AGENTIC-SYSTEMSMay 12, 20261 min read6 views

PLACO: A Multi-Stage Framework for Cost-Effective Performance in Human-AI Teams

PLACO is a multi-stage framework designed to enhance the performance of Human-AI teams by effectively combining human and model outputs using Bayesian principles and calibrated probabilities.

Computer Science > Artificial Intelligence

Title:PLACO: A Multi-Stage Framework for Cost-Effective Performance in Human-AI Teams

View PDF HTML (experimental)Abstract:Human-AI teams play a pivotal role in improving overall system performance when neither the human nor the model can achieve such performance on their own. With the advent of powerful and accessible Generative AI models, several mundane tasks have morphed into Human-AI team tasks. From writing essays to developing advanced algorithms, humans have found that using AI assistance has led to an accelerated work pace like never before. In classification tasks, where the final output is a single hard label, it is crucial to address the combination of human and model output. Prior work elegantly solves this problem using Bayes rule, using the assumption that human and model output are conditionally independent given the ground truth. Specifically, it discusses a combination method to combine a single deterministic labeler (the human) and a probabilistic labeler (the classifier model) using the model's instance-level and the human's class-level calibrated probabilities.

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Source: arXiv cs.AI Recent

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