1. 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?

2. 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.

3. 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.

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

5. 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

6. 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.

7. General Ethical Themes
Personal integrity and identity
Responsibility
Autonomy
Fair access

8. 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!

9. 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)

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

11. 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

12. 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?

13. 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

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