Francesca Rossi, University of Padova

The future will see autonomous machines acting in the same environment as humans, in areas as diverse as driving, assistive technology, and health care. Think of self-driving cars, companion robots, and medical diagnosis support systems. We also believe that humans and machines will often need to work together and agree on common decisions. Thus hybrid collective decision making systems will be in great need. In this scenario, both machines and collective decision making systems should follow some form of moral values and ethical principles (appropriate to where they will act but always aligned to humans’), as well as safety constraints. In fact, humans would accept and trust more machines that behave as ethically as other humans in the same environment. Also, these principles would make it easier for machines to determine their actions and explain their behavior in terms understandable by humans. Moreover, often machines and humans will need to make decisions together, either through consensus or by reaching a compromise. This would be facilitated by shared moral values and ethical principles. We will study the embedding and learning of safety constraints, moral values, and ethical principles in collective decision making systems for societies of machines and humans.

The future will see autonomous agents acting in the same environment as humans, in areas as diverse as driving, assistive technology, and health care. In this scenario, collective decision making will be the norm. We will study the embedding of safety constraints, moral values, and ethical principles in agents, within the context of hybrid human/agents collective decision making. We will do that by adapting current logic-based modelling and reasoning frameworks, such as soft constraints, CP-nets, and constraint-based scheduling under uncertainty. For ethical principles, we will use constraints specifying the basic ethical “laws”, plus sophisticated prioritised and possibly context-dependent constraints over possible actions, equipped with a conflict resolution engine. To avoid reckless behavior in the face of uncertainty, we will bound the risk of violating these ethical laws. We will also replace preference aggregation with an appropriately developed constraint/value/ethics/preference fusion, an operation designed to ensure that agents’ preferences are consistent with the system’s safety constraints, the agents’ moral values, and the ethical principles of both individual agents and the collective decision making system. We will also develop approaches to learn ethical principles for artificial intelligent agents, as well as predict possible ethical violations.