We can live with many of the uncertainties of big data for now, with the hope that its benefits will outweigh its harms, but we shouldn't blind ourselves to the possible irreversibility of changes-whether good or bad-to society. The first Web extra at http://youtu.be/24czULRCI9c is an audio recording in which Katina Michael at the University of Wollongong discusses the June 2013 Computer magazine special issue on "Big Data: New Opportunities and New Challenges," introducing the special issue, the guest editors, the authors, the articles, and the IEEE Society on Social Implications of Technology (SSIT). The second Web extra at http://youtu.be/9zpFqEDydDA is an audio recording in which Katina Michael at the University of Wollongong talks about the IEEE Society on the Social Implications of Technology (SSIT), IEEE Technology and Society (T&S) magazine, and the International Symposium on Technology and Society (ISTAS). The third Web extra at http://youtu.be/mn_9YHV2RGQis an audio recording in which Katina Michael at the University of Wollongong discusses how we can live with many of the uncertainties of big data for now, with the hope that its benefits will outweigh its harms, but we shouldnít blind ourselves to the possible irreversibility of changes-whether good or bad-to society.

The so-called fourth industrial revolution and its economic and societal implications are no longer solely an academic concern, but a matter for political as well as public debate. Characterized as the convergence of robotics, AI, autonomous systems and information technology – or cyberphysical systems – the fourth industrial revolution was the focus of the World Economic Forum, at Davos, in 2016 [1] . Also in 2016 the US White House initiated a series of public workshops on artificial intelligence (AI) and the creation of an interagency working group, and the European Parliament Committee for Legal Affairs published a draft report with recommendations to the Commission on Civil Law Rules on Robotics.

One of the major criticisms of Artificial Intelligence is its lack of explainability. A claim is made by many critics that without knowing how an AI may derive a result or come to a given conclusion, it is impossible to trust in its outcomes. This problem is especially concerning when AI-based systems and applications fail to perform their tasks successfully. In this Special Issue Editorial, we focus on two main areas, explainable AI (XAI) and accuracy, and how both dimensions are critical to building trustworthy systems. We review prominent XAI design themes, leading to a reframing of the design and development effort that highlights the significance of the human, thereby demonstrating the importance of human-centered AI (HCAI). The HCAI approach advocates for a range of deliberate design-related decisions, such as those pertaining to multi-stakeholder engagement and the dissolving of disciplinary boundaries. This enables the consideration and integration of deep interdisciplinary knowledge, as evidenced in our example of social cognitive approaches to AI design. This Editorial then presents a discussion on ways forward, underscoring the value of a balanced approach to assessing the opportunities, risks and responsibilities associated with AI design. We conclude by presenting papers in the Special Issue and their contribution, pointing to future research endeavors.