1 citations · 1 across the 9 of their papers we have counts for
9 papers
Embeddings for Preferences, Not Semantics
Carter Blair, Ariel D. Procaccia, Milind Tambe
Modern AI is opening the door to collective decision-making in which participants express their views as free-form text rather than voting on a fixed set of candidates. A natural i…
Probably Approximately Consensus: On the Learning Theory of Finding Common Ground
Carter Blair, Ben Armstrong, Shiri Alouf-Heffetz +2
A primary goal of online deliberation platforms is to identify ideas that are broadly agreeable to a community of users through their expressed preferences. Yet, consensus elicitat…
Procedural Fairness in Multi-Agent Bandits
Joshua Caiata, Carter Blair, Kate Larson
In the context of multi-agent multi-armed bandits (MA-MAB), fairness is often reduced to outcomes: maximizing welfare, reducing inequality, or balancing utilities. However, evidenc…
Generating Fair Consensus Statements with Social Choice on Token-Level MDPs
Carter Blair, Kate Larson
Current frameworks for consensus statement generation with large language models lack the inherent structure needed to provide provable fairness guarantees when aggregating diverse…
Reflective Verbal Reward Design for Pluralistic Alignment
Carter Blair, Kate Larson, Edith Law
AI agents are commonly aligned with "human values" through reinforcement learning from human feedback (RLHF), where a single reward model is learned from aggregated human feedback…
Democratizing Reward Design for Personal and Representative Value-Alignment
Carter Blair, Kate Larson, Edith Law
Aligning AI agents with human values is challenging due to diverse and subjective notions of values. Standard alignment methods often aggregate crowd feedback, which can result in…
Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations
Carter Blair, Xiyao Wang, Charles Perin
Emotion is an important factor to consider when designing visualizations as it can impact the amount of trust viewers place in a visualization, how well they can retrieve informati…
Normative Modules: A Generative Agent Architecture for Learning Norms that Supports Multi-Agent Cooperation
Atrisha Sarkar, Andrei Ioan Muresanu, Carter Blair +3
Generative agents, which implement behaviors using a large language model (LLM) to interpret and evaluate an environment, has demonstrated the capacity to solve complex tasks acros…
Liquid Ensemble Selection for Continual Learning
Carter Blair, Ben Armstrong, Kate Larson
Continual learning aims to enable machine learning models to continually learn from a shifting data distribution without forgetting what has already been learned. Such shifting dis…