Adham El Shazly

Machine learning (ML) is increasingly used to interrogate biological systems whose complexity resists law-like, deductive explanation. As a result, embeddings, clusters, and attributions are often overinterpreted, dependencies are left implicit, and claims about explainability are often insufficiently bounded. In this work, we present a framework for contextualizing how machine learning contributes to scientific understanding in biology via compression, qualitative intelligibility, and dependency models. Compression is achieved when inductive biases encode biological structure, reducing the effective hypothesis space and yielding representations aligned with known biology. Qualitative intelligibility is supported when high-dimensional measurements are mapped to human-graspable objects, such as embeddings, clusters, and trajectories, that enable accurate qualitative reasoning without exact calculation. Dependency modelling is realized when learned models make explicit the pattern of relations among system components and thereby guide prediction and intervention. We examine how these principles manifest in successful ML applications and discuss considerations that emerge from this framework. Overall, when viewed through these lenses, ML can transform predictive success into intervention-guiding knowledge in the life sciences.

We often inquire not just to know, but to understand. In this paper I give an account of inquiries that aim to illuminate or makes sense of their object and argue they don’t reduce to inquiries which concern forming beliefs or acquiring knowledge. My core claim is that inquiry aimed at understanding is a constructive and generative process, unlike inquiry aiming at knowledge acquisition, which culminates in the representation of pre-existing facts. Central to this process are sensemaking frames—representational devices that interpret their target by structuring and organising information about it. To that end, I propose and defend the noetic account of inquiry, according to which epistemic improvement in inquiry is a matter of structuring information to illuminate and make sense of the object of inquiry. I conclude by explaining how inquiring to understand is distinctively open-ended, a creative process of structuring the information landscape, a landscape which isn’t always readily given to us.

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, yet it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems.

A platitude about understanding is that it involves grasping. But what is grasping? In this thesis, I develop a novel account of grasping that is rooted in phenomenal consciousness. According to this account, grasping is a matter of having a distinct kind of conscious experience: clear perception. Clear perception has a distinct cognitive phenomenology that characterizes it. Call this the clarity account of grasping. I go on to argue that the dominant view of grasping in the literature, the ability account of grasping, is false. Using the new clarity account, I argue that grasping is not only distinct from the cognitive abilities associated with understanding, but also explanatory prior to them. Finally, I argue that the clarity account implies that understanding cannot be transmitted through testimony, because clear perception cannot be acquired on the basis of testimony. As such, clear perception plays key cognitive and epistemic roles in understanding and coming to understand.