associated with smart sensors in geriatric rehabilitation and provide ethical guidance on the best use of smart sensors to all stakeholders, including technology developers, health professionals, patients, and health authorities. -/- Methods: On the basis of a systematic (...)
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  17. Implications and Applications of Artificial Intelligence in the Legal Domain.Besan S. Abu Nasser, Marwan M. Saleh & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 7 (12):18-25.
    Abstract: As the integration of Artificial Intelligence (AI) continues to permeate various sectors, the legal domain stands on the cusp of a transformative era. This research paper delves into the multifaceted relationship between AI and the law, scrutinizing the profound implications and innovative applications that emerge at the intersection of these two realms. The study commences with an examination of the current landscape, assessing the challenges and opportunities that AI presents within legal frameworks. With an emphasis on efficiency, accuracy, (...)
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  18. Ethical Concerns in Computational Linguistic Field National Defense: A Philosophical Investigation of Language and Security.Mhd Halkis Malkis4 - 2024 - Linguistic and Philosophical Investigations 23 (1):386–396.
    This research examines ethical issues in computational linguistics that can be applied to national defense by analyzing philosophical and security language. The increasing use of language contexts, such as intelligence and communication data analysis, raises ethical and philosophical challenges related to privacy, control, and accuracy. This research aims to identify and analyze ethical issues, especially in the use of computational linguistics in defense applications, as well as their implications for the protection of individual rights and privacy. This method involves (...)
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  19. Two Reasons for Subjecting Medical AI Systems to Lower Standards than Humans.Jakob Mainz, Jens Christian Bjerring & Lauritz Munch - 2023 - Acm Proceedings of Fairness, Accountability, and Transaparency (Facct) 2023 1 (1):44-49.
    This paper concerns the double standard debate in the ethics of AI literature. This debate essentially revolves around the question of whether we should subject AI systems to different normative standards than humans. So far, the debate has centered around the desideratum of transparency. That is, the debate has focused on whether AI systems must be more transparent than humans in their decision-making processes in order for it to be morally permissible to use such systems. Some have argued that the (...)
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  20.  18
    The Exact Solution to the Free Will Problem: The Three Universal Laws of Nature.Angelito Malicse - manuscript
    The Exact Solution to the Free Will Problem: The Three Universal Laws of Nature -/- The problem of free will has persisted for thousands of years, captivating philosophers, scientists, theologians, and scholars across generations. The fundamental question has always been whether human beings truly have the freedom to make decisions or whether all actions are predetermined by external forces such as biology, environment, and societal influence. Traditional philosophical and scientific frameworks have struggled to provide a definitive answer. However, this long-standing (...)
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  21. How AI Can Implement the Universal Formula in Education and Leadership Training.Angelito Malicse - manuscript
    How AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in real (...)
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  22. (1 other version)Socrates in the schools: Gains at three-year follow-up.Frank Fair, Lory E. Haas, Carol Gardoski, Daphne Johnson, Debra Price & Olena Leipnik - 2015 - Journal of Philosophy in Schools 2 (2).
    Three recent research reports by Topping and Trickey, by Fair and colleagues, and by Gorard, Siddiqui and Huat See have produced data that support the conclusion that a Philosophy for Children program of one-hour-per-week structured discussions has a marked positive impact on students. This article presents data from a follow up study done three years after the completion of the study reported in Fair et al.. The data show that the positive gains in scores on the Cognitive Abilities Test were (...)
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  23. The Fallacy of Many Questions.Frank Fair - 1973 - Southwestern Journal of Philosophy 4 (1):89-92.
    In this article I explore two accounts of the Fallacy of Many Questions made famous by the question "Have you stopped beating your wife?" The accounts are from the works of Lennart Aqvist and Noel Belnap, and the two authors differ in their accounts of the fallacy. Then I give my own account based on understanding a facet of erotetic logic, i. e., the logic of questions.
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  24.  84
    The joy and significance of serendipity in retail.Faire Faire - 2024 - A Report From Faire.
    The joy and significance of serendipity in retail: A report from Faire.
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  25. Accuracy-First Epistemology Without Additivity.Richard Pettigrew - 2022 - Philosophy of Science 89 (1):128-151.
    Accuracy arguments for the core tenets of Bayesian epistemology differ mainly in the conditions they place on the legitimate ways of measuring the inaccuracy of our credences. The best existing arguments rely on three conditions: Continuity, Additivity, and Strict Propriety. In this paper, I show how to strengthen the arguments based on these conditions by showing that the central mathematical theorem on which each depends goes through without assuming Additivity.
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  26. Repugnant Accuracy.Brian Talbot - 2019 - Noûs 53 (3):540-563.
    Accuracy‐first epistemology is an approach to formal epistemology which takes accuracy to be a measure of epistemic utility and attempts to vindicate norms of epistemic rationality by showing how conformity with them is beneficial. If accuracy‐first epistemology can actually vindicate any epistemic norms, it must adopt a plausible account of epistemic value. Any such account must avoid the epistemic version of Derek Parfit's “repugnant conclusion.” I argue that the only plausible way of doing so is to say (...)
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  27. Accuracy and Credal Imprecision.Dominik Berger & Nilanjan Das - 2019 - Noûs 54 (3):666-703.
    Many have claimed that epistemic rationality sometimes requires us to have imprecise credal states (i.e. credal states representable only by sets of credence functions) rather than precise ones (i.e. credal states representable by single credence functions). Some writers have recently argued that this claim conflicts with accuracy-centered epistemology, i.e., the project of justifying epistemic norms by appealing solely to the overall accuracy of the doxastic states they recommend. But these arguments are far from decisive. In this essay, we (...)
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  28. Accuracy-dominance and conditionalization.Michael Nielsen - 2021 - Philosophical Studies 178 (10):3217-3236.
    Epistemic decision theory produces arguments with both normative and mathematical premises. I begin by arguing that philosophers should care about whether the mathematical premises (1) are true, (2) are strong, and (3) admit simple proofs. I then discuss a theorem that Briggs and Pettigrew (2020) use as a premise in a novel accuracy-dominance argument for conditionalization. I argue that the theorem and its proof can be improved in a number of ways. First, I present a counterexample that shows that (...)
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  29. Lying, accuracy and credence.Matthew A. Benton - 2018 - Analysis 78 (2):195-198.
    Traditional definitions of lying require that a speaker believe that what she asserts is false. Sam Fox Krauss seeks to jettison the traditional belief requirement in favour of a necessary condition given in a credence-accuracy framework, on which the liar expects to impose the risk of increased inaccuracy on the hearer. He argues that this necessary condition importantly captures nearby cases as lies which the traditional view neglects. I argue, however, that Krauss's own account suffers from an identical drawback (...)
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  30. On Accuracy and Coherence with Infinite Opinion Sets.Mikayla Kelley - 2023 - Philosophy of Science 90 (1):92-128.
    There is a well-known equivalence between avoiding accuracy dominance and having probabilistically coherent credences (see, e.g., de Finetti 1974, Joyce 2009, Predd et al. 2009, Pettigrew 2016). However, this equivalence has been established only when the set of propositions on which credence functions are defined is finite. In this paper, I establish connections between accuracy dominance and coherence when credence functions are defined on an infinite set of propositions. In particular, I establish the necessary results to extend the (...)
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  31. The Accuracy and Rationality of Imprecise Credences.Miriam Schoenfield - 2015 - Noûs 51 (4):667-685.
    It has been claimed that, in response to certain kinds of evidence, agents ought to adopt imprecise credences: doxastic states that are represented by sets of credence functions rather than single ones. In this paper I argue that, given some plausible constraints on accuracy measures, accuracy-centered epistemologists must reject the requirement to adopt imprecise credences. I then show that even the claim that imprecise credences are permitted is problematic for accuracy-centered epistemology. It follows that if imprecise credal (...)
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  32. Accuracy Across Doxastic Attitudes: Recent Work on the Accuracy of Belief.Robert Weston Siscoe - 2022 - American Philosophical Quarterly 59 (2):201-217.
    James Joyce's article “A Nonpragmatic Vindication of Probabilism” introduced an approach to arguing for credal norms by appealing to the epistemic value of accuracy. The central thought was that credences ought to accurately represent the world, a guiding thought that has gone on to generate an entire research paradigm on the rationality of credences. Recently, a number of epistemologists have begun to apply this same thought to full beliefs, attempting to explain and argue for norms of belief in terms (...)
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  33. Accuracy Uncomposed: Against Calibrationism.Ben Levinstein - 2017 - Episteme 14 (1):59-69.
    Pettigrew offers new axiomatic constraints on legitimate measures of inaccuracy. His axiom called ‘Decomposition’ stipulates that legitimate measures of inaccuracy evaluate a credence function in part based on its level of calibration at a world. I argue that if calibration is valuable, as Pettigrew claims, then this fact is an explanandum for accuracy-rst epistemologists, not an explanans, for three reasons. First, the intuitive case for the importance of calibration isn’t as strong as Pettigrew believes. Second, calibration is a perniciously (...)
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  34. An Accuracy Based Approach to Higher Order Evidence.Miriam Schoenfield - 2016 - Philosophy and Phenomenological Research 96 (3):690-715.
    The aim of this paper is to apply the accuracy based approach to epistemology to the case of higher order evidence: evidence that bears on the rationality of one's beliefs. I proceed in two stages. First, I show that the accuracy based framework that is standardly used to motivate rational requirements supports steadfastness—a position according to which higher order evidence should have no impact on one's doxastic attitudes towards first order propositions. The argument for this will require a (...)
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  35. Algorithmic Fairness Criteria as Evidence.Will Fleisher - forthcoming - Ergo: An Open Access Journal of Philosophy.
    Statistical fairness criteria are widely used for diagnosing and ameliorating algorithmic bias. However, these fairness criteria are controversial as their use raises several difficult questions. I argue that the major problems for statistical algorithmic fairness criteria stem from an incorrect understanding of their nature. These criteria are primarily used for two purposes: first, evaluating AI systems for bias, and second constraining machine learning optimization problems in order to ameliorate such bias. The first purpose typically involves treating each (...)
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  36. Accuracy and Verisimilitude: The Good, the Bad, and the Ugly.Miriam Schoenfield - 2022 - British Journal for the Philosophy of Science 73 (2):373-406.
    It seems like we care about at least two features of our credence function: gradational-accuracy and verisimilitude. Accuracy-first epistemology requires that we care about one feature of our credence function: gradational-accuracy. So if you want to be a verisimilitude-valuing accuracy-firster, you must be able to think of the value of verisimilitude as somehow built into the value of gradational-accuracy. Can this be done? In a recent article, Oddie has argued that it cannot, at least if (...)
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  37. Bridging Rationality and Accuracy.Miriam Schoenfield - 2015 - Journal of Philosophy 112 (12):633-657.
    This paper is about the connection between rationality and accuracy. I show that one natural picture about how rationality and accuracy are connected emerges if we assume that rational agents are rationally omniscient. I then develop an alternative picture that allows us to relax this assumption, in order to accommodate certain views about higher order evidence.
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  38. (1 other version)The accuracy-coherence tradeoff in cognition.David Thorstad - forthcoming - British Journal for Philosophy of Science.
    I argue that bounded agents face a systematic accuracy-coherence tradeoff in cognition. Agents must choose whether to structure their cognition in ways likely to promote coherence or accuracy. I illustrate the accuracy-coherence tradeoff by showing how it arises out of at least two component tradeoffs: a coherence-complexity tradeoff between coherence and cognitive complexity, and a coherence-variety tradeoff between coherence and strategic variety. These tradeoffs give rise to an accuracy-coherence tradeoff because privileging coherence over complexity or strategic (...)
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  39. Fairness, Free-Riding and Rainforest Protection.Chris Armstrong - 2016 - Political Theory 44 (1):106-130.
    If dangerous climate change is to be avoided, it is vital that carbon sinks such as tropical rainforests are protected. But protecting them has costs. These include opportunity costs: the potential economic benefits which those who currently control rainforests have to give up when they are protected. But who should bear those costs? Should countries which happen to have rainforests within their territories sacrifice their own economic development, because of our broader global interests in protecting key carbon sinks? This essay (...)
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  40. Accuracy and the Imps.James M. Joyce & Brian Weatherson - 2019 - Logos and Episteme 10 (3):263-282.
    Recently several authors have argued that accuracy-first epistemology ends up licensing problematic epistemic bribes. They charge that it is better, given the accuracy-first approach, to deliberately form one false belief if this will lead to forming many other true beliefs. We argue that this is not a consequence of the accuracy-first view. If one forms one false belief and a number of other true beliefs, then one is committed to many other false propositions, e.g., the conjunction of (...)
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  41. Accuracy and epistemic conservatism.Florian Steinberger - 2018 - Analysis 79 (4):658-669.
    Epistemic utility theory is generally coupled with veritism. Veritism is the view that truth is the sole fundamental epistemic value. Veritism, when paired with EUT, entails a methodological commitment: norms of epistemic rationality are justified only if they can be derived from considerations of accuracy alone. According to EUT, then, believing truly has epistemic value, while believing falsely has epistemic disvalue. This raises the question as to how the rational believer should balance the prospect of true belief against the (...)
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  42. Algorithmic Fairness from a Non-ideal Perspective.Sina Fazelpour & Zachary C. Lipton - 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society.
    Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered (...)
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  43. Accuracy in imagining.Amy Kind - 2024 - Philosophy and the Mind Sciences 5.
    Recent treatments of imagination have increasingly treated imagining as a skill. Insofar as imaginative accuracy is one of the factors that underwrites this skill, it is important to understand what it means to say that an imagining is accurate. This paper takes up that task. The discussion proceeds in four parts. First, I address two worries that may naturally arise about the coherence ofthe notion of imaginative accuracy. Second, with those worries addressed, I turn to an exploration of (...)
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  44. Fair equality of chances for prediction-based decisions.Michele Loi, Anders Herlitz & Hoda Heidari - 2024 - Economics and Philosophy 40 (3):557-580.
    This article presents a fairness principle for evaluating decision-making based on predictions: a decision rule is unfair when the individuals directly impacted by the decisions who are equal with respect to the features that justify inequalities in outcomes do not have the same statistical prospects of being benefited or harmed by them, irrespective of their socially salient morally arbitrary traits. The principle can be used to evaluate prediction-based decision-making from the point of view of a wide range of antecedently (...)
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  45. Playing Fair and Following the Rules.Justin Tosi - 2017 - Journal of Moral Philosophy 14 (2):134-141.
    In his paper “Fairness, Political Obligation, and the Justificatory Gap” (published in the Journal of Moral Philosophy), Jiafeng Zhu argues that the principle of fair play cannot require submission to the rules of a cooperative scheme, and that when such submission is required, the requirement is grounded in consent. I propose a better argument for the claim that fair play requires submission to the rules than the one Zhu considers. I also argue that Zhu’s attribution of consent to people (...)
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  46. Can All-Accuracy Accounts Justify Evidential Norms?Christopher J. G. Meacham - 2018 - In Kristoffer Ahlstrom-Vij & Jeff Dunn (eds.), Epistemic Consequentialism. Oxford: Oxford University Press.
    Some of the most interesting recent work in formal epistemology has focused on developing accuracy-based approaches to justifying Bayesian norms. These approaches are interesting not only because they offer new ways to justify these norms, but because they potentially offer a way to justify all of these norms by appeal to a single, attractive epistemic goal: having accurate beliefs. Recently, Easwaran & Fitelson (2012) have raised worries regarding whether such “all-accuracy” or “purely alethic” approaches can accommodate and justify (...)
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  47. The Fair Chances in Algorithmic Fairness: A Response to Holm.Clinton Castro & Michele Loi - 2023 - Res Publica 29 (2):231–237.
    Holm (2022) argues that a class of algorithmic fairness measures, that he refers to as the ‘performance parity criteria’, can be understood as applications of John Broome’s Fairness Principle. We argue that the performance parity criteria cannot be read this way. This is because in the relevant context, the Fairness Principle requires the equalization of actual individuals’ individual-level chances of obtaining some good (such as an accurate prediction from a predictive system), but the performance parity criteria do (...)
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  48. Lockeans Maximize Expected Accuracy.Kevin Dorst - 2019 - Mind 128 (509):175-211.
    The Lockean Thesis says that you must believe p iff you’re sufficiently confident of it. On some versions, the 'must' asserts a metaphysical connection; on others, it asserts a normative one. On some versions, 'sufficiently confident' refers to a fixed threshold of credence; on others, it varies with proposition and context. Claim: the Lockean Thesis follows from epistemic utility theory—the view that rational requirements are constrained by the norm to promote accuracy. Different versions of this theory generate different versions (...)
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  49. Accuracy and Probabilism in Infinite Domains.Michael Nielsen - 2023 - Mind 132 (526):402-427.
    The best accuracy arguments for probabilism apply only to credence functions with finite domains, that is, credence functions that assign credence to at most finitely many propositions. This is a significant limitation. It reveals that the support for the accuracy-first program in epistemology is a lot weaker than it seems at first glance, and it means that accuracy arguments cannot yet accomplish everything that their competitors, the pragmatic (Dutch book) arguments, can. In this paper, I investigate the (...)
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  50. Fairness and the Strengths of Agents' Claims.Nathaniel Sharadin - 2016 - Utilitas 28 (3):347-360.
    John Broome has proposed a theory of fairness according to which fairness requires that agents’ claims to goods be satisfied in proportion to the relative strength of those claims. In the case of competing claims for a single indivisible good, Broome argues that what fairness requires is the use of a weighted lottery as a surrogate to satisfying the competing claims: the relative chance of each claimant's winning the lottery should be set to the relative strength of (...)
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