The use of fictional models is extensive and rewarding in modern science. This fact captured the attention of philosophers of science, who are focusing on questions such as the following: is it possible for a fictional model to be explanatory? And, if so, in virtue of what is such a fictional model explanatory? In this paper, I discuss these questions in relation to the realism vs. anti-realism debate in philosophy of science. I focus on work developed by Alisa Bokulich who (...) has argued for the explanatory role played by fictional models, claiming that it can be accommodated by realism in ‘a broad sense’. To this end, Bokulich introduced what she called ‘an eikonic conception’ of explanation, as opposed to the so-called ‘ontic’ conception. This move, Bokulich claims, allows non-causal explanations to be acceptable to a modified version, as opposed to a ‘naïve’ brand of realism. Roughly, a fictional model can capture aspects of reality if it manages to act as a proxy for a theory targeting a physical system. As Bokulich explains with the example of James Clerk Maxwell’s use of mechanical models, this is due to structural correspondences between a model and a theory at a certain level of abstraction. However, Bokulich does not explain how the model grasps reality. In this paper, I fill this missing piece of the puzzle. While I agree with Bokulich on the explanatory function of fictional models, I shall invoke her reading of Maxwell’s work to draw a different conclusion. Specifically, I argue that structural correspondences at various levels form what I call ‘a ladder of abstractions’ which connects aspects of a target system to the corresponding depths of descriptions. Modifying in these terms the scope of ‘representing a system’, I suggest that Bokulich’s proposed modification of realism is obviated. (shrink)

Relationships of counterfactual dependence have played a major role in recent debates of explanation and understanding in the philosophy of science. Usually, counterfactual dependencies have been viewed as the explanantia of explanation, i.e., the things providing explanation and understanding. Sometimes, however, counterfactual dependencies are themselves the targets of explanations in science. These kinds of explanations are the focus of this paper. I argue that “micro-level model explanations” explain the particular form of the empirical regularity underlying a counterfactual dependency by representing (...) it as a physical necessity on the basis of postulated microscopic entities. By doing so, micro-level models rule out possible forms the regularity could have taken. Micro-model explanations, in other words, constrain empirical regularities and their associated counterfactual dependencies. I introduce and illustrate micro-level model explanations in detail, contrast them to other accounts of explanation, and consider potential problems. (shrink)

Assistive systems based on Artificial Intelligence (AI) are bound to reshape decision-making in all areas of society. One of the most intricate challenges arising from their implementation in high-stakes environments such as medicine concerns their frequently unsatisfying levels of explainability, especially in the guise of the so-called black-box problem: highly successful models based on deep learning seem to be inherently opaque, resisting comprehensive explanations. This may explain why some scholars claim that research should focus on rendering AI systems understandable, rather (...) than explainable. Yet, there is a grave lack of agreement concerning these terms in much of the literature on AI. We argue that the seminal distinction made by the philosopher and physician Karl Jaspers between different types of explaining and understanding in psychopathology can be used to promote greater conceptual clarity in the context of Machine Learning (ML). Following Jaspers, we claim that explaining and understanding constitute multi-faceted epistemic approaches that should not be seen as mutually exclusive, but rather as complementary ones as in and of themselves they are necessarily limited. Drawing on the famous example of Watson for Oncology we highlight how Jaspers’ methodology translates to the case of medical AI. Classical considerations from the philosophy of psychiatry can therefore inform a debate at the centre of current AI ethics, which in turn may be crucial for a successful implementation of ethically and legally sound AI in medicine. (shrink)