The pace of progress in machine learning is astounding. As a community, we have made great leaps forward across a variety of tasks, from complex vision challenges such as scene segmentation and object recognition, to striking language understanding capability and remarkably fluent text generation. As a result, machine learning finds itself increasingly deployed in high-stakes domains such as healthcare, justice, hiring, and driverless vehicles. Without diminishing such achievements, these remarkable claims often obscure a more nuanced reality in which machine learning performs well under narrow and specific conditions.

This thesis is motivated by the desire for a machine learning that works equally well for all people, for reliable systems we can trust, and for a fuller understanding of the way our models perform. The typical model analysis toolkit, comprising standardised evaluation datasets, aggregated performance metrics, and off-the-shelf methods for interpretability and explainability, often proves useful in the appropriate circumstances. But, in situations where we seek precise and detailed understanding of exactly what our models actually do—i.e., a systematic account of their behaviour in response to various inputs—we may stand to benefit from new approaches. In search of complementary ideas to support the scientific study of machine learning behaviour, this thesis turns for inspiration to the prototypical science of behaviour: experimental psychology.

Drawing on experimental psychology along epistemological, methodological and metascientific lines, this thesis will explore ideas including the differing nature of explanatory practices, the role of experimental design and a focus on observable behaviour, and lessons from psychology’s most recent replication crisis. In doing so, we’ll apply psychological principles and techniques to a range of contemporary machine learning settings, with a particular focus on neural networks.

First, we will introduce simple behavioural experiments to shed light on the phenomenon of catastrophic forgetting. In particular, through the use of synthetic parameterisable stimuli, we’ll investigate the role of task similarity and task ordering in a continual learning setup. Our analysis leads us to an easy-to-compute heuristic that is predictive of catastrophic forgetting.

Second, we apply our approach to the important context of machine learning fairness, and develop novel behavioural experiments designed to isolate a new form of algorithmic bias. Specifically, we demonstrate that the particular aspects of a dataset that a model finds challenging (be they classes, demographic groups, or otherwise) will vary from model to model, and it is hard to identify what is “difficult” without first training a model. When difficulty varies along demographic lines, we show that this can lead to amplified performance disparities when using popular deep learning architectures.

Third, following a brief survey of psychology’s own crisis of confidence, we then introduce the multiverse analysis to machine learning research. While the multiverse analysis is first developed in psychology, we make significant adaptations so as to render it tractable in the machine learning setting, through the use of a Gaussian Process surrogate and Bayesian experimental design. We demonstrate the utility of the machine learning multiverse through two case studies, one on the relative merit of adaptive versus non-adaptive gradient-based optimisers, and the other on the large batch generalisation gap.

The common thread that runs throughout this thesis is the application of ideas from experimental psychology to the machine learning setting. If we hope to maintain the tangible sense of rapid progress in machine learning, yet also to build confidence and trust that our systems consistently work as we expect, it is essential we develop thorough accounts of exactly what our models do in key scenarios. We hope to contribute to this endeavour in our work, by way of an exploration of approaches informed by the discipline of experimental psychology, and their application to the scientific study of machine learning behaviour.