The Department of Computer Science, characterized by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison, is looking for a Doctoral student in computing science with a focus on responsible AI with learning from multiple representations. The Department of Computing science has been growing rapidly in recent years where focus on an inclusive and bottom-up driven environment are key elements in our sustainable growth. The 60 Doctoral students within the department consists of a diverse group from different nationalities, background and fields. If you work as a Doctoral student with us you receive the benefits of support in career development, networking, administrative and technical support functions along with good employment conditions. Is this interesting for you?

Two key components for using ML responsibly provide a prudent "start here" for organizations: model explainability and data transparency. The inability to explain why a model arrived at a particular result presents a level of risk in nearly every industry. In some areas, like healthcare, the stakes are particularly high when a model could be presenting a recommendation for patient care. In financial services, regulators may need to know why a lender is making a loan. Data transparency can ensure there is no unfair or unintended bias in the training data sets used to build the model, which can lead to disparate impact for protected classes – and consumers have what is increasingly a legally protected right to know how their data is being used.

Anyone who runs a business knows that one of the hardest things to do is accuse a customer of malfeasance. That's why, before members of Scandinavian Airlines' (SAS) fraud detection unit accuse a customer of attempting to scam the carrier's loyalty points program, the detectives need confidence that their case is solid. "It would hurt us even more if we accidentally managed to say that something is fraud, but it isn't," said Daniel Engberg, head of data analytics and artificial intelligence for SAS, which is headquartered in Stockholm, Sweden. The airline is currently flying a reduced schedule with limited in-flight services to help slow the spread of COVID-19, the disease caused by the novel coronavirus. Before the restrictions, SAS handled more than 800 departures per day and 30 million passengers per year.

Progress in ML for health care to date has been limited by the lack of well-defined questions and a dearth of annotated datasets. Many ML researchers remain focused on questions for which annotations are readily available, without necessarily questioning the clinical relevance of the problems and their solutions. For example, a popular benchmark challenge in the community focuses on predicting in-hospital mortality on the basis of data collected during the first 48 hours after admission to the intensive care unit4. Clearly annotated data are publicly available, and in recent years, performance on this task has approached an area under the curve (Box 1) of 0.9 (ref. However, assessing clinical utility requires careful evaluation against the scenario in which the model will be used.