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People are averse to machines making moral decisions
Yochanan E Bigman1, Kurt Gray1
1Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, 235 E Cameron Ave, Chapel Hill, NC 27514, USA.
Cognition
|August 15, 2018
Summary
Most people do not want autonomous machines making moral decisions, as they perceive machines as lacking full thought and feeling. This aversion may hinder the integration of AI in critical sectors like healthcare and transportation.
Area of Science:
- Human-Computer Interaction
- Artificial Intelligence Ethics
- Cognitive Science
Background:
- Increasing integration of autonomous systems in society.
- Ethical considerations surrounding artificial intelligence (AI) decision-making.
- Public perception of machine capabilities in moral reasoning.
Purpose of the Study:
- To investigate public aversion to autonomous machines making moral decisions.
- To identify the psychological factors underlying this aversion.
- To explore potential strategies for increasing the acceptability of machine moral decision-making.
Main Methods:
- Nine empirical studies were conducted.
- Studies involved morally-relevant decisions in driving, legal, medical, and military contexts.
- Perceptions of machine cognition (thinking) and sentience (feeling) were assessed.
Main Results:
- A significant aversion to machines making moral decisions was found across studies.
- This aversion was mediated by the perception that machines lack full thinking and feeling capabilities.
- The aversion persisted even when machine moral decisions yielded positive outcomes.
- Attempts to mitigate aversion through advisory roles, enhanced perceived experience, or expertise showed limited success.
Conclusions:
- Public aversion to machine moral decision-making is substantial and linked to perceived deficits in machine cognition and sentience.
- Overcoming this aversion is challenging, posing significant hurdles for AI integration in domains requiring moral judgment.
- Further research is needed to explore effective strategies for fostering trust and acceptance of AI in sensitive applications.
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