Agent Welfare

Agent welfare, focusing on the well-being of artificial agents interacting with humans or other agents, is a burgeoning research area aiming to design and deploy safe, trustworthy, and efficient AI systems. Current research emphasizes balancing agent welfare with other objectives like model accuracy and societal benefit, often employing reinforcement learning and generative adversarial networks to achieve this balance, while also exploring frameworks for defining and measuring agent intentionality. This work is crucial for mitigating risks associated with increasingly autonomous AI agents and ensuring their responsible integration into various applications, from scientific research to human-computer interaction.