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Descriptive AI Ethics: Collecting and Understanding the Public Opinion

arXiv 2101.05957 ↗First posted 15 Jan 2021cs.CY cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 47 references that are in the map. First posted on arXiv 15 Jan 2021.

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References · 3

  1. 2018How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairnesscited by 48
  2. 2017Society-in-the-Loop: Programming the Algorithmic Social Contractcited by 35
  3. 2018Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Predictioncited by 35

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