This article reports the findings of AI4People, a year-long initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations – to assess, to develop, to incentivise, and to support good AI – which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.

Evidence-driven agent-based modeling plays a useful part in understanding social phenomena. By integrating social-cognitive theories in our agent models, we bear evidence from social and psychological studies on our models for human decision-making. Social practice theory (SPT) provides a socio-cognitive theory that emphasizes three empirically and theoretically grounded aspects of behavior: habituality, sociality, and interconnectivity. Previous work has emphasized the importance of SPT for agents, has made abstract models of SPT, or used SPT to study energy systems. This article provides a set of requirements for integrating SPT in agent models and an evaluation of 11 current agent models with respect to these requirements. We find that current agent models do not fully capture habituality, sociality, or interconnectivity, nor is there a model that aims to integrate all three aspects. For example, current models do not support context-dependent habits, use a comprehensive set of collective concepts, and support hierarchies of activities. Our evaluation allows researchers to pick one of the current agent models depending on their needs regarding habituality, sociality, and interconnectivity. Furthermore, this article shows the usefulness of an agent model that integrates SPT and provides requirements that help modelers to achieve this model.

Accountability and responsibility are key concepts in the academic and societal debate on Autonomous Weapon Systems, but these notions are often used as high-level overarching constructs and are not operationalised to be useful in practice. “Meaningful Human Control” is often mentioned as a requirement for the deployment of Autonomous Weapon Systems, but a common definition of what this notion means in practice, and a clear understanding of its relation with responsibility and accountability is also lacking. In this paper, we present a definition of these concepts and describe the relations between accountability, responsibility, control and oversight in order to show how these notions are distinct but also connected. We focus on accountability as a particular form of responsibility—the obligation to explain one’s action to a forum—and we present three ways in which the introduction of Autonomous Weapon Systems may create “accountability gaps”. We propose a Framework for Comprehensive Human Oversight based on an engineering, socio-technical and governance perspective on control. Our main claim is that combining the control mechanisms at technical, socio-technical and governance levels will lead to comprehensive human oversight over Autonomous Weapon Systems which may ensure solid controllability and accountability for the behaviour of Autonomous Weapon Systems. Finally, we give an overview of the military control instruments that are currently used in the Netherlands and show the applicability of the comprehensive human oversight Framework to Autonomous Weapon Systems. Our analysis reveals two main gaps in the current control mechanisms as applied to Autonomous Weapon Systems. We have identified three first options as future work for the design of a control mechanism, one in the technological layer, one in the socio-technical layer and one the governance layer, in order to achieve comprehensive human oversight and ensure accountability over Autonomous Weapon Systems.

Rumour is a collective emergent phenomenon with a potential for provoking a crisis. Modelling approaches have been deployed since five decades ago; however, the focus was mostly on epidemic behaviour of the rumours which does not take into account the differences between agents. We use social practice theory to model agent decision-making in organizational rumourmongering. Such an approach provides us with an opportunity to model rumourmongering agents with a layer of cognitive realism and study the impacts of various intervention strategies for prevention and control of rumours in organizations.

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.

This paper describes a framework for ethical analysis of the practice of computer Modeling & Simulation (M&S). Each of the authors presents a computational model as a case study and offers an ethical analysis by applying the philosophical, scientific, and practical components of the framework. Each author also provides a constructive response to the other case studies. The paper concludes with a summary of guidelines for using this ethical framework when preparing, executing, and analyzing M&S activities. Our hope is that this collaborative engagement will encourage others to join a rich and ongoing conversation about the ethics of M&S.

Stakeholder participation is a requirement for environmental decision-making in the European Union. Despite this, numerous instances can be seen in water governance in which stakeholders feel undervalued and unheard, thereby creating unfavourable procedural outcomes, resistance and conflict. In this article, we propose that a process of early-stage deliberation constructed around the values of the stakeholders involved can reduce, and even prevent such conflicts. We suggest that if values that stakeholders perceive as relevant can be identified and discussed as part of the deliberation process then (1) stakeholder preferences can change, and (2) participants can develop a mutual understanding of each other’s values and perspectives. To explore these propositions, facilitated workshops were conducted at two Dutch water institutes, based around the topics of land subsidence and the pharmaceutical contamination of water systems. Participants deliberated on values that they considered relevant. The results suggest that mutual understanding of stakeholders’ perspectives increases as a result of value-based deliberation.

To better understand the impact of deliberations during participatory policymaking events, we introduce and explore the concept of group proximity. An example of such events is citizens' summits, during which many parallel groups deliberate on solutions for a policy issue. At the summit that was studied, each group followed a value deliberation process with the aim to increase mutual understanding among participants. They were asked to rank the solutions in their order of preference before and after the deliberation. From these rankings, group proximity can be calculated with a rank correlation, enabling a precise comparison of participants' preferences in each deliberative group. High group proximity indicates very similar rankings in a deliberative group, while low group proximity demonstrates the opposite. Comparing group proximity of the before and after rankings shows if a group ranked convergent, unchanged or divergent. This measure allows for a quantitative analysis of early-stage public policymaking processes.

In the political debate on Autonomous Weapon Systems strong views and opinions are voiced, but empirical research to support these opinions is lacking. Insight into which moral values are related to the deployment of Autonomous Weapon Systems is missing. We describe the empirical results of two studies on moral values regarding Autonomous Weapon Systems that aim to understand the perception of people pertaining to the introduction of Autonomous Weapon Systems. One study consists of a sample of military personnel of the Dutch Ministry of Defense and the second study contains a sample of civilians. The results indicate both groups are more anxious about the deployment of Autonomous Weapon Systems than about the deployment of Human Operated drones, and that they perceive Autonomous Weapon Systems to have less respect for the dignity of human life. The concerns for Autonomous Weapon Systems creating new kinds of psychological and moral harm is very present in the public debate, and this is in our opinion one element that deserves to be carefully considered in future debates on the ethics of the design and deployment of Autonomous Weapon Systems. The results of these studies reveal a common ground regarding the moral values of human dignity and anxiety pertaining the introduction of Autonomous Weapon Systems which could further the ethical debate.

Social simulations gain strength when agent behaviour can (1) represent human behaviour and (2) be explained in understandable terms. Agents with values and norms lead to simulation results that meet human needs for explanations, but have not been tested on their ability to reproduce human behaviour. This paper compares empirical data on human behaviour to simulated data on agents with values and norms in a psychological experiment on dividing money: the ultimatum game. We find that our agent model with values and norms produces aggregate behaviour that falls within the 95% confidence interval wherein human behaviour lies more often than other tested agent models. A main insight is that values serve as a static component in agent behaviour, whereas norms serve as a dynamic component.

Ethics by Design concerns the methods, algorithms and tools needed to endow autonomous agents with the capability to reason about the ethical aspects of their decisions, and the methods, tools and formalisms to guarantee that an agent's behavior remains within given moral bounds. In this context some questions arise: How and to what extent can agents understand the social reality in which they operate, and the other intelligences (AI, animals and humans) with which they co-exist? What are the ethical concerns in the emerging new forms of society, and how do we ensure the human dimension is upheld in interactions and decisions by autonomous agents?. But overall, the central question is: "Can we, and should we, build ethically-aware agents?" This paper presents initial conclusions from the thematic day of the same name held at PRIMA2017, on October 2017.

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.

Understanding the social contexts in which actions and interactions take place is of utmost importance for planning one’s goals and activities. People use social practices as means to make sense of their environment, assessing how that context relates to past, common experiences, culture and capabilities. Social practices can therefore simplify deliberation and planning in complex contexts. In the context of patient-centered planning, hospitals seek means to ensure that patients and their families are at the center of decisions and planning of the healthcare processes. This requires on one hand that patients are aware of the practices being in place at the hospital and on the other hand that hospitals have the means to evaluate and adapt current practices to the needs of the patients. In this paper we apply a framework for formalizing social practices of an organization to an emergency department that carries out patient-centered planning. We indicate how such a formalization can be used to answer operational queries about the expected outcome of operational actions.