AI ethics on Tuesday, 1 May 2018

BACKGROUND: Sequence data used in reconstructing phylogenetic trees may include various sources of error. Typically errors are detected at the sequence level, but when missed, the erroneous sequences often appear as unexpectedly long branches in the inferred phylogeny. RESULTS: We propose an automatic method to detect such errors. We build a phylogeny including all the data then detect sequences that artificially inflate the tree diameter. We formulate an optimization problem, called the k-shrin

Emerging combinations of artificial intelligence, big data, and the applications these enable are receiving significant media and policy attention. Much of the attention concerns privacy and other ethical issues. In our article, we suggest that what is needed now is a way to comprehensively understand these issues and find mechanisms of addressing them that involve stakeholders, including civil society, to ensure that these technologies' benefits outweigh their disadvantages. We suggest that the

Medical students are exposed to multiple factors during their academic and clinical study that have been shown to contribute to high levels of depression, anxiety, and stress. The purpose of this article was to explore the issue of depression in the medical student population, including prevalence, causes, and key issues, along with suggestions for early identification and support from one medical school in New Zealand. After establishing that the prevalence of depression is higher for medical s

Although different types of teams increasingly employ embodied physical action (EPA) robots as a collaborative technology to accomplish their work, we know very little about what makes such teams successful. This paper has two objectives: the first is to examine whether a team’s emotional attachment to its robots can lead to better team performance and viability; the second is to determine whether robot and team identification can promote a team’s emotional attachment to its robots. To achieve t