Towards a Future Reallocation of Work between Humans and Machines – Taxonomy of Tasks and Interaction Types in the Context of Machine Learning

In today’s race for competitive advantages, more and more companies implement innovations in artificial intelligence and machine learning (ML). Although these machines take over tasks that have been executed by humans, they will not make human workforce obsolete. To leverage the potentials of ML, collaboration between humans and machines is necessary. Before collaboration processes can be developed, a classification of tasks in the field of ML is needed. Therefore, we present a taxonomy for the classification of tasks due to their complexity and the type of interaction. To derive insights about typical tasks and task-complexity, we conducted a literature review as well as a focus group workshop. We identified three levels of task-complexity and three types of interactions. Connecting them reveals three generic types of tasks. We provide prescriptive knowledge inherent in the task/interaction-taxonomy

Social context, interaction and expectation play a role in alcohol use amongst Australian and Danish women aged 50 to 70 years

© 2020 Taylor & Francis Group, LLC. In this study, researchers explored the relationship between alcohol use and life transitions among women aged 50 to 70 years in Australia and Denmark. Data were collected via semi-structured interviews of 49 women, with thematic analysis indicating that alcohol use is a normal and accepted activity among Australian and Danish women. Alcohol use was influenced by women’s specific life transitions including their retirement status. Using alcohol as a crutch was not a legitimate story, but women found it acceptable to temporarily manage stress. The researchers provided insight to women’s perceptions on drinking that may guide future international public health strategies for this group