On The Stability of Moral Preferences: A Problem with Computational Elicitation Methods
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Abstract (arXiv)
Preference elicitation frameworks feature heavily in the research on participatory ethical AI tools and provide a viable mechanism to enquire and incorporate the moral values of various stakeholders. As part of the elicitation process, surveys about moral preferences, opinions, and judgments are typically administered only once to each participant. This methodological practice is reasonable if participants' responses are stable over time such that, all other relevant factors being held constant, their responses today will be the same as their responses to the same questions at a later time. However, we do not know how often that is the case. It is possible that participants' true moral preferences change, are subject to temporary moods or whims, or are influenced by environmental factors we don't track. If participants' moral responses are unstable in such ways, it would raise important methodological and theoretical issues for how participants' true moral preferences, opinions, and judgments can be ascertained. We address this possibility here by asking the same survey participants the same moral questions about which patient should receive a kidney when only one is available ten times in ten different sessions over two weeks, varying only presentation order across sessions. We measured how often participants gave different responses to simple (Study One) and more complicated (Study Two) repeated scenarios. On average, the fraction of times participants changed their responses to controversial scenarios was around 10-18% across studies, and this instability is observed to have positive associations with response time and decision-making difficulty. We discuss the implications of these results for the efficacy of moral preference elicitation, highlighting the role of response instability in causing value misalignment between stakeholders and AI tools trained on their moral judgments.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 10 of its 33 references that are in the map. First posted on arXiv 5 Aug 2024.
Builds on
10 of 33 references.
References · 10
- 2021Can Machines Learn Morality? The Delphi Experimentcited by 140
- 2019Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learningcited by 57
- 2017A Voting-Based System for Ethical Decision Makingcited by 55
- 2020Adapting a Kidney Exchange Algorithm to Align with Human Valuescited by 36
- 2023Moral Machine or Tyranny of the Majority?cited by 22
- 2020Indecision Modelingcited by 8
- 2023Deploying a Robust Active Preference Elicitation Algorithm on MTurk: Experiment Design, Interface, and Evaluation for COVID-19 Patient Prioritizationcited by 7
- 2024Value Preferences Estimation and Disambiguation in Hybrid Participatory Systemscited by 7
- 2020Artificial Artificial Intelligence: Measuring Influence of AI 'Assessments' on Moral Decision-Makingcited by 4
- 2022Diverse Representation via Computational Participatory Elections -- Lessons from a Case Studycited by 3
Built on by
Citing papers per year: 2025: 3; 2026: 4.
7 papers.
- 2025Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisionscited by 9
- 2025Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Valuescited by 2
- 2025Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignmentcited by 2
- 2026Reflect: Transparent Principle-Guided Reasoning for Constitutional Alignment at Scalecited by 1
- 2026The End Justifies the Mean: A Linear Ranking Rule for Proportional Sequential Decisionscited by 0
- 2026Position: We Need Practical AI Alignment Methods to Mirror Human Reasoningcited by 0
- 2026Algorithmic Impact Reveals the Hidden Social Choice Structure of Alignmentcited by 0
- 2026Algorithmic Impact Reveals the Hidden Social Choice Structure of Alignment25 Aug 2026
- 2026Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning20 Jul 2026
- 2026The End Justifies the Mean: A Linear Ranking Rule for Proportional Sequential Decisions12 May 2026
- 2026Reflect: Transparent Principle-Guided Reasoning for Constitutional Alignment at Scale26 Jan 2026
- 2025Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment4 Sep 2025
- 2025Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Values30 May 2025
- 2025Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions2 Mar 2025