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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…