Sina Fazelpour

Assistant Professor

Sina Fazelpour
  • Data-driven and artificial intelligence technologies
  • Diversity in social groups and networks
  • PhD in Philosophy, University of British Columbia — Canada
  • BA in Philosophy, University of Toronto — Canada
  • MS in Medical Biophysics, University of Toronto — Canada
  • BE in Electrical and Biomedical Engineering, McMaster University — Canada

Sina Fazelpour is an assistant professor in the Khoury College of Computer Sciences and the College of Social Sciences and Humanities at Northeastern University, based in Boston.

Fazelpour researches issues of justice, diversity, and reliability in data-driven and artificial intelligence technologies. He also works to understand the concepts and consequences of diversity in social groups and networks. In doing so, Fazelpour draws on analytical tools of philosophy, methods of cognitive science, and formal techniques of agent-based simulation and machine learning. His articles on these topics and other areas have appeared in Philosophy of Science, Philosophy and Phenomenological Research, Synthese, European Journal for Philosophy of Science, Cognition, and more.

Before joining Northeastern, Fazelpour was an SSHRC Postdoctoral Fellow in the Department of Philosophy at Carnegie Mellon University, with a secondary affiliation with the Machine Learning Department. During 2020 and 2021, he was the council fellow on the World Economic Forum’s Global Future Council on Data Policy. In addition to a doctorate in philosophy, Fazelpour holds a master’s degree in medical biophysics and a bachelor’s degree in electrical and biomedical engineering.

  • Diversity and homophily in social networks

    Citation: Sina Fazelpour, Hannah Rubin. (2022). Diversity and homophily in social networks CogSci. https://escholarship.org/uc/item/7646n2mc

  • Homophily and Incentive Effects in Use of Algorithms

    Citation: Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary C. Lipton, David Danks. (2022). Homophily and Incentive Effects in Use of Algorithms CoRR, abs/2205.09701. https://doi.org/10.48550/arXiv.2205.09701

  • Fair Machine Learning Under Partial Compliance

    Citation: Jessica Dai, Sina Fazelpour, and Zachary Lipton. 2021. Fair Machine Learning Under Partial Compliance. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. Association for Computing Machinery, New York, NY, USA, 55–65. doi:10.1145/3461702.3462521

  • Norms in Counterfactual Selection

    Citation: Fazelpour, S. Norms in Counterfactual Selection. Philos Phenomenol Res. 2021; 103: 114– 139. doi:10.1111/phpr.12691

  • Affect-biased attention and predictive processing

    Citation: Ransom, Madeleine, Sina Fazelpour, Jelena Markovic, James Kryklywy, Evan T. Thompson, and Rebecca M. Todd. "Affect-biased attention and predictive processing." Cognition 203 (2020): 104370. doi:10.1016/j.cognition.2020.104370

  • Algorithmic Fairness from a Non-ideal Perspective

    Citation: Sina Fazelpour and Zachary C. Lipton. 2020. Algorithmic Fairness from a Non-ideal Perspective. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES '20). Association for Computing Machinery, New York, NY, USA, 57–63. doi:10.1145/3375627.3375828

  • Attention in the predictive mind

    Citation: Ransom, Madeleine, Sina Fazelpour, and Christopher Mole. "Attention in the predictive mind." Consciousness and cognition 47 (2017): 99-112. doi:10.1016/j.concog.2016.06.011

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