An introduction to explainable artificial intelligence with LIME and SHAP

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Albert Clapés i Sintes i Sergio Escalera Guerrero[en] Artificial intelligence (AI) and more specifically machine learning (ML) have shown their potential by approaching or even exceeding human levels of accuracy for a variety of real-world problems. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, creating a tradeoff between accuracy and interpretability. These models are known for being "black box" and opaque, which is especially problematic in industries like healthcare. Therefore, understanding the reasons behind predictions is crucial in establishing trust, which is fundamental if one plans to take action based on a prediction, or when deciding whether or not to implement a new model. Here is where explainable artificial intelligence (XAI) comes in by helping humans to comprehend and trust the results and output created by a machine learning model. This project is organised in 3 chapters with the aim of introducing the reader to the field of explainable artificial intelligence. Machine learning and some related concepts are introduced in the first chapter. The second chapter focuses on the theory of the random forest model in detail. Finally, in the third chapter, the theory behind two contemporary and influential XAI methods, LIME and SHAP, is formalised. Additionally, a public diabetes tabular dataset is used to illustrate an application of these two methods in the medical sector. The project concludes with a discussion of its possible future works

Twinning, a promising dynamic process to strengthen the agency of midwives

Twinning collaborations, where two groups work together cross-culturally on joint goals, are common worldwide. This research offers one of the first systematic examinations of the twinning process, and brought with it a new definition: ‘Twinning is a cross-cultural, reciprocal process where two groups of people work together to achieve joint goals’. Experts were consulted to find out what makes twinning successful and 25 critical success factors were found, the majority of which focused on equity. It was also found that cultural differences between the groups can both hinder and facilitate professional growth, depending on personal preparedness to bridge cultural differences. When twins can build relationships of trust, they tend to succeed better at collaborating and learning from each other. Co-funded by midwives4mothers, the Foundation for Special Facilities for Maternal Care and the Royal Dutch Organisation of Midwives (KNOV

Artificial Intelligence and Patient-Centered Decision-Making

Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient

Designing effective contracts within the buyer-seller context: a DEMATEL and ANP study

This study examines the factors that contribute to effective contract design within the context of buyer-seller relationship. Research streams on contract factors, supply chain factors, environmental factors, and competitive factors were reviewed to arrive at 18 contract factors. A hybrid model of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Hierarchy Process (ANP) analysed empirical data collected from 17 experts to weight the importance of contract factors. It was found that most important factors are, in order of significance: policies, supplier technology, force majeure, formality, relationship learning, buyer power, legal actions, liquidated damages, supplier power and partnership

Next challenges for adaptive learning systems

Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.</jats:p

Making a Shift in Educator Evaluation

The educator evaluation process can be a compliance task as well as an arduous process causing stress and anxiety for educators and their evaluators. The evaluation process in this suburban district is changing. Educators and evaluators are working together to create a new knowledge base and share it amongst their school community and others. Educators are being allowed voice and choice when determining how they will be evaluated and the areas in which they are going to focus their own personal growth. Teachers are becoming school-based experts on the topics that they are learning and researching. This has allowed for not only trust and genuine mutual respect to grow but also innovative practices to be fostered within the school community. Educator evaluation is changing, and it is becoming a tool that is offering our educators and schools new opportunities for self-reflection and growth