Descriptive AI Ethics: Collecting and Understanding the Public Opinion
This work addresses the responsibility gap in AI ethics for researchers and policymakers, but it is incremental as it builds on existing normative and descriptive methods without introducing a fundamentally new solution.
The paper tackles the need for data-driven research on public perceptions of ethical, moral, and legal issues in autonomous AI systems by proposing a mixed AI ethics model that combines normative and descriptive approaches, using public data to inform scholarly discussions and bridge optimistic and pessimistic views on AI deployment.
There is a growing need for data-driven research efforts on how the public perceives the ethical, moral, and legal issues of autonomous AI systems. The current debate on the responsibility gap posed by these systems is one such example. This work proposes a mixed AI ethics model that allows normative and descriptive research to complement each other, by aiding scholarly discussion with data gathered from the public. We discuss its implications on bridging the gap between optimistic and pessimistic views towards AI systems' deployment.