Research article: Soft ethics, the governance of the digital and the General Data Protection Regulation Luciano Floridi Research article: The fallacy of inscrutability Joshua A. Kroll Opinion piece: Constitutional democracy and technology in the age of artificial intelligence Paul Nemitz Research article: Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making Vidushi Marda Research article: Algorithms that remember: model inversion attacks and data protection law Michael Veale, Reuben Binns, Lilian Edwards Research article: Ethical governance is essential to building trust in robotics and artificial intelligence systems Alan F. T. Winfield, Marina Jirotka Research article: Apples, oranges, robots: four misunderstandings in today's debate on the legal status of AI systems Ugo Pagallo Research article: Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem Jaron Harambam, Natali Helberger, Joris van Hoboken
At VentureBeat, there's a constant internal conversation about how we can keep finding better ways to meet our mission of covering transformative technology. "AI" is usually the key word in those conversations, which increasingly revolve around issues and topics that require deeper thought, greater attention, and accountability from the Fourth Estate. Each one is composed of features that explore a central topic from a variety of angles. Early next week, we'll be publishing our first -- an examination of power in AI. There's been much ink spilled about AI ethics, and for good reason.
The last few years have seen a surge in interest in the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to engineering their knowledge bases. This special issue reviews recent research in Bayesian problem-solving techniques. The articles cover the topics of inference in Bayesian networks, decision-theoretic planning, and qualitative decision theory. Here, I provide a brief introduction to Bayesian networks and then cover applications of Bayesian problem-solving techniques, knowledge-based model construction and structured representations, and the learning of graphic probability models.