15 found
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  1. Algorithmic bias: Senses, sources, solutions.Sina Fazelpour & David Danks - 2021 - Philosophy Compass 16 (8):e12760.
    Data‐driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so‐called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm‐based decision‐making. This paper aims to facilitate the application of philosophical analysis to these contested issues by providing an overview of three (...)
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  2. Diversity, Trust, and Conformity: A Simulation Study.Sina Fazelpour & Daniel Steel - 2022 - Philosophy of Science 89 (2):209-231.
    Previous simulation models have found positive effects of cognitive diversity on group performance, but have not explored effects of diversity in demographics (e.g., gender, ethnicity). In this paper, we present an agent-based model that captures two empirically supported hypotheses about how demographic diversity can improve group performance. The results of our simulations suggest that, even when social identities are not associated with distinctive task-related cognitive resources, demographic diversity can, in certain circumstances, benefit collective performance by counteracting two types of conformity (...)
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  3. 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 a (...)
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    Affect-biased attention and predictive processing.Madeleine Ransom, Sina Fazelpour, Jelena Markovic, James Kryklywy, Evan T. Thompson & Rebecca M. Todd - 2020 - Cognition 203 (C):104370.
    In this paper we argue that predictive processing (PP) theory cannot account for the phenomenon of affect-biased attention prioritized attention to stimuli that are affectively salient because of their associations with reward or punishment. Specifically, the PP hypothesis that selective attention can be analyzed in terms of the optimization of precision expectations cannot accommodate affect-biased attention; affectively salient stimuli can capture our attention even when precision expectations are low. We review the prospects of three recent attempts to accommodate affect with (...)
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  5. Algorithmic Fairness and the Situated Dynamics of Justice.Sina Fazelpour, Zachary C. Lipton & David Danks - 2022 - Canadian Journal of Philosophy 52 (1):44-60.
    Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. We propose a framework that takes dynamic trajectories as direct objects of moral appraisal, highlighting three respects in which (...)
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    Information elaboration and epistemic effects of diversity.Daniel Steel, Sina Fazelpour, Bianca Crewe & Kinley Gillette - 2019 - Synthese 198 (2):1287-1307.
    We suggest that philosophical accounts of epistemic effects of diversity have given insufficient attention to the relationship between demographic diversity and information elaboration, the process whereby knowledge dispersed in a group is elicited and examined. We propose an analysis of IE that clarifies hypotheses proposed in the empirical literature and their relationship to philosophical accounts of diversity effects. Philosophical accounts have largely overlooked the possibility that demographic diversity may improve group performance by enhancing IE, and sometimes fail to explore the (...)
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  7. Attention in the Predictive Mind.Madeleine Ransom, Sina Fazelpour & Christopher Mole - 2017 - Consciousness and Cognition 47:99-112.
    It has recently become popular to suggest that cognition can be explained as a process of Bayesian prediction error minimization. Some advocates of this view propose that attention should be understood as the optimization of expected precisions in the prediction-error signal (Clark, 2013, 2016; Feldman & Friston, 2010; Hohwy, 2012, 2013). This proposal successfully accounts for several attention-related phenomena. We claim that it cannot account for all of them, since there are certain forms of voluntary attention that it cannot accommodate. (...)
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  8.  73
    Multiple diversity concepts and their ethical-epistemic implications.Daniel Steel, Sina Fazelpour, Kinley Gillette, Bianca Crewe & Michael Burgess - 2018 - European Journal for Philosophy of Science 8 (3):761-780.
    A concept of diversity is an understanding of what makes a group diverse that may be applicable in a variety of contexts. We distinguish three diversity concepts, show that each can be found in discussions of diversity in science, and explain how they tend to be associated with distinct epistemic and ethical rationales. Yet philosophical literature on diversity among scientists has given little attention to distinct concepts of diversity. This is significant because the unappreciated existence of multiple diversity concepts can (...)
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  9. Fair machine learning under partial compliance.Jessica Dai, Sina Fazelpour & Zachary Lipton - 2021 - In Jessica Dai, Sina Fazelpour & Zachary Lipton (eds.), Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. pp. 55–65.
    Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the allocation outcomes? (...)
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  10.  22
    Diversity in sociotechnical machine learning systems.Maria De-Arteaga & Sina Fazelpour - 2022 - Big Data and Society 9 (1).
    There has been a surge of recent interest in sociocultural diversity in machine learning research. Currently, however, there is a gap between discussions of measures and benefits of diversity in machine learning, on the one hand, and the broader research on the underlying concepts of diversity and the precise mechanisms of its functional benefits, on the other. This gap is problematic because diversity is not a monolithic concept. Rather, different concepts of diversity are based on distinct rationales that should inform (...)
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  11.  22
    Diversity and homophily in social networks.Sina Fazelpour & Hannah Rubin - unknown
    Diversity of social identities can improve the performance of groups through varied cognitive and communicative pathways. Recently, research efforts have focused on identifying when we should expect to see these potential benefits in real-world settings. While most research to date has studied this topic at individual and interpersonal levels, in this paper, we develop an agent-based model to explore how various aspects of homophily, the tendency of individuals to associate with similar others, affects performance at a larger scale. Study 1 (...)
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  12.  66
    Norms in Counterfactual Selection.Sina Fazelpour - 2021 - Philosophy and Phenomenological Research 103 (1):114-139.
    In the hopes of finding supporting evidence for various accounts of actual causation, many philosophers have recently turned to psychological findings about the influence of norms on counterfactual cognition. Surprisingly little philosophical attention has been paid, however, to the question of why considerations of normality should be relevant to counterfactual cognition to begin with. In this paper, I follow two aims. First, against the methodology of two prominent psychological accounts, I argue for a functional approach to understanding the selectivity of (...)
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  13. The Many Faces of Attention: why precision optimization is not attention.Madeleine Ransom & Sina Fazelpour - 2020 - In Dina Mendonça, Manuel Curado & Steven S. Gouveia (eds.), The Philosophy and Science of Predictive Processing. New York, NY: Bloomsbury Publishing. pp. 119-139.
    The predictive coding (PC) theory of attention identifies attention with the optimization of the precision weighting of prediction error. Here we provide some challenges for this identification. On the one hand, the precision weighting of prediction error is too broad a phenomenon to be identified with attention because such weighting plays a central role in multimodal integration. Cases of crossmodal illusions such as the rubber hand illusion and the McGurk effect involve the differential precision weighting of prediction error, yet attention (...)
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  14. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society.Jessica Dai, Sina Fazelpour & Zachary Lipton (eds.) - 2021
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  15.  12
    Correction to: Multiple diversity concepts and their ethical-epistemic implications.Daniel Steel, Sina Fazelpour, Kinley Gillette, Bianca Crewe & Michael Burgess - 2018 - European Journal for Philosophy of Science 8 (3):781-781.
    The original version of this article unfortunately contained a mistake. The fourth author’s name is Bianca Crewe, not Bianca Crew. The original article has been corrected.
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