# A Voting-Based System for Ethical Decision Making

arXiv: https://arxiv.org/abs/1709.06692 · HTML: https://mlsanity.com/paper/1709.06692/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2017.

## Built on by (most cited first, 30 of 55)

- [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2307.15217/) (2023)
- [Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback](https://mlsanity.com/paper/2404.10271/) (2024)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Axioms for AI Alignment from Human Feedback](https://mlsanity.com/paper/2405.14758/) (2024)
- [Generative Social Choice](https://mlsanity.com/paper/2309.01291/) (2023)
- [AI Alignment and Social Choice: Fundamental Limitations and Policy Implications](https://mlsanity.com/paper/2310.16048/) (2023)
- [Moral Machine or Tyranny of the Majority?](https://mlsanity.com/paper/2305.17319/) (2023)
- [Proportional Aggregation of Preferences for Sequential Decision Making](https://mlsanity.com/paper/2306.14858/) (2023)
- [Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders](https://mlsanity.com/paper/2205.08928/) (2022)
- [Preference Poisoning Attacks on Reward Model Learning](https://mlsanity.com/paper/2402.01920/) (2024)
- [The Staircase of Ethics: Probing LLM Value Priorities through Multi-Step Induction to Complex Moral Dilemmas](https://mlsanity.com/paper/2505.18154/) (2025)
- [Metaethical Perspectives on 'Benchmarking' AI Ethics](https://mlsanity.com/paper/2204.05151/) (2022)
- [On Goodhart's law, with an application to value alignment](https://mlsanity.com/paper/2410.09638/) (2024)
- [Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions](https://mlsanity.com/paper/2503.00940/) (2025)
- [Designing Fiduciary Artificial Intelligence](https://mlsanity.com/paper/2308.02435/) (2023)
- [Learning Linear Utility Functions From Pairwise Comparison Queries](https://mlsanity.com/paper/2405.02612/) (2024)
- [On the Pros and Cons of Active Learning for Moral Preference Elicitation](https://mlsanity.com/paper/2407.18889/) (2024)
- [On The Stability of Moral Preferences: A Problem with Computational Elicitation Methods](https://mlsanity.com/paper/2408.02862/) (2024)
- [From Multi-Agent Systems and the Semantic Web to Agentic AI: A Unified Narrative of the Web of Agents](https://mlsanity.com/paper/2507.10644/) (2025)
- [Moral Dilemmas for Moral Machines](https://mlsanity.com/paper/2203.06152/) (2022)
- [Moral Change or Noise? On Problems of Aligning AI With Temporally Unstable Human Feedback](https://mlsanity.com/paper/2511.10032/) (2025)
- [When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data](https://mlsanity.com/paper/2201.07763/) (2022)
- [Learning How to Vote with Principles: Axiomatic Insights Into the Collective Decisions of Neural Networks](https://mlsanity.com/paper/2410.16170/) (2024)
- [Human-centered mechanism design with Democratic AI](https://mlsanity.com/paper/2201.11441/) (2022)
- [Artificial virtuous agents in a multiagent tragedy of the commons](https://mlsanity.com/paper/2210.02769/) (2022)
- [Measuring Value Alignment](https://mlsanity.com/paper/2312.15241/) (2023)
- [Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment](https://mlsanity.com/paper/2509.04445/) (2025)
- [Egalitarian Judgment Aggregation](https://mlsanity.com/paper/2102.02785/) (2021)
- [If our aim is to build morality into an artificial agent, how might we begin to go about doing so?](https://mlsanity.com/paper/2310.08295/) (2023)
- [MoralityGym: A Benchmark for Evaluating Hierarchical Moral Alignment in Sequential Decision-Making Agents](https://mlsanity.com/paper/2602.13372/) (2026)

## Abstract (arXiv)

We present a general approach to automating ethical decisions, drawing on machine learning and computational social choice. In a nutshell, we propose to learn a model of societal preferences, and, when faced with a specific ethical dilemma at runtime, efficiently aggregate those preferences to identify a desirable choice. We provide a concrete algorithm that instantiates our approach; some of its crucial steps are informed by a new theory of swap-dominance efficient voting rules. Finally, we implement and evaluate a system for ethical decision making in the autonomous vehicle domain, using preference data collected from 1.3 million people through the Moral Machine website.