Soo Jin Suzie Kim

The assumption that the justifiability of scientific belief depends exclusively on the relevant facts is a widely accepted orthodoxy both inside and outside of the scientific establishment. Drawing on the pragmatic and moral encroachment thesis in epistemology, this article challenges that assumption by showing that practical and moral considerations affect the justifiability of our beliefs regarding the safety of COVID-19 vaccines. In particular, I show that a proper consideration of the practical and moral costs of committing to the belief that COVID-19 vaccines are safe enough for a general vaccine mandate requires COVID-19 vaccines to meet a higher-than-current evidentiary standard of safety. Furthermore, I show that a reassessment of existing COVID-19 vaccines against a higher evidentiary standard of safety would entail a wider scope of medical exemptions and remedial duties. Beyond the case of COVID-19 vaccines, this argument has important general implications for how scientific beliefs should be assessed.

Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of symbolic regression. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. We propose a framework that leverages deep learning for symbolic regression via a simple idea: use a large model to search the space of small models. Specifically, we use a recurrent neural network to emit a distribution over tractable mathematical expressions and employ a novel risk-seeking policy gradient to train the network to generate better-fitting expressions. Our algorithm outperforms several baseline methods (including Eureqa, the gold standard for symbolic regression) in its ability to exactly recover symbolic expressions on a series of benchmark problems, both with and without added noise. More broadly, our contributions include a framework that can be applied to optimize hierarchical, variable-length objects under a blackbox performance metric, with the ability to incorporate constraints in situ, and a risk-seeking policy gradient formulation that optimizes for best-case performance instead of expected performance.

According to T.M. Scanlon, the core idea of contractualism consists in the claim that what we are morally required to do is conceptually grounded in the value of living in “relations of mutual recognition” with others. Specifically, Scanlon's contractualist idea of “living in relations of mutual recognition with others” requires that one act only in ways that cannot be reasonably rejected by all of those affected, according to the results of a hypothetical reflection conducted within one's own mind. I claim, however, that Scanlon is mistaken to rely on a single individual's hypothetical reflection (monological reflection) to determine what no one affected could reasonably reject. Instead, following Jürgen Habermas, I claim that we must engage in actual dialogue with all who stand to be affected, in order to live in relations of mutual recognition with others.