The philosophical debate around the impact of machine learning in science is often framed in terms of a choice between AI and classical methods as mutually exclusive alternatives involving difficult epistemological trade-offs. A common worry regarding machine learning methods specifically is that they lead to opaque models that make predictions but do not lead to explanation or understanding. Focusing on the field of molecular biology, I argue that in practice machine learning is often used with explanatory aims. More specifically, I (...) argue that machine learning can be tightly integrated with other, more traditional, research methods and in a clear sense can contribute to insight into the causal processes underlying phenomena of interest to biologists. One could even say that machine learning is not the end of theory in important areas of biology, as has been argued, but rather a new beginning. I support these claims with a detailed discussion of a case study involving gene regulation by microRNAs. (shrink)

The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry (...) that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally. (shrink)

This essay provides a framework of concepts and principles suitable for systematic discussion of issues surrounding expertise. Expertise creates inequality. Its multiple benefits and the creativity of technology lead to a society replete with expertises. The basic binds of expertise derive from the desire of non-experts to be able to both enjoy what expertise offers and insure that it is exercised in the social interest. This involves trusting the exercise of expertise, involuntarily or voluntarily. A healthy society provides various means (...) to move trust from involuntary to voluntary. The social means for achieving this are laid out. The purpose of this short essay is to briefly lay out a conceptual framework within which to construct, clarify, evaluate and apply expertises. It is not to promote some particular notion of expertise over others, or to review the vast literatures, such as that on trust in science, that make up the domain. A few notes on one work towards this essay’s close may indicate what a major, and expert, process this would be. (shrink)

A paradigm instructs in how to do research successfully. Analytic philosophy of science, currently dominant, models paradigmatic rational science as a system of logical inferences. It is, however, an abundantly inadequate paradigm. This paper presents an alternative paradigm: science as an organized collection of problem solving processes. This position is backed, on the one side, by a cognitive model of problem solving process applicable to all problem solving circumstances and, on the other, by a non-formal conception of rationality that provides (...) a wider enriched notion of rational research process than is available to the analytic paradigm. The result is a very different way of looking at science and of doing history and philosophy of science. The position is developed sufficiently to display its nature and merits. (shrink)

To learn about real world phenomena, scientists have traditionally used models with clearly interpretable elements. However, modern machine learning (ML) models, while powerful predictors, lack this direct elementwise interpretability (e.g. neural network weights). Interpretable machine learning (IML) offers a solution by analyzing models holistically to derive interpretations. Yet, current IML research is focused on auditing ML models rather than leveraging them for scientific inference. Our work bridges this gap, presenting a framework for designing IML methods—termed ’property descriptors’—that illuminate not just (...) the model, but also the phenomenon it represents. We demonstrate that property descriptors, grounded in statistical learning theory, can effectively reveal relevant properties of the joint probability distribution of the observational data. We identify existing IML methods suited for scientific inference and provide a guide for developing new descriptors with quantified epistemic uncertainty. Our framework empowers scientists to harness ML models for inference, and provides directions for future IML research to support scientific understanding. (shrink)

The use of machine learning instead of traditional models in neuroscience raises significant questions about the epistemic benefits of the newer methods. I draw on the literature on model intelligibility in the philosophy of science to offer some benchmarks for the interpretability of artificial neural networks used as a predictive tool in neuroscience. Following two case studies on the use of ANN’s to model motor cortex and the visual system, I argue that the benefit of providing the scientist with understanding (...) of the brain trades off against the predictive accuracy of the models. This trade-off between prediction and understanding is better explained by a non-factivist account of scientific understanding. (shrink)