Client Centered Assessment

Client-centered assessment aims to evaluate systems, particularly AI agents like large language models (LLMs), from the perspective of the user or "client" they interact with, focusing on metrics like perceived therapeutic alliance and outcome satisfaction. Current research employs various methods, including simulated clients and multi-agent debate frameworks, to assess aspects such as the quality of generated text, aesthetic judgments, and the fairness of contributions in federated learning settings. This research is crucial for ensuring the responsible development and deployment of AI systems, particularly in sensitive applications like healthcare and creative content generation, by providing a more human-centric evaluation framework.