Measuring Value Alignment
On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment
Abstract (arXiv)
As artificial intelligence (AI) systems become increasingly integrated into various domains, ensuring that they align with human values becomes critical. This paper introduces a novel formalism to quantify the alignment between AI systems and human values, using Markov Decision Processes (MDPs) as the foundational model. We delve into the concept of values as desirable goals tied to actions and norms as behavioral guidelines, aiming to shed light on how they can be used to guide AI decisions. This framework offers a mechanism to evaluate the degree of alignment between norms and values by assessing preference changes across state transitions in a normative world. By utilizing this formalism, AI developers and ethicists can better design and evaluate AI systems to ensure they operate in harmony with human values. The proposed methodology holds potential for a wide range of applications, from recommendation systems emphasizing well-being to autonomous vehicles prioritizing safety.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 8 of its 17 references that are in the map. First posted on arXiv 23 Dec 2023.
Builds on
8 of 17 references.
Closest ancestors · 1
Same direction, newest first
- 2021Value alignment: a formal approach18 Oct
Other references · 7
- 2016Concrete Problems in AI Safetycited by 1,582
- 2018Scalable agent alignment via reward modeling: a research directioncited by 438
- 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 129
- 2019Optimal Policies Tend to Seek Powercited by 115
- 2017A Voting-Based System for Ethical Decision Makingcited by 55
- 2018Measuring collaborative emergent behavior in multi-agent reinforcement learningcited by 5
- 2023Multi-Value Alignment in Normative Multi-Agent System: An Evolutionary Optimisation Approachcited by 4
Built on by
Citing papers per year: 2024: 1; 2025: 2.
3 papers.
Similar, not cited
Close in wording, no citation either way
- 2021The Challenge of Value Alignment: from Fairer Algorithms to AI Safety
- 2026Toward a Theory of Value in AI Alignment
- 2024Beyond Preferences in AI Alignment
- 2023A Multi-Level Framework for the AI Alignment Problem
- 2024Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment
- 2024Democratizing Reward Design for Personal and Representative Value-Alignment
- 2025Moral Anchor System: A Predictive Framework for AI Value Alignment and Drift Prevention
- 2026AI Value Alignment for Evolving Social Norms