Sina Fazelpour


I’m a SSHRC Postdoctoral Fellow at Carnegie Mellon University’s Philosophy Department, working with David Danks and Zack Lipton on characterizing and mitigating the potential harms of algorithmic-based decision making. Currently, my research focuses on

  • Fairness and transparency in machine learning based decision support systems
  • Epistemic impact of diversity on group performance

More broadly, I am interested in issues having to do with counterfactuals and causation, explanation, and formal modeling and the application of these to questions in social sciences, cognitive science and artificial intelligence.


I hold a PhD in Philosophy from the University of British Columbia. I was advised by Christopher Mole and Evan Thompson. During my studies at UBC, I was a graduate research fellow at The W. Maurice Young Centre for Applied Ethics, an Instructor in the Cognitive Systems Program, and a UBC Public Scholar. Prior to philosophy, I completed a bachelor’s degree in electrical and biomedical engineering at McMaster University, and a master’s in medical biophysics at the University of Toronto, during which I also worked as a researcher at the department of diagnostic imaging at the SickKids hospital in Toronto. Aside from academic work, I enjoy football (or soccer if you prefer) and literature. Some of my translations and poems have appeared in The Antigonish Review, Frogpond, and Haiku 21: an anthology of contemporary English-language haiku.


  • On July 30, I will be giving a talk at The W. Maurice Young Centre for Applied Ethics titled “Diversity, Trust, Conformity: A Simulation-Based Study of Demographic Diversity’s Epistemic Impact”
  • Between July 21 – 24, 2019, I will be attending The Summer Institute on AI and Society, co-convened by CIFAR, the AI PULSE program at UCLA School of Law, and the Alberta Machine Intelligence Institute in Edmonton.



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


Teaching Awards

In 2017-2018, I was awarded the The Don Brown Graduate Teaching Awards.

Course instructor

2018-2019: COGS 300: Understanding and Designing Cognitive Systems, Cognitive Systems Program, University of British Columbia (Syllabus + Labs)

2017-2018: COGS 300: Understanding and Designing Cognitive Systems, Cognitive Systems Program, University of British Columbia

2016-2017: PHIL 125: Introduction to Scientific Reasoning, Philosophy Department, University of British Columbia

Teaching Assistant

I was a teaching assistant for various courses at different levels, including introduction to philosophy, ethics, symbolic logic, biomedical ethics, philosophy of law, philosophy of mind and cognitive science. Philosophy Department, University of British Columbia. Winter 2014-Fall 2017.

Teaching Training

  • Certificate Program in Advanced Teaching and Learning at University of British Columbia.
  • Instructional Skills Workshop, UBC Graduate Pathways to Success.


Mailing Address
Sina Fazelpour
Carnegie Mellon University
Department of Philosophy
Baker Hall 161
5000 Forbes Avenue
Pittsburgh, PA 15213
United States
Room 4305,
Doherty Hall
Hamerschlag Dr.
Pittsburgh, PA 15213
United States

Email Address
sinaf [at]