As machine learning (ML) algorithms are increasingly used to inform decisions across domains, there has been a proliferation of literature seeking to define “fairness” narrowly as an error to be “fixed” and to quantify it as an algorithm’s deviation from a formalised metric of equality. Dozens of notions of fairness have been proposed, many of which are both mathematically incompatible and morally irreconcilable with one another. There is little consensus on how to define, test for, and mitigate unfair algorithmic bias.

One key obstacle is the disparity between academic theory and practical and contextual applicability. The unambiguous formalisation of fairness in a technical solution is at odds with the contextualised needs in practice. The notion of algorithmic fairness lies at the intersection of multiple domains, including non-discrimination law, statistics, welfare economics, philosophical ethics, and computer science. Literature on algorithmic fairness has predominantly been published in computer science, and while it has been shifting to consider contextual implications, many approaches crystallised into open source toolkits are tackling a narrowly defined technical challenge.

The objective of my PhD thesis is to address this gap between theory and practice in computer science by presenting context-conscious methodologies throughout ML de- velopment lifecycles. The core chapters are organised by each phase: design, build, test, and monitor. In the design phase, we propose a systematic way of defining fairness by understanding the key ethical and practical trade-offs. In the test phase, we introduce methods to identify and measure risks of unintended biases. In the deploy phase, we identify appropriate mitigation strategies depending on the source of unfairness. Finally, in the monitor phase, we formalise methods for monitoring fairness and adjusting the ML model appropriately to any changes in assumptions and input data.

The primary contribution of my thesis is methodological, including improving our understanding of limitations of current approaches and proposal of new tools and in- terventions. It shifts the conversation in academia away from axiomatic, unambiguous formalisations of fairness towards a more context-conscious, holistic approach that covers the end-to-end ML development lifecycle. This thesis aims to provide end-to-end coverage in guidance for industry practitioners, regulators, and academics on how fairness can be considered and enforced in practice.