Model-based decision support with uncertain human-centric data

Both human and algorithmic decision making can be complex. To truly intertwine the two, algorithms need to understand the human decision making process and humans need to have transparent understanding of algorithmic decision analytics. In this thesis, we leverage Bayesian Gaussian processes to better understand people as well as offering a framework for people to better understand algorithms. Key to such transparency on both sides is the robust and principled reporting of uncertainty and careful consideration of preference: the latter being the foundational basis of human decision making. We consider pairwise preference modelling in which the strength of preference (e.g. strong or weak) can be seamlessly taken into account in the model, as well as confidence (uncertainty). We contribute to probabilistic functional regression by enabling the functional predictor space to be interpretable. We develop a principled, robust tool for understanding black-box algorithms, leading to a novel method for the interpretability of features in local domains. Finally, using our heteroscedastic ordinal regression model, we analyse the relationship between the features of occupations (such as their requirement for manual dexterity) and their expected share of US employment in 2030. Together, the approaches and algorithms we provide create a suite of models that increase our decision making capability with human-centric data