The healthcare industry faces a series of pressing challenges concerning the implementation of safe and ethical artificial intelligence. But there are resources available for health care teams to help support safe and ethical AI to start them off and set a common foundation for the discussion. As Jessica Newman, director of the AI Security Initiative at the Center for Long Term Cybersecurity at the University of California, Berkeley, explained there are four common types of AI technologies being implemented in healthcare today. The first is predictive and prescriptive analytics, used in healthcare applications like precision medicine, where a system might be used to predict the most successful treatment based upon particular attributes and context. The second type of AI is robotic process automation, or RPA, which is designed to automate and replicate relatively simple, rule based administrative processes and health care.

Recent advances in AI and machine learning have helped create new tools and products and pushed scientific knowledge forward. They also bring along risks and complexities that we don't yet fully understand--and these range from the hyperlocal (for example, companies perpetuating bias in their AI-powered hiring processes) to the existential (a general artificial intelligence wiping out life as we know it). It can be hard to keep up with the state of the field, let alone understand the deep implications of new research. But we're here to help: the second season of the TDS Podcast, hosted by Jeremie Harris, focuses on these emerging questions around AI, safety, and ethics. While we encourage you to explore the entire season (now with more than two-dozen episodes, and counting!), You can approach this selection in order, or mix-and-match as you see fit.

The convergence of the availability of a vast amount of big data, the speed and stretch of cloud computing platforms, and the advancement of sophisticated machine learning algorithms have given birth to an array of innovations in Artificial Intelligence (AI). Other applications that benefit from the implementation of AI systems in the public sector include food supply chain, energy, and environmental management. Indeed, the benefits that AI systems bring to society are grand, and so are the challenges and worries. The evolving technologies learning curve implies miscalculations and mistakes, resulting in unanticipated harmful impacts. We are living in times when it is paramount that the possibility of harm in AI systems has to be recognized and addressed quickly. Thus, identifying the potential risks caused by AI systems means a plan of measures to counteract them has to be adopted as soon as possible.

Today the UK government publishes'Using artificial intelligence in the public sector,' an initiative led by the Office for Artificial Intelligence (OAI) and the Government Digital Service (GDS), with The Alan Turing Institute's public policy programme contributing guidance on AI ethics and safety. The new guide states that several public sector organisations are already successfully using AI for tasks ranging from fraud detection to answering customer queries. It explains how the potential uses for AI in the public sector are significant, but must be balanced with ethical, fairness and safety considerations. These ethical and safety issues are laid out in full in a section of the guide titled'Understanding artificial intelligence ethics and safety' by Dr David Leslie, Ethics Fellow in the Turing's public policy programme. This groundbreaking work is the most comprehensive guidance on the topic of AI ethics and safety in the public sector to date.