On 4 May at 11:00, Modar Sulaiman will defend his doctoral thesis “From Data to Fair Decisions: On Ensuring Fairness in Machine Learning Models” to obtain the degree of Doctor of Philosophy (in Computer Science).
Supervisor
Lecturer Kallol Roy, University of Tartu
Opponents
Prof. Catuscia Palamidessi, National Institute for Research in Digital Science and Technology (France)
Assoc. Prof. Özlem Özgöbek, Norwegian University of Science and Technology (Norway)
Summary
Machine learning systems have quietly become integral to many everyday decisions, such as who is invited to a job interview, which loan application is approved, and what kind of medical follow-up is recommended. These systems can process vast amounts of data and make decisions at a scale no human could match. Still, this efficiency comes with a risk: when the underlying data reflect existing social biases, the resulting models may systematically disadvantage specific individuals or groups based on characteristics such as gender, age, or ethnicity. This thesis poses a demanding yet straightforward question: How can we build models that remain accurate while treating different groups more fairly?
The thesis approaches this challenge from three complementary directions. First, it introduces a method termed 'The Fairness Stitch', which intervenes inside the layers of deep neural networks rather than only adjusting the final output layer. By modifying the internal representation in this way, the method can substantially reduce hidden biases while maintaining a reasonable level of predictive accuracy. Second, the work proposes a group-level training scheme that assigns different penalties to errors across various demographic groups, thereby encouraging the model to perform robustly not only on the majority group but also on groups that are typically underrepresented or systematically disadvantaged in the data. Third, the thesis develops a graph-based technique for identifying and correcting unreliable training labels, with particular attention to settings in which some groups are exposed to noisier or more biased labelling processes than others.
Together, these three lines of work show that fairness does not have to be an afterthought. By intervening in the model architecture, the training objective, and the quality of the data, it is possible to obtain classifiers that are more balanced across groups while still performing well overall. The results provide practical tools and design principles for organisations that wish to use machine learning responsibly in high-stake decision-making.
