@inproceedings{
fr{\"a}nken2023social,
title={Social Contract {AI}: Aligning {AI} Assistants with Implicit Group Norms},
author={Jan-Philipp Fr{\"a}nken and Samuel Kwok and Peixuan Ye and Kanishk Gandhi and Dilip Arumugam and Jared Moore and Alex Tamkin and Tobias Gerstenberg and Noah Goodman},
booktitle={Socially Responsible Language Modelling Research},
year={2023},
url={https://openreview.net/forum?id=LGjqs5ILdN}
}
We explore the idea of aligning an AI assistant by inverting a model of users' (unknown) preferences from observed interactions. To validate our proposal, we run proof-of-concept simulations in the economic ultimatum game, formalizing user preferences as policies that guide the actions of simulated players. We find that the AI assistant accurately aligns its behavior to match standard policies from the economic literature (e.g., selfish, altruistic). However, the assistant’s learned policies lack robustness and exhibit limited generalization in an out-of-distribution setting when confronted with a currency (e.g., grams of medicine) that was not included in the assistant's training distribution. Additionally, we find that when there is inconsistency in the relationship between language use and an unknown policy (e.g., an altruistic policy combined with rude language), the assistant's learning of the policy is slowed. Overall, our preliminary results suggest that developing simulation frameworks in which AI assistants need to infer preferences from diverse users can provide a valuable approach for studying practical alignment questions.
Social Contract AI: Aligning AI Assistants with Implicit Group Norms
Jan-Philipp Fränken Samuel Kwok Peixuan Ye Kanishk Gandhi Dilip Arumugam Jared Moore Alex Tamkin Tobias Gerstenberg Noah Goodman