During the COVID-19 crisis there have been many difficult decisions governments and other decision makers had to make. E.g. do we go for a total lock down or keep schools open? How many people and which people should be tested? Although there are many good models from e.g. epidemiologists on the spread of the virus under certain conditions, these models do not directly translate into the interventions that can be taken by government. Neither can these models contribute to understand the economic and/or social consequences of the interventions. However, effective and sustainable solutions need to take into account this combination of factors. In this paper, we propose an agent-based social simulation tool, ASSOCC, that supports decision makers understand possible consequences of policy interventions, but exploring the combined social, health and economic consequences of these interventions.

Power distribution network management must integrate with demand side management, alongside distributed energy resources, in order to meet sustainability, resilience, and economic challenges through a smart grid approach. This paper presents an implementation of the Universal Smart Energy Framework (USEF) through a multiagent system and a novel semantic web ontology, which aligns and enriches relevant existing standards. USEF provides a common specification of the market processes and information exchange but does not specify the internal reasoning of the different roles involved. The authors explain the systematic design and development process from the requirements of the energy-flexibility value chain to software implementation. The underpinning ontology formalizes a domain perspective which is coherent with existing standards, and is sufficient for the agent-oriented implementation of the mentioned framework. As well as contributing this model as a web ontology artifact, the presented work utilizes metaprogramming to transform the domain model into a standard agent communication language ontology. The research reported in this paper is expected to lead towards efficient and scalable development of decision support and automation software for smart grids.

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The domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including content related to ethics. By using Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, this study uncovers topics in teaching ethics in AI courses and their trends related to where the courses are taught, by whom, and at what level of cognitive complexity and specificity according to Bloom’s taxonomy. In this exploratory study based on unsupervised machine learning, we analyzed a total of 166 courses: 116 from North American universities, 11 from Asia, 36 from Europe, and 10 from other regions. Based on this analysis, we were able to synthesize a model of teaching approaches, which we call BAG (Build, Assess, and Govern), that combines specific cognitive levels, course content topics, and disciplines affiliated with the department(s) in charge of the course. We critically assess the implications of this teaching paradigm and provide suggestions about how to move away from these practices. We challenge teaching practitioners and program coordinators to reflect on their usual procedures so that they may expand their methodology beyond the confines of stereotypical thought and traditional biases regarding what disciplines should teach and how. This article appears in the AI & Society track.