Fazelpour, S. (forthcoming) A Decision-Theoretic Approach to Counterfactual Selection. Philosophy and Phenomenological Research
Abstract: 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 counterfactual cognition. Second, I take some steps towards a systematic analysis by providing a qualitative, decision-theoretic account of one important function of counterfactual thinking, namely, inferring, in the face of undesirable outcomes, corrective policies that prevent the occurrence of similar outcomes in future circumstances. I make a case for employing this analysis by (a) showing its value for assessing the rationality of imagination-driven counterfactual generation, (b) highlighting its use for making sense of practices in history and policy analysis where counterfactual selection plays a central role, and (c) demonstrating its diagnostic value for identifying areas where counterfactual generation may lead us into epistemic and ethical troubles.
Fazelpour, S., Lipton, Z. (2020) Algorithmic Fairness from a Non-ideal Perspective (a version of this work has appeared in the proceedings for AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) 2020.)
Abstract: 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 variety of algorithms in attempts to satisfy subsets of these parities or to trade off the degree to which they are satisfied against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future research. Link.
Steel, D., Fazelpour, S., Crew, B. & Gillette, K. (2019) Information Elaboration and Epistemic Effects of Diversity. Synthese.
Abstract: We suggest that philosophical accounts of epistemic effects of diversity have given insufficient attention to the relationship between demographic diversity and information elaboration (IE), 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 relationship between diversity and IE altogether. We claim these omissions are significant from both a practical and theoretical perspective. Moreover, we explain how the overlooked explanations suggest that epistemic benefits of diversity can depend on epistemically unjust social dynamics. Link.
Steel, D., Fazelpour, S., Gillette, K. Crew, B. & Burgess, M. (2018). Multiple diversity concepts and their ethical-epistemic implications. European Journal for Philosophy of Science.
Abstract: 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 generate unclarity about the meaning of “diversity,” lead to problematic inferences from empirical research, and obscure complex ethical-epistemic questions about how to define diversity in specific cases. We illustrate some ethical-epistemic implications of our proposal by reference to an example of deliberative mini-publics on human tissue biobanking. Link.
Ransom, M., Fazelpour, S., & Mole, C. (2016). Attention in the predictive mind. Consciousness and Cognition.
Abstract: 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 voluntaryattention that it cannot accommodate. We therefore suggest that, although the theory of Bayesian prediction error minimization introduces some powerful tools for the explanation of mental phenomena, its advocates have been wrong to claim that Bayesian prediction error minimization is ‘all the brain ever does’. Link
Fazelpour, S., & Thompson, E. (2015). The Kantian brain: brain dynamics from a neurophenomenological perspective. Current Opinion in Neurobiology.
Abstract: Current research on spontaneous, self-generated brain rhythms and dynamic neural network coordination cast new light on Immanuel Kant’s idea of the ‘spontaneity’ of cognition, that is, the mind’s capacity to organize and synthesize sensory stimuli in novel, unprecedented ways. Nevertheless, determining the precise nature of the brain-cognition mapping remains an outstanding challenge. Neurophenomenology, which uses phenomenological information about the variability of subjective experience in order to illuminate the variability of brain dynamics, offers a promising method for addressing this challenge. Link
Hojjat, S. P., Fazelpour, S., & Shirani, S. (2007). Multiple description coding of video using phase scrambling. In IEEE Pacific Rim Conference on Communications, Computers and Signal Processing.
Abstract: In this paper, we proposed a method to decrease the effects of data loss in communication of a video sequence using phase scrambling. Phase scrambling is used to spread the data of each pixel of a video sequence over all pixels of the scrambled video. In our experiments we studied the effects of different loss patterns. The results obtained by employing this method shows great improvements compared to cases of data loss without exploiting phase scrambling. This technique can be readily used in transmission of video segments over unreliable networks. Link