One of the major challenges we face with ethical AI today is developing computational systems whose reasoning and behaviour are provably aligned with human values. Human values, however, are notorious for being ambiguous, contradictory and ever-changing. In order to bridge this gap, and get us closer to the situation where we can formally reason about implementing values into AI, this paper presents a formal representation of values, grounded in the social sciences. We use this formal representation to articulate the key challenges for achieving value-aligned behaviour in multiagent systems (MAS) and a research roadmap for addressing them.

Value alignment in AI has emerged as one of the basic principles that should govern autonomous AI systems. It essentially states that a system's goals and behaviour should be aligned with human values. But how to ensure value alignment? In this paper we first provide a formal model to represent values through preferences and ways to compute value aggregations; i.e. preferences with respect to a group of agents and/or preferences with respect to sets of values. Value alignment is then defined, and computed, for a given norm with respect to a given value through the increase/decrease that it results in the preferences of future states of the world. We focus on norms as it is norms that govern behaviour, and as such, the alignment of a given system with a given value will be dictated by the norms the system follows.

BRUSSELS – (Thomson Reuters Foundation) – As climate change intensifies the devastation from storms, wildfires and droughts, artificial intelligence (AI) and digital tools are increasingly being seen as a way to predict and limit its impacts. Governments, tech firms and investors are showing growing interest in machine-based learning systems that use algorithms to identify patterns in data sets and make predictions, recommendations or decisions in real or virtual settings. In June, the Rise Fund, an impact investing arm of private equity firm TPG, invested $100 million in a data and AI-driven "nowcasting" system devised by Kentucky-based startup Climavision to predict weather patterns with granular accuracy. And an intergovernmental roadmap on AI's role in fighting global warming is due to launch at November's COP26 climate summit in Scotland. But AI can also be highly energy-intensive and environmentally damaging, say critics who warn that the tech could be a costly distraction from more effective ways of tackling climate change.

Virginia Dignum is a Professor in the Dept. of Computing Science of Umeå University, where she leads the Social and Ethical Artificial Intelligence research group. Prior to that she was an Associate Professor in the Faculty of Technology, Policy and Management of Delft University of Technology. She received a PhD in 2004 from Utrecht University, before that she worked for 12 years in consultancy and system development in the areas of expert systems and knowledge management. Her research focuses on the complex interconnections and interdependencies between people, organizations, and technology. Prof. Dignum is actively involved in international initiatives on policy and strategy guidelines for AI research and applications, she is a member of the European Commission High-Level Expert Group on Artificial Intelligence, the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, the TU Delft Design for Values Institute, the European forum AI4People, the Responsible Robotics Foundation, the Dutch AI Alliance on AI (ALLAI-NL), and the ADA-AI Foundation.

Our routines play an important role in a wide range of social challenges such as climate change, disease outbreaks and coordinating staff and patients in a hospital. To use agent-based simulations (ABS) to understand the role of routines in social challenges we need an agent framework that integrates routines. This paper provides the domain-independent Social Practice Agent (SoPrA) framework that satisfies requirements from the literature to simulate our routines. By choosing the appropriate concepts from the literature on agent theory, social psychology and social practice theory we ensure SoPrA correctly depicts current evidence on routines. By creating a consistent, modular and parsimonious framework suitable for multiple domains we enhance the usability of SoPrA. SoPrA provides ABS researchers with a conceptual, formal and computational framework to simulate routines and gain new insights into social systems.

As artificial intelligence is being used to solve problems in healthcare, agriculture, weather prediction and more, scientists and engineers are investigating how AI could be used to fight climate change. AI algorithms could indeed be used to build better climate models and determine more efficient methods of reducing CO2 emissions, but AI itself often requires substantial computing power and therefore consumes a lot of energy. Is it possible to reduce the amount of energy consumed by AI and improve its effectiveness when it comes to fighting climate change? Virginia Dignum, an ethical artificial intelligence professor at the Umeå University in Sweden, was recently interviewed by Horizon Magazine. Dignum explained that AI can have a large environmental footprint that can go unexamined.

AI is changing the way we work, live and solve challenges. It can improve healthcare, protect elephants from poachers, and work out how broadband should be distributed. But it could be most valuable as a range of applications helping humanity fight our biggest threat – climate change. AI can strengthen climate predictions, enable smarter decision-making for decarbonising industries from building to transport, and work out how to allocate renewable energy. AI's relevance as a climate change fighting tool comes at a time when there are increasing ethical concerns linked largely to a data-hungry form of the technology called machine learning, where computer systems analyse patterns in existing data to make predictions and decisions.

Artificial intelligence (AI) technology can help us fight climate change--but it also comes at a cost to the planet. To truly benefit from the technology's climate solutions, we also need a better understanding of AI's growing carbon footprint, say researchers. AI is changing the way we work, live and solve challenges. It can improve healthcare, protect elephants from poachers, and work out how broadband should be distributed. But it could be most valuable as a range of applications helping humanity fight our biggest threat--climate change.

Artificial Intelligence (AI) applications are being used to predict and assess behaviour in multiple domains, such as criminal justice and consumer finance, which directly affect human well-being. However, if AI is to improve people's lives, then people must be able to trust AI, which means being able to understand what the system is doing and why. Even though transparency is often seen as the requirement in this case, realistically it might not always be possible or desirable, whereas the need to ensure that the system operates within set moral bounds remains. In this paper, we present an approach to evaluate the moral bounds of an AI system based on the monitoring of its inputs and outputs. We place a "glass box" around the system by mapping moral values into explicit verifiable norms that constrain inputs and outputs, in such a way that if these remain within the box we can guarantee that the system adheres to the value. The focus on inputs and outputs allows for the verification and comparison of vastly different intelligent systems; from deep neural networks to agent-based systems. The explicit transformation of abstract moral values into concrete norms brings great benefits in terms of explainability; stakeholders know exactly how the system is interpreting and employing relevant abstract moral human values and calibrate their trust accordingly. Moreover, by operating at a higher level we can check the compliance of the system with different interpretations of the same value. These advantages will have an impact on the well-being of AI systems users at large, building their trust and providing them with concrete knowledge on how systems adhere to moral values.

Artificial intelligence needs transparency so humans can hold it to account, a researcher has claimed. Virginia Dignum, associate professor at the Delft University of Technology, told an audience at New York University on Friday that if we don't understand why machines act the way they do, we won't be able to judge their decisions. Dignum cited a story by David Berreby, a science writer and researcher, that was published in Psychology Today: "Evidence suggests that when people work with machines, they feel less sense of agency than they do when they work alone or with other people." The trolley problem, Dignum explained, is an area where people may place blind faith in a machine to choose the right outcome. The question is whether to switch the lever on a hypothetical runaway train so that it kills one person instead of five.