Following a Postdoctoral Fellowship at the University of Western Ontario's Rotman Institute of Philosophy, I am working as a Senior Policy Associate at the University of Toronto's Mowat Centre. I held a previous Postdoc at the University of Tuebingen's Centre for Integrative Neuroscience and the Max Planck Institute for Biological Cybernetics. I received my PhD from the University of Pittsburgh in History & Philosophy of Science. I have a MSc in Computer Science from the University of Toronto, where I worked on the effects of biased datasets in machine learning. I am also active in outreach, including public lectures on AI Ethics, writing for a general audience about data privacy and informed consent, and a long-running civic engagement project called The Soft City that uses art to encourage participation in city building.
My academic research covers topics in the history & philosophy of science, including AI ethics, computational modeling methods,
model organisms, mechanistic explanation, body perception, and classification in psychiatry. I am also doing policy research covering big data
and AI in healthcare, informed consent, data governance, and algorithmic decision-making.
Computer Science departments and technology companies are waking up to the dangers that can result from the field's `build first, fix later' ethos, especially when deploying AI systems. I note that many of the strategies being discussed to deal with AI's ethical crisis suffer from the same problems that created the crisis: tech culture's belief that all problems have technical solutions and devaluing of outside expertise. I draw on standpoint epistemology and social epistemology to develop more promising strategies for solving ethical problems in AI.
Historians emphasize how choices of model organisms are made on pragmatic grounds or due to contingent factors, while many scientists and philosophers claim that model organisms ought to be chosen based on phylogenetic relatedness. I examine a variety of examples of model organism, including plants, and argue that i) phylogenetic relatedness is a poor guide for choosing model organisms, and ii) in many cases the apparently pragmatic factors guiding model choice have an epistemic character.
Mechanistic and mathematical explanations are made out to be distinct types of explanation with rather different properties. Whether the relation between explanandum and explanans is symmetric or not is taken to be one important difference. I suggest that both types of expanation can be either symmetric or asymmetric, depending on the possible interventions we can do on the system, and the properties of the relation. It is the possibility of interventions that determines wheher the relation is causal or not. In most interesting cases of scientific explanation, mathematical and mechanistic explanation need to work together.
Scientists treat abstractions like network structures or populations as though they are important bearers of causal powers. Philosophers tend to think of abstractions as mere representations, which are not the right kinds of things to bear causal power. This project looks at the history of metaphysics to examine the reasons why we think of concrete universals as contradictions in terms, and to carve out a metaphysical option that might better express scientists' views. This is a joint project with .
Pregnant people are very closely monitored, and given a vast array of advice about what to do, and (primarily) what NOT to do. Focusing just on the proscriptions coming from health care providers, we investigate the ways in which statistics are used (and sometimes misused) to justify these proscriptions. In some cases, arguments from ignorance are employed (e.g., to restrict drinking), when risks are unknown. In other cases, arguments about risk increase are employed (e.g., to encourage inducing labour after the due date), when overall risk remains low. This is a joint project with .
Idealization in Computational Models
In a 2018 chapter about Explanation and Connectionist Models in The Routledge Handbook of the Computational Mind, I outline the computational modeling methods used in cognitive science, and contrast them with the simulation methods used in the physical sciences, as well as with the more detailed brain simulations sometimes used in neuroscience. I argue that connectionist models of cognition explain by instantiating idealized neural models, and demonstrating their properties.
In a paper under review, I argue that the Parallel Distributed Procesing (PDP) Research Group's approach to connectionist modeling in the 1980s to 1990s was largely misunderstood. I reinterpret both the textual evidence and the models they produced to show that realistic physiological detail was never their goal, despite some rhetoric that suggested this. Rather, although the idea hadn't yet been described and given a vocabulary in philosophy of science, what the PDP group were doing was creating idealized models, and using them as tools for discovering multi-level mechanistic explanations of cognition.
Body Perception in Psychiatry
In a paper in Synthese (online 2017), I demonstrate that treatment and research of Anorexia Nervosa (AN) largely overlook one of its three diagnostic criteria---a disturbance of body perception---despite evidence that this symptom might be central to AN etiology and treatment outcomes. I review the history of revisions to the Eating Disorders category of the Diagnostic and Statistical Manual of Mental Disorders (DSM). This history suggests that the assumption that AN is an Eating Disorder may be responsible for AN's body perception symptoms being overlooked, and perhaps also partly responsible for AN's tragically high relapse and mortality rates. I propose a change to the DSM taxonomy allowing for disorders to be listed under multiple categories.
With several , I worked on a series of experiments investigating whether the Rubber-Hand Illusion (RHI) paradigm might be useful for changing perceptions of the size of one's body. In the first study, using Virtual Reality, we induced a full body RHI on healthy participants, effectively changing their perceptions of the size of their bodies, at least in the short term. This study was published in PLOS One in 2014. Another group recently showed that this effect can be maintained for several hours in both participants with AN and healthy controls, suggesting that it might be a useful paradigm for treating the disturbance of body perception in AN.
Ehrsson, Holmes, and Passingham (2005) introduced a non-visual version of the RHI. I ran an experiment demonstrating that participants can induce a similar tactile version of the RHI on their own body, without guidance from an experimenter. We compare active (subject controlling the brush) and passive (experimenter controlling the brush) touch on objective and subjective messures of the RHI. Preliminary analysis of the results shows i) subjects can independently induce a tactile RHI on their own body, and ii) the active variant of the RHI is stronger than the passive version. These results suggest a way of developing home therapies that might help modify the disturbance of body image characteristic of Anorexia Nervosa.
Mechanistic Explanation in Neuroscience
My dissertation was about how to integrate explanations in cognitive psychology and neuroscience. I defended a non-hierarchical view of the relations between models pitched at different levels of grain and abstraction. On this account, each model might express only some of the causes at play, or part of the mechanism. Multiple models do not fit together neatly like pieces of a puzzle or mosaic, but instead partially overlap in patchy ways.
In a paper published in Synthese (2016), I argued against popular accounts of integration that suggest that psychology and neuroscience can be seamlessly integrated by taking psychological explanations to be sketches of neural mechanisms.
In a paper co-authored with , we used several historical case studies in neuroanatomy, and neurophysiology to illustrate how explanations develop from sketchy, primitive ideas to detailed mechanistic accounts. We highlighted several issues that have not received adequate attention elsewhere, including how top-down and bottom-up methods need to be combined in order to discover how structure and function relate; alternatives to the unrealistic expectation that explanations in neuroscience will all fit into a neat hierarchy of levels; and how the use of model organisms and varying experimental protocols complicates this research. This paper appeared (2017) in The Routledge Handbook of Mechanisms and Mechanical Philosophy.
In a paper for Peter Machamer's Festschrift (2017, Springer), I examine the notion of a mechanism schema. Peter has suggested that Piaget's theory of child development was his inspiration for mechanism schemata. I compare mechanism schemata to Piagetian schemata to draw out some features of mechanism schemata that have been overlooked in the new mechanist literature.
I have taught for several years at the University of Toronto, in the Cognitive Science program. I have
also taught in philosophy, history and philosophy of science, computer science, and neuroscience departments.
Select Courses Taught
The following is a list of select courses I have taught with links to syllabi and course materials. A complete list can be found on my CV.
The full list of my presentations is available in my CV.