arXiv:2411.03651v1 Announce Type: new Abstract: We consider the challenge of AI value alignment with multiple individuals that have different reward functions and optimal policies in an underlying Markov decision process. We formalize this problem as one of policy aggregation, where the goal is to identify a desirable collective policy. We argue that an approach informed by social choice theory is especially suitable. Our key insight is that social choice methods can be reinterpreted by identifying ordinal preferences with volumes of subsets of the state-action occupancy polytope. Building on this insight, we demonstrate that a variety of methods–including approval voting, Borda count, the proportional veto core, and quantile fairness–can be practically applied to policy aggregation.
In the article “AI Value Alignment with Multiple Individuals: A Social Choice Perspective,” the authors address the challenge of aligning artificial intelligence (AI) systems with the values and preferences of multiple individuals. They recognize that each individual may have different reward functions and optimal policies within a Markov decision process. To tackle this problem, the authors propose a policy aggregation approach that aims to identify a desirable collective policy.
The authors argue that social choice theory provides a suitable framework for addressing this challenge. They highlight that social choice methods can be reinterpreted by associating ordinal preferences with volumes of subsets of the state-action occupancy polytope. This key insight allows for the practical application of various social choice methods to policy aggregation.
Throughout the article, the authors explore several social choice methods, including approval voting, Borda count, the proportional veto core, and quantile fairness. They demonstrate how these methods can be effectively applied to policy aggregation, offering potential solutions for AI value alignment with multiple individuals.
By leveraging social choice theory and its reinterpretation in the context of AI value alignment, the authors provide valuable insights and practical approaches to address the complexities of aligning AI systems with the diverse preferences and values of multiple individuals.
Exploring AI Value Alignment with Multiple Individuals
Abstract: We consider the challenge of AI value alignment with multiple individuals that have different reward functions and optimal policies in an underlying Markov decision process. We formalize this problem as one of policy aggregation, where the goal is to identify a desirable collective policy. We argue that an approach informed by social choice theory is especially suitable. Our key insight is that social choice methods can be reinterpreted by identifying ordinal preferences with volumes of subsets of the state-action occupancy polytope. Building on this insight, we demonstrate that a variety of methods–including approval voting, Borda count, the proportional veto core, and quantile fairness–can be practically applied to policy aggregation.
In the realm of artificial intelligence, aligning the values of multiple individuals is a challenging task. Each person may have unique reward functions and optimal policies, making it difficult to find a collective policy that suits everyone. However, by integrating social choice theory into the process, we can overcome this challenge and foster better value alignment in AI systems.
Policy Aggregation: A Solution for Value Alignment
The problem of aligning AI values with multiple individuals can be formalized as policy aggregation. The goal here is to identify a desirable collective policy that takes into account the individual preferences and rewards of each person involved. By finding a common ground, we can create AI systems that benefit everyone involved.
What makes social choice theory particularly suitable for this problem is the insight that ordinal preferences can be linked to the volumes of subsets of the state-action occupancy polytope. This means that we can utilize various social choice methods to practically apply policy aggregation techniques.
Approaches in Policy Aggregation
Several methods from social choice theory can be applied to policy aggregation:
- Approval Voting: In this method, individuals vote for policies they approve of. The policy with the highest number of approvals is selected as the collective policy.
- Borda Count: Each individual assigns a score to each policy, and the policy with the highest sum of scores across all individuals is chosen as the collective policy.
- Proportional Veto Core: This method allows individuals to veto policies that they strongly disagree with. The collective policy is determined by finding a compromise that satisfies the conditions set by each individual’s vetoes.
- Quantile Fairness: This approach aims to find a collective policy that distributes benefits fairly across individuals, focusing on achieving a desired level of fairness.
By employing these methods, we can effectively tackle the challenge of AI value alignment with multiple individuals.
“Integrating social choice theory into AI value alignment enables us to foster collective decision-making and create AI systems that truly serve the needs and values of everyone involved.”
It is worth noting that the success of policy aggregation relies on effective communication, active participation from all individuals, and a willingness to find compromises. By embracing these principles, we can pave the way for AI systems that are not only technically advanced but also ethically aligned with human values.
Conclusion
The challenge of aligning AI values with multiple individuals can be overcome by employing social choice theory in the policy aggregation process. By considering ordinal preferences and volumes of subsets of the state-action occupancy polytope, we can practically apply methods such as approval voting, Borda count, the proportional veto core, and quantile fairness.
Through collective decision-making and value alignment, we can create AI systems that serve the needs and preferences of all individuals involved. This innovative approach opens new possibilities for ethical and inclusive AI implementation in various domains.
The paper titled “AI Value Alignment with Multiple Individuals: A Social Choice Perspective” addresses the challenge of aligning AI systems with multiple individuals who have different reward functions and optimal policies in a Markov decision process (MDP). The authors propose a solution to this problem by leveraging social choice theory to identify a desirable collective policy.
One of the key insights in this paper is the reinterpretation of social choice methods by associating ordinal preferences with volumes of subsets of the state-action occupancy polytope. This reinterpretation allows for the practical application of various social choice methods to policy aggregation in the context of AI value alignment.
The authors demonstrate the practicality of their approach by applying several social choice methods to policy aggregation. These methods include approval voting, Borda count, the proportional veto core, and quantile fairness. By applying these methods, the authors aim to identify a collective policy that maximizes the alignment of AI systems with the preferences of the multiple individuals involved.
This research has significant implications for the development and deployment of AI systems in scenarios where multiple individuals with diverse preferences are involved. By leveraging social choice theory, AI systems can be designed to align with the collective preferences of these individuals, ensuring fair and equitable outcomes.
Moving forward, it would be interesting to see how these methods perform in real-world scenarios with more complex and dynamic environments. Additionally, exploring the potential trade-offs and limitations of each social choice method in the context of AI value alignment could provide valuable insights for future research.
Overall, this paper presents a novel and practical approach to address the challenge of AI value alignment with multiple individuals. By incorporating social choice theory, the proposed methods offer a promising avenue for achieving collective policy alignment in AI systems.
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