Machine learning has become one of the most emerging topics in a lot of research areas, such as pervasive and ubiquitous computing. Such computing applications always rely on the supervised learning approach to recognize user’s context before a suitable level of services are provided. However, since more and more users are involved in modern applications, the monitored data cannot be guaranteed to be always true due to wrong information. This may cause the mislabeling in machine learning and so affects the prediction. The goal of this Ph.D. thesis is to improve the data quality and solve the mislabeling problem caused by considering non-expert users. To achieve this goal, we propose a novel algorithm, called Skeptical Learning, aiming at interacting with the users and filtering out anomalies when an invalid input is monitored. This algorithm guarantees the machine to use the pre-known knowledge to check the availability of its own prediction as well as the label provided by the users. This thesis clarifies how we design this algorithm and makes three main contributions: (i.) we study the predictability of human behavior through the notion of personal context; (ii.)we design and develop Skeptical Learning as a paradigm to deal with the unreliability of users when providing non-confidential labels that describe their personal context; (iii.) we introduce an MCS platform where we implement Skeptical Learning on top of it to solve unreliable labels issue. Our evaluations have shown that Skeptical Learning could be widely used in pervasive and ubiquitous computing applications to better understand the quality of the data relying on the machine knowledge, and thus prevent mislabeling problem due to non-expert information.

The thesis investigates how one should determine the appropriate transparency of an information processing system from a receiver perspective. Research in the past has suggested that the model should be maximally transparent for what is labeled as ”high stake decisions”. Instead of motivating the choice of a model’s transparency on the non-rigorous criterion that the model contributes to a high stake decision, this thesis explores an alternative method. The suggested method involves that one should let the transparency depend on how well an explanation of the model’s output satisfies the purpose of an explanation. As a result, we do not have to bother if it is a high stake decision, we should instead make sure the model is sufficiently transparent to provide an explanation that satisfies the expressed purpose of an explanation.

Yes / Background: The efficacy of medicines depends on their accessibility and availability. Dedicated medicine shortage reporting systems (MSRS) have been set up in different countries, either mandatory or voluntary, following the recommendations of the World Health Organisation to ensure these. Objectives: To explore how the Medicine Shortages Reporting System (MSRS) can tackle medicine shortages through improved access and sustainability. Methods: Personnel directly involved in the reporting mechanisms for medicine shortages in eight (8) countries participated in semi-structured interviews. An interview protocol based on the Dynamic Capabilities View and Organisational Information Processing Theory (OIPT) was developed. It contained questions related to participant's views on the process involved in MSRS and how it was used to tackle shortages. Data were thematically analysed. Results: Three core elements were identified to influence MSRS's ability to tackle shortages and ensure sustainability; (1) the ability to identify what information requirements the reporting system needs, (2) identify information processing capabilities, and (3) the ability to match requirements and information processing capabilities through a dynamic capability decision-making process. The dynamic decision-making process involves reiteratively sensing shortages by understanding and validating information received. Conclusion: Building MSRS to tackle shortages for accessibility and sustainability is a systemic process that entails understanding the various elements and processes of MSRS. It includes defining medicine shortages, reconfiguring resources, defining accessibility and ensuring the system's sustainability. Our study provides insights into MSRS developed for mitigating medicine shortages and provides a framework for a sustainable MSRS. The findings extend the literature on medicine shortage management by identifying the various elements required to set up an MSRS. It also provides practical implications for countries that seek to establish MSRS to mitigate medicine shortages. Further studies could extend the number of participating countries to provide a clearer picture of the MSRS and how it can reduce medicine shortages. / This research is supported by the National Institute for Health and Care Research (NIHR) Yorkshire and Humber Patient Safety Research Collaboration (NIHR Yorkshire and Humber PSRC). Grant number - NIHR204293.