ABSTRACT. Recent developments in AI, Machine Learning and Robotics have raised concerns about the ethical consequences of both academic and industrial AI research. Leading academics, businessmen and politicians have voiced an increasing number of questions about the consequences of AI not only over people, but also on the large-scale consequences on the the future of work and employment, its social consequences and the sustainability of the planet. In this work, we analyse the use and the occurrence of ethics-related research in leading AI, machine learning and robotics venues. In order to do so we perform long term, historical corpus-based analyses on a large number of flagship conferences and journals. Our experiments identify the prominence of ethics-related terms in published papers and presents several statistics on related topics. Finally, this research provides quantitative evidence on the pressing ethical concerns of the AI community.

ABSTRACT. Replaceability is a form of generalized substitutability whose features make it potentially of great importance for problem simplification. It differs from simple substitutability in that it only requires that substitutable values exist for every solution containing a given value without requiring that the former always be the same. This is also the most general form of substitutability that allows inferences from local to global versions of this property. Building on earlier work, this study first establishes that algorithms for localized replaceability (called consistent neighbourhood replaceability or CNR algorithms) based on all-solutions neighbourhood search outperform other replaceability algorithms by several orders of magnitude. It also examines the relative effectiveness of different forms of depth-first CNR algorithms. Secondly, it demonstrates an apparent complexity ridge, which does not occur at the same place in the problem space as the complexity areas for consistency or full search algorithms. Thirdly, it continues the study of methods for inferring replaceability in structured problems in order to improve efficiency. This includes correcting an oversight in earlier work and extending the formal analysis. It is also shown that some strategies for inferring replaceable values can be extended to disjunctive constraints in scheduling problems.

ABSTRACT. With the current surge of interest in ethics in AI, we present our position with respect to these challenges. Our proposal, responsible technologies, aims to (1) address a number of the ethical challenges put forward in AI, and (2) provide the first building blocks towards the development of ethical AI systems. The current discussion on how to address ethics in AI usually focuses on issues like policies, education, or research culture. There is no computational method yet mature enough to address ethics in AI. We break ground by proposing new methods and tools, underpinned by multidisciplinary research, that can make humans and machines understand their respective dynamic goals while strictly abiding by the values that inspire our societies. This position paper presents our plan of work for the development of responsible technologies that embed values within technology through what we refer to as ethics by construction.