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
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.
Neural networks (NNs) and deep learning (DL) currently provide the best solutions to many problems in image recognition, speech recognition, natural language processing, control and precision health. NN and DL make the artificial intelligence (AI) much closer to human thinking modes. However, there are many open problems related to DL in NN, e.g.: convergence, learning efficiency, optimality, multi-dimensional learning, on-line adaptation. This requires to create new algorithms and analysis methods. Practical applications both require and stimulate this development.