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Joshua Greene

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AAAI Conference 2016 Conference Paper

Embedding Ethical Principles in Collective Decision Support Systems

  • Joshua Greene
  • Francesca Rossi
  • John Tasioulas
  • Kristen Venable
  • Brian Williams

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.

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RLDM Conference 2015 Conference Abstract

Motivated bias in a reversal learning task

  • Donal Cahill
  • Joshua Greene

That we ultimately come to hold a rosier picture of reality than is objectively warranted is well documented (Alicke & Sedikides, 2009; Svenson, 1981; Taylor & Brown, 1988; Williams & Gilovich, 2008). Here we tested whether such beliefs could be due to a biased weighting of positive versus negative evidence. We presented 330 participants with a reversal learning paradigm where each reversal entailed a mean increase or decrease in expected reward across actions. We found participants were more likely to treat outcomes as diagnostic of a reversal when that reversal promised an increase versus a decrease in future expected reward. A further control task showed that this bias was not due to risk preference.

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