Beyond technical fixes: exploring ethical and political education in the training of data scientists

Weaver, Nicola Jane (2026) Beyond technical fixes: exploring ethical and political education in the training of data scientists. Ed.D thesis, University of Glasgow.

Abstract

This dissertation adopts a primarily qualitative approach, grounded in semi-structured interviews with in-service data scientists, to explore how ethical dilemmas are experienced, interpreted and navigated in professional contexts. The research is motivated by the increasing societal scrutiny of data-driven technologies and the recognition that data science, far from being a neutral or purely technical field, is entangled with questions of power, justice and social impact.

The aim is to investigate the everyday ethical challenges data scientists face and their perceptions of the adequacy and relevance of existing (or absent) ethics education and professional guidelines. The interviews examine how data scientists make sense of their responsibilities, the pressures and constraints they encounter in their organisational settings, and the various ways by which they attempt to balance technical objectives with broader social values. In doing so, the dissertation addresses a gap in the literature: the lived realities of data scientists who face the continuous need for ethical reasoning and decision-making in the rapidly evolving landscape of data science.

These empirical insights form the foundation for a secondary, but vital, philosophical dimension of the project: a normative argument that ethics education in data science should be reimagined to move beyond technical compliance and individual responsibility. Drawing on concepts from liberal and critical theories, the dissertation highlights the limitations of current approaches that emphasise personal virtue or adherence to professional codes, arguing that such frameworks are insufficient to address the structural and systemic dimensions of harm that data science can produce.

Instead, this dissertation proposes how ethics training can be reconceptualised to address broader structural concerns, including data and algorithmic biases, power asymmetries and social justice. In this way, my research bridges empirical inquiry and theory, using practitioners’ voices to describe the present landscape as well as to inform a more politically engaged vision for the future of ethics education in data science. By examining the individual experiences of data scientists, the study contributes to ongoing efforts to develop more robust, justice-oriented frameworks for both the teaching and practice of data science ethics. In doing so, it aspires to cultivate a data science profession that is not only technically proficient but also socially responsible and attuned to the demands of justice in a digital and data-driven world.

• Item Type: Thesis (Ed.D)
• Qualification Level: Doctoral
• Subjects: L Education > L Education (General)
• Colleges/Schools: College of Social Sciences > School of Education
• Supervisor's Name: Daniels, Dr. Stephen and Hedge, Professor Nicki
• Date of Award: 2026
• Depositing User: Theses Team
• Unique ID: glathesis:2026-86018
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 17 Jun 2026 09:43
• Last Modified: 17 Jun 2026 10:08
• Thesis DOI: 10.5525/gla.thesis.86018
• URI: https://theses.gla.ac.uk/id/eprint/86018