EthicsAccountabilityTrustworthinesscs.CY
Descriptive AI Ethics: Collecting and Understanding the Public Opinion
Gabriel Lima, Meeyoung Cha
January 15, 2021
arXiv.org
5 citations
Key Takeaways
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Public opinion should inform AI ethics debates: The paper argues that data-driven research on how the public perceives AI ethics is essential, suggesting that scholarly discussions about responsible AI should be grounded in what actual people think, not just expert opinions alone.
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The "responsibility gap" is a real concern worth studying: The research identifies the responsibility gap—uncertainty about who is accountable when autonomous AI systems cause harm—as a key ethical issue that deserves systematic investigation through public input.
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A mixed approach combining theory and public input is needed: The paper proposes combining normative ethics (what should be done) with descriptive research (what people actually think), suggesting that responsible AI development requires both philosophical frameworks and empirical understanding of public concerns.
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Bridging optimism and pessimism requires evidence: By collecting public perspectives on AI's ethical and legal implications, the research aims to move beyond polarized debates where some view AI deployment optimistically while others are deeply skeptical—suggesting common ground may exist when we understand what people actually worry about.
Abstract
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.