•  60
    Explanation Hacking: The perils of algorithmic recourse
    with Atoosa Kasirzadeh
    In Juan Manuel Durán & Giorgia Pozzi (eds.), Philosophy of science for machine learning: Core issues and new perspectives, Springer. forthcoming.
    We argue that the trend toward providing users with feasible and actionable explanations of AI decisions—known as recourse explanations—comes with ethical downsides. Specifically, we argue that recourse explanations face several conceptual pitfalls and can lead to problematic explanation hacking, which undermines their ethical status. As an alternative, we advocate that explanations of AI decisions should aim at understanding.
  •  270
    Do ML models represent their targets?
    Philosophy of Science. forthcoming.
    I argue that ML models used in science function as highly idealized toy models. If we treat ML models as a type of highly idealized toy model, then we can deploy standard representational and epistemic strategies from the toy model literature to explain why ML models can still provide epistemic success despite their lack of similarity to their targets.
  •  8
    Talk at the Philosophy [in:of:for:and] Digital Knowledge Infrastructures online workshop (08/09/2022).
  •  327
    How Values Shape the Machine Learning Opacity Problem
    In Insa Lawler, Kareem Khalifa & Elay Shech (eds.), Scientific Understanding and Representation, Routledge. pp. 306-322. 2022.
    One of the main worries with machine learning model opacity is that we cannot know enough about how the model works to fully understand the decisions they make. But how much is model opacity really a problem? This chapter argues that the problem of machine learning model opacity is entangled with non-epistemic values. The chapter considers three different stages of the machine learning modeling process that corresponds to understanding phenomena: (i) model acceptance and linking the model to the…Read more
  •  195
    People have always shared information through chains and networks of testimony. It’s arguably part of what makes us human and enables us to live in cooperative communities with populations greater than the Dunbar number. The invention of the Internet and the rise of social media have turbo-charged our ability to share information. In this chapter, we develop a normative framework for sharing information online. This framework takes into account both ethical and epistemic considerations that are …Read more
  •  2
    Online trust and distrust
    In Michael Hannon & Jeroen de Ridder (eds.), The Routledge Handbook of Political Epistemology, Routledge. 2021.
    Trust makes cooperation possible. It enables us to learn from others and at a distance. It makes democratic deliberation possible. But it also makes us vulnerable: when we place our trust in another’s word, we are liable to be deceived—sometimes intentionally, sometimes unintentionally. Our evolved mechanisms for deciding whom to trust and whom to distrust mostly rely on face-to-face interactions with people whose reputation we can both access and influence. Online, these mechanisms are largely …Read more
  •  273
    The epistemic position of an agent often depends on their position in a larger network of other agents who provide them with information. In general, agents are better off if they have diverse and independent sources. Sullivan et al. [19] developed a method for quantitatively characterizing the epistemic position of individuals in a network that takes into account both diversity and independence; and presented a proof-of-concept, closed-source implementation on a small graph derived from Twitter…Read more
  •  236
    From Explanation to Recommendation: Ethical Standards for Algorithmic Recourse
    Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES’22). forthcoming.
    People are increasingly subject to algorithmic decisions, and it is generally agreed that end-users should be provided an explanation or rationale for these decisions. There are different purposes that explanations can have, such as increasing user trust in the system or allowing users to contest the decision. One specific purpose that is gaining more traction is algorithmic recourse. We first pro- pose that recourse should be viewed as a recommendation problem, not an explanation problem. Then,…Read more
  •  523
    Inductive Risk, Understanding, and Opaque Machine Learning Models
    Philosophy of Science 89 (5): 1065-1074. 2022.
    Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validatin…Read more
  •  231
    Motivated numeracy and active reasoning in a Western European sample
    with Paul Connor, Mark Alfano, and Nava Tintarev
    Behavioral Public Policy 1. 2020.
    Recent work by Kahan et al. (2017) on the psychology of motivated numeracy in the context of intracultural disagreement suggests that people are less likely to employ their capabilities when the evidence runs contrary to their political ideology. This research has so far been carried out primarily in the USA regarding the liberal–conservative divide over gun control regulation. In this paper, we present the results of a modified replication that included an active reasoning intervention with Wes…Read more
  •  478
    Negative Epistemic Exemplars
    In Stacey Goguen & Benjamin Sherman (eds.), Overcoming Epistemic Injustice: Social and Psychological Perspectives, Rowman & Littlefield. 2019.
    In this chapter, we address the roles that exemplars might play in a comprehensive response to epistemic injustice. Fricker defines epistemic injustices as harms people suffer specifically in their capacity as (potential) knowers. We focus on testimonial epistemic injustice, which occurs when someone’s assertoric speech acts are systematically met with either too little or too much credence by a biased audience. Fricker recommends a virtue­theoretic response: people who do not suffer from biases…Read more
  •  1100
    Vulnerability in Social Epistemic Networks
    with Max Sondag, Ignaz Rutter, Wouter Meulemans, Scott Cunningham, Bettina Speckmann, and Mark Alfano
    International Journal of Philosophical Studies 28 (5): 1-23. 2020.
    Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that g…Read more
  •  98
    Model Explanation Versus Model-Induced Explanation
    Foundations of Science 26 (4): 1049-1074. 2021.
    Scientists appeal to models when explaining phenomena. Such explanations are often dubbed model explanations or model-based explanations. But what are the precise conditions for ME? Are ME special explanations? In our paper, we first rebut two definitions of ME and specify a more promising one. Based on this analysis, we single out a related conception that is concerned with explanations that are induced from working with a model. We call them ‘model-induced explanations’. Second, we study three…Read more
  •  531
    Vectors of epistemic insecurity
    In Ian James Kidd, Quassim Cassam & Heather Battaly (eds.), Vice Epistemology, Routledge. 2020.
    Epistemologists have addressed a variety of modal epistemic standings, such as sensitivity, safety, risk, and epistemic virtue. These concepts mark out the ways that beliefs can fail to track the truth, articulate the conditions needed for knowledge, and indicate ways to become a better epistemic agent. However, it is our contention that current ways of carving up epistemic modality ignore the complexities that emerge when individuals are embedded within a community and listening to a variety of…Read more
  •  417
    Humility in networks
    In Mark Alfano, Michael Patrick Lynch & Alessandra Tanesini (eds.), The Routledge Handbook of the Philosophy of Humility, Routledge. 2021.
    What do humility, intellectual humility, and open-mindedness mean in the context of inter-group conflict? We spend most of our time with ingroup members, such as family, friends, and colleagues. Yet our biggest disagreements —— about practical, moral, and epistemic matters —— are likely to be with those who do not belong to our ingroup. An attitude of humility towards the former might be difficult to integrate with a corresponding attitude of humility towards the latter, leading to smug tribalis…Read more
  •  874
    Ethical pitfalls for natural language processing in psychology
    with Mark Alfano and Amir Ebrahimi Fard
    In Morteza Dehghani & Ryan Boyd (eds.), The Atlas of Language Analysis in Psychology, Guilford Press. forthcoming.
    Knowledge is power. Knowledge about human psychology is increasingly being produced using natural language processing (NLP) and related techniques. The power that accompanies and harnesses this knowledge should be subject to ethical controls and oversight. In this chapter, we address the ethical pitfalls that are likely to be encountered in the context of such research. These pitfalls occur at various stages of the NLP pipeline, including data acquisition, enrichment, analysis, storage, and shar…Read more
  •  1633
    Understanding from Machine Learning Models
    British Journal for the Philosophy of Science 73 (1): 109-133. 2022.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the…Read more
  •  435
    Universality caused: the case of renormalization group explanation
    European Journal for Philosophy of Science 9 (3): 36. 2019.
    Recently, many have argued that there are certain kinds of abstract mathematical explanations that are noncausal. In particular, the irrelevancy approach suggests that abstracting away irrelevant causal details can leave us with a noncausal explanation. In this paper, I argue that the common example of Renormalization Group explanations of universality used to motivate the irrelevancy approach deserves more critical attention. I argue that the reasons given by those who hold up RG as noncausal d…Read more
  •  1898
    Can Real Social Epistemic Networks Deliver the Wisdom of Crowds?
    with Max Sondag, Ignaz Rutter, Wouter Meulemans, Scott Cunningham, Bettina Speckmann, and Mark Alfano
    In Tania Lombrozo, Joshua Knobe & Shaun Nichols (eds.), Oxford Studies in Experimental Philosophy, Volume 1, Oxford University Press. forthcoming.
    In this paper, we explain and showcase the promising methodology of testimonial network analysis and visualization for experimental epistemology, arguing that it can be used to gain insights and answer philosophical questions in social epistemology. Our use case is the epistemic community that discusses vaccine safety primarily in English on Twitter. In two studies, we show, using both statistical analysis and exploratory data visualization, that there is almost no neutral or ambivalent discuss…Read more
  •  321
    Idealizations and Understanding: Much Ado About Nothing?
    Australasian Journal of Philosophy 97 (4): 673-689. 2019.
    Because idealizations frequently advance scientific understanding, many claim that falsehoods play an epistemic role. In this paper, we argue that these positions greatly overstate idealiza...
  •  232
    Understanding: not know-how
    Philosophical Studies 175 (1): 221-240. 2018.
    There is considerable agreement among epistemologists that certain abilities are constitutive of understanding-why. These abilities include: constructing explanations, drawing conclusions, and answering questions. This agreement has led epistemologists to conclude that understanding is a kind of know-how. However, in this paper, I argue that the abilities constitutive of understanding are the same kind of cognitive abilities that we find in ordinary cases of knowledge-that and not the kind of pr…Read more