ETHICBOTS “crude” questions (how) is the ICT monitoring and use of personal data to be regulated? who is responsible for actions carried out by human-robot hybrid teams? can bionic implants be used to enhance physical and intellectual capabilities?

concerning human-machine integration Human-softbot integration, as achieved by AI research on information and communication technologies; Human-robot, non-invasive integration, as achieved by robotic research on autonomous systems inhabiting human environments; Human-robot invasive integration, as achieved by bionic research.

ETHICBOTS Strategic objectives Raising awareness and deepening understanding of these techno-ethical issues (conceptual analysis); Ethical monitoring of ICT, robotic, and bionic technologies for enhancing human mental and physical capacities; Fostering integration between Science and Society, by promoting responsible research, providing input to EU and national committees for ethical monitoring, warning, and opinion generation, improving communication between scientists, citizens and special groups.

Multiple-actor enterprise Ordinary citizens Legal experts Computer scientists Sociologists Roboticists Philosophers Theologians ………………………

Conceptual analysis by experts Conceptual analysis on the basis of specialized knowledge triaging techno-ethical issues, deepening our understanding of the higher- ranked issues, identifying ethical motivations opening new research perspectives, dispelling misconceptions

Triaging: identifying potential impact categories We need a set of Potential Impact Categories (PICs) as a basis for triaging emerging techno-ethical issues. Examples: imminence, novelty, Social pervasiveness of technologies.

General Ethical Themes Personal integrity and identity Responsibility Autonomy Fair access

Deepening our understanding learning machines and responsibility Designers, manufacturers, and operators cannot fully predict the behaviour of many learning machines based on  symbolic learning  neural network learning  evolutionary algorithms Traditional concepts of responsibility ascription fail!

Deepening our understanding Being cautious about precautionary principles  Should one enforce a “human-in-the-control- loop” exceptionless requirement?  No! Machines can take decisions which humans should not override (e.g., to prevent accidents)

Ethically motivated research Improving machine learning standards Practising cooperative design Providing machines with explanation & justification facilities

Explanation and justification Accountability, autonomy, trust, social anxiety Machines should become increasingly capable to explain and justify their courses of action Antecedents in knowledge-based decision support systems and expert systems Future Developments: Machine introspective and reflective capacities

Dispelling misconceptions “The machine will do exactly what we programmed it to do…” Do we fully understand the robots we make and theorize about? Can we fully predict and control robot behaviour?

Misconceptions at war The American military is working on a new generation of soldiers, far different from the army it has. "They don't get hungry," said Gordon Johnson of the Joint Forces Command at the Pentagon. "They're not afraid. They don't forget their orders. They don't care if the guy next to them has just been shot. Will they do a better job than humans? Yes.“ The robot soldier is coming. Front-page article, NYT 16 feb T. Weiner

Robo-soldiers & AI-complete problems Open context interpretation Recognizing surrender gestures Telling bystanders from foes