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An Empirical Approach to Capture Moral Uncertainty in AI

Published: 07 February 2020 Publication History

Abstract

As AI Systems become increasingly autonomous they are expected to engage in complex moral decision-making processes. For the purpose of guidance of such processes theoretical and empirical solutions have been sought. In this research we integrate both theoretical and empirical lines of thought to address the matters of moral reasoning in AI Systems. We reconceptualize a metanormative framework for decision-making under moral uncertainty within the Discrete Choice Analysis domain and we operationalize it through a latent class choice model. The discrete choice analysis-based formulation of the metanormative framework is theory-rooted and practical as it captures moral uncertainty through a small set of latent classes. To illustrate our approach we conceptualize a society in which AI Systems are in charge of making policy choices. In the proof of concept two AI systems make policy choices on behalf of a society but while one of the systems uses a baseline moral certain model the other uses a moral uncertain model. It was observed that there are cases in which the AI Systems disagree about the policy to be chosen which we believe is an indication about the relevance of moral uncertainty.

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cover image ACM Conferences
AIES '20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
February 2020
439 pages
ISBN:9781450371100
DOI:10.1145/3375627
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2020

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Author Tags

  1. artificial intelligence
  2. metanormative theory
  3. moral uncertainty
  4. morality

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  • European Research Council

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AIES '20
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Overall Acceptance Rate 61 of 162 submissions, 38%

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