ETHICBOTS crude questions

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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?

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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.

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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.

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Multiple-actor enterprise

• Ordinary citizens
• Legal experts
• Computer scientists
• Sociologists

• Roboticists
• Philosophers
• Theologians

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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

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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.

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General Ethical Themes

• Personal integrity and identity
• Responsibility
• Autonomy
• Fair access

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Deepening our understandinglearning 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!

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Deepening our understandingBeing 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)

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Ethically motivated research

• Improving machine learning standards
• Practising cooperative design
• Providing machines with explanation
  justification facilities

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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

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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?

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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. 2005 T.
Weiner
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Robo-soldiers AI-complete problems

• Open context interpretation
• Recognizing surrender gestures
• Telling bystanders from foes