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

topic: general_aitop score: 100released: 2026-05-12first surfaced: 2026-05-12arXivPDFlinked_to_results2026-05-12

Authors: Pranavkumar Mallela, Vinay Kumar, Shashi Shekhar Jha et al.

arXiv · PDF

Summary

This paper addresses how to combine human and AI predictions in classification tasks where both contribute to a final label. Prior work assumed conditional independence and used Bayes rule with calibrated probabilities from both parties. The current work extends or refines this combination method for practical deployment in Human-AI teams on classification tasks. (Abstract appears truncated, so full contribution is unclear.)

Main takeaways:

  • Focuses on combining human (deterministic) and model (probabilistic) outputs for classification
  • Assumes human and model predictions are conditionally independent given ground truth
  • Uses instance-level model probabilities and class-level human calibration
  • Aims for cost-effective performance in Human-AI collaboration
  • (Abstract cut off—unclear what the novel contribution is beyond prior Bayes-rule methods)

Relevance

No direct connection to my work on persona installation, marker implantation, or behavioral conditioning in language models. This is about combining predictions from humans and AI in decision-making systems, which is a different problem space.

Abstract

arXiv:2605.08388v1 Announce Type: new 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.