The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.
Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.
The complete book in a single file.
Artificial intelligence is already ubiquitous, and is increasingly being used to autonomously make ever more consequential decisions. However, there has been relatively little research into the consequences for equity of the use of narrow AI and automated decision systems in medicine and public health. A narrative review using a hermeneutic approach was undertaken to explore current and future uses of AI in medicine and public health, issues that have emerged, and longer-term implications for population health. Accounts in the literature reveal a tremendous expectation on AI to transform medical and public health practices, especially regarding precision medicine and precision public health. Automated decisions being made about disease detection, diagnosis, treatment, and health funding allocation have significant consequences for individual and population health and wellbeing. Meanwhile, it is evident that issues of bias, incontestability, and erosion of privacy have emerged in sensitive domains where narrow AI and automated decision systems are in common use. As the use of automated decision systems expands, it is probable that these same issues will manifest widely in medicine and public health applications. Bias, incontestability, and erosion of privacy are mechanisms by which existing social, economic and health disparities are perpetuated and amplified. The implication is that there is a significant risk that use of automated decision systems in health will exacerbate existing population health inequities. The industrial scale and rapidity with which automated decision systems can be applied to whole populations heightens the risk to population health equity. There is a need therefore to design and implement automated decision systems with care, monitor their impact over time, and develop capacities to respond to issues as they emerge.