CURRENT WORK

A paper on statistical models (R&R at Philosophy of Science; email for manuscript)

Traditional wisdom dictates that statistical model outputs are estimates, not measurements. Despite this, statistical models are employed as measurement instruments in the social sciences. Within philosophy, the model-based account of measurement offers a theoretical foundation for the use of statistical models as measurement instruments

In this paper, I scrutinize the use of a specific model - logistic regression - for measurement in the social sciences. I consider how different theories of measurement impact whether the outputs of statistical model qualify as measurements. Ultimately, I argue that logistic regression fails to yield measurements regardless of which theory of measurement we accept.

A paper on machine learning (under review; email for manuscript)

Models in machine learning are opaque - some call them 'black-box models' for this reason. It is often argued that opaque models need special ethical and legal attention not afforded to classical statistical models. There is no consensus in the literature, however, about what opacity is and how it distinguishes models in machine learning from classical statistical models. Words like 'explainability' and 'interpretability' are used interchangeably to characterize model opacity.

In this paper, I argue for three main conclusions: (1) If we recognize a distinction between formal and efficient explanations, then black-box models are explainable, at least formally. (2) Given that black-box models are formally explainable, opacity is best understood as a lack of interpretability rather than a lack of explainability. Interpretability, I argue, can be precisely defined as a mapping between model terms and relations in a real system. (3) Once model opacity is defined in terms of interpretability rather than explainability, some legal and ethical worries about the use of black-box models for public-facing decisions are deflated.

'Carnap and Bar-Hillel's Theory of Semantic Information' (forthcoming)

This paper is included included in the forthcoming 'Carnap Handbuch' edited by Christian Dambock and Georg Scheimer.

'A New Defense of Inductive Logic by Analogy With Deductive Logic' (email for manuscript)

In this paper I offer a new defense of Carnap's inductive logic and the logical interpretation of probability that goes with it. While many consider language relativity to undermine the objectivity of Carnap's system of inductive logic, I show that deductive logic is vulnerable to the same concerns about language relativity. It is better, I propose, to accept that both inductive and deductive logic offer an account of objective relations between propositions but not objective initial truth value or probability assignments. I argue that the logical interpretation of probability is the correct interpretation of non-physical probability, where non-physical probability characterizes, for example, the non-deductive support relation between evidence and hypothesis.