Artificial Intelligence (AI) applications are being used to predict and assess behaviour in multiple domains which directly affect human well-being. However, if AI is to improve people’s lives, then people must be able to trust it, by being able to understand what the system is doing and why. Although transparency is often seen as the requirementin this case, realistically it might not always be possible, 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 interms 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.

Artificial Intelligence (AI) applications are beingused 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 be deployed safely, then people need to understand how the system is interpreting and whether it is adhering to the relevant moral values. Even though transparency is often seen as the requirement in this case, realistically it might notalways be possible or desirable, whereas the needto 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 contextual 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(s) in a specific context. 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–whereas by making the context explicit we exposethe different perspectives and frameworks that are taken into account when subsuming moral values into specific norms and functionalities. We present a modal logic formalisation of the Glass Box approach which is domain-agnostic, implementable, and expandable.

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.

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.

The integration of social robots in human societies requires that they are capable to take decisions that may affect the lives of people around them. In order to ensure that these robots will behave according to shared ethical principles, an important shift in the design and development of social robots is needed, one where the main goal is improving ethical transparency rather than technical performance, and placing human values at the core of robot designs. In this abstract, we discuss the concept of ethical decision making and how to achieve trust according to the principles of Autonomy, Responsibility and Transparency (ART).

Autonomous Weapon Systems (AWS) are said to become the third revolution in warfare. These systems raise many questions and concerns that demand in-depth research on ethical and moral responsibility. Ethical decisionmaking is studied in related fields like Autonomous Vehicles and Human Oper‐ ated drones, but not yet fully extended to the deployment of AWS and research on moral judgement is lacking. In this paper, we propose design requirements for a Moral Machine (Similar to http://moralmachine.mit.edu/) for Autonomous Weapons to conduct a large-scale study of the moral judgement of people regarding the deployment of this type of weapon. We ran an online survey to get a first impression on the importance of six variables that will be implemented in a proof-of-concept of a Moral Machine for Autonomous Weapons and describe a scenario containing these six variables. The platform will enable large-scale randomized controlled experiments and generate knowledge about people’s feel‐ ings concerning this type of weapons. The next steps of our study include devel‐ opment and testing of the design before the prototype is upscaled to a Massive Online Experiment

Cybersecurity is a growing concern for organisations. Decision-making concerning responses to threats will involve making choices from a number of possible options. The choice made will often depend on the values held by the organisation and/or the individual/s making the decisions. To address the issue of how to capture ethical dilemmas and the values associated with the choices they raise, we propose the use of the Ripple Down Rules (RDR) incremental knowledge acquisition method. We provide an example of how the RDR knowledge can be acquired in the context of a value tree and then translated into a goal-plan tree that can be used by a BDI agent towards supporting the creation of ethical dilemmas that could be used for what-if analysis or training. We also discuss the AORTA framework can be extended with values to allow the BDI cognitive agent to take into consideration organisational norms and policies in its decision-making. 

Current developments in governance and policy setting are challenging traditional top-down models of decision-making. Whereas, on the one hand, citizens are increasingly demanding and expected to participate directly on governance questions, social networking platforms are, on the other hand, increasingly providing podia for the spread of unfounded, extremist and/or harmful ideas. Participatory deliberation is a form of democratic policy making in which deliberation is central to decision-making using both consensus decision-making and majority rule. However, by definition, it will lead to socially accepted results rather than ensuring the moral acceptability of the result. In fact, participation per se offers no guidance regarding the ethics of the decisions taken, nor does it provide means to evaluate alternatives in terms of their moral "quality." This article proposes an open participatory model, Massive Open Online Deliberation (MOOD), that can be used to solve some of the current policy authority deficits. MOOD taps on individual understanding and opinions by harnessing open, participatory, crowd-sourced, and wiki-like methodologies, effectively producing collective judgements regarding complex political and social issues in real time. MOOD oilers the opportunity for people to develop and draft collective judgements on complex issues and crises in real time. MOOD is based on the concept of Ethics by Participation, a formalized and guided process of moral deliberation that extends deliberative democracy platforms to identify morally acceptable outcomes and enhance critical thinking and reflection among participants.