The focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.The goal is develop and assess... Show moreThe focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.The goal is develop and assess the technical methods which are required to shift the actions of the Inspectorate to a data-driven paradigm, concretely under a supervised classification framework of machine learning.The aspect of reliability is addressed as a data quality concern, viz. missingness and noise.The aspect of fairness is addressed as a counter to bias in the selection process of inspections.The conclusion is that, whilst no complete solution has yet been suggested, it is possible to address the concerns related to data quality and data bias, culminating in well-performing classification models which are reliable and fair. Show less
Patients share valuable advice and experiences with their peers in online patient discussion groups. These uncensored experiences can provide a complementaryperspective to that of the health... Show morePatients share valuable advice and experiences with their peers in online patient discussion groups. These uncensored experiences can provide a complementaryperspective to that of the health professional and thereby yield novel hypotheses which could be tested in further rigorous medical research. This thesis focuses on the development of automatic extraction methods to harvest these patient experiences from online patient forums using text mining techniques. We also examine the complementary value of these patient-reported outcomes to traditional sources of medical knowledge for scientific hypothesis generation. Specifically, we focus on the extraction of adverse drug events (i.e., side effects) and coping strategies for dealing with adverse drug events. Show less
We present an extensive study of methods for exactly solving stochastic constraint (optimisation) problems (SCPs) in network analysis. These problems are prevalent in science, governance and... Show moreWe present an extensive study of methods for exactly solving stochastic constraint (optimisation) problems (SCPs) in network analysis. These problems are prevalent in science, governance and industry. Both our proposed solving methods aim to strike a good balance between convenience, generality, and speed. The first method we study is generic and decomposes stochastic constraints into a multitude of smaller local constraints that are solved using a constraint programming (CP) or mixed-integer programming (MIP) solver. However, many SCPs are formulated on probability distributions with a monotonic property, meaning that adding a positive decision to a partial solution to the problem cannot cause a decrease in solution quality. The second method is specifically designed for solving global stochastic constraints on monotonic probability distributions (SCMDs) in CP. Both methods use knowledge compilation to obtain a decision diagram encoding of the relevant probability distributions, where we focus on ordered binary decision diagrams (OBDDs). We discuss theoretical advantages and disadvantages of these methods and evaluate them experimentally. We conclude that, while the decomposition method is easy to implement and can be used to solve and SCP, the global stochastic constraint solves problems faster, and is still widely applicable due to the prevalence of monotonicity in real-world problems. Show less
