Explainability, the capability of an artificial intelligence system (AIS) to explain its outcomes in a manner that is comprehensible to human beings at an acceptable level, has been deemed essential for critical sectors, such as healthcare. Is it really the case? In this perspective, we consider two extreme cases, ``Oracle'' (without explainability) versus ``AI Colleague'' (with explainability) for a thorough analysis. We discuss how the level of automation and explainability of AIS can affect the determination of liability among the medical practitioner/facility and manufacturer of AIS. We argue that explainability plays a crucial role in setting a responsibility framework in healthcare, from a legal standpoint, to shape the behavior of all involved parties and mitigate the risk of potential defensive medicine practices.

Artificial intelligence (AI) is quickly making inroads into medical practice, especially in forms that rely on machine learning, with a mix of hope and hype.1 Multiple AI-based products have now been approved or cleared by the US Food and Drug Administration (FDA), and health systems and hospitals are increasingly deploying AI-based systems.2 For example, medical AI can support clinical decisions, such as recommending drugs or dosages or interpreting radiological images.2 One key difference from most traditional clinical decision support software is that some medical AI may communicate results or recommendations to the care team without being able to communicate the underlying reasons for those results.3

We have entered the "Data Decade," says RSA Security* CTO Dr. Zulfikar Ramzan. Here are three "Data Decade" trends that will reshape how we talk about, approach, and manage data. The concept of "data" has been embedded in our lives and across every industry since the beginning of time – in various incarnations and forms. In the security and risk industry, data is the lifeblood for how you assess business risk and respond to threats. As we embark on this new decade, I believe data will become the foremost currency that drives the digital economy.

Artificial intelligence (AI) is quickly making inroads into medical practice, especially in forms that rely on machine learning, with a mix of hope and hype.1 Multiple AI-based products have now been approved or cleared by the US Food and Drug Administration (FDA), and health systems and hospitals are increasingly deploying AI-based systems.2 For example, medical AI can support clinical decisions, such as recommending drugs or dosages or interpreting radiological images.2 One key difference from most traditional clinical decision support software is that some medical AI may communicate results or recommendations to the care team without being able to communicate the underlying reasons for those results.3 Medical AI may be trained in inappropriate environments, using imperfect techniques, or on incomplete data. Even when algorithms are trained as well as possible, they may, for example, miss a tumor in a radiological image or suggest the incorrect dose for a drug or an inappropriate drug.

The EU is not really giving robots human rights; but some of its proposals are cause for concern. James Vincent on the Verge provides a sensible commentary which corrects the rather alarming headlines generated by the European Parliament draft report issued recently. Actually there are several reasons to keep calm. It is said that in the early days the Eurocrats had to quickly suppress an explanatory leaflet which described the European Economic Community as a bus. The Commission was the engine, the Council was the driver; and the Parliament… was a passenger.