# Allocating Human Oversight in AI-Enabled Analytics

arXiv: https://arxiv.org/abs/2604.12497 · HTML: https://mlsanity.com/paper/2604.12497/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 14 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [Linear Contextual Bandits with Knapsacks](https://mlsanity.com/paper/1507.06738/) (2015)
- [Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI](https://mlsanity.com/paper/2501.09731/) (2025)
- [LOLA: LLM-Assisted Online Learning Algorithm for Content Experiments](https://mlsanity.com/paper/2406.02611/) (2024)
- [How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective](https://mlsanity.com/paper/2502.17773/) (2025)
- [Fine-Tune, Then Rectify](https://mlsanity.com/paper/2511.19486/) (2025)
- [PPI is the Difference Estimator: Recognizing the Survey Sampling Roots of Prediction-Powered Inference](https://mlsanity.com/paper/2603.19160/) (2026)
- [Estimating Wage Disparities Using Foundation Models](https://mlsanity.com/paper/2409.09894/) (2024)
- [Asymptotically Optimal Sequential Testing with Heterogeneous LLMs](https://mlsanity.com/paper/2604.01086/) (2026)

## Built on by (most cited first, 1 of 1)

- [Learning a Size-Weight Frontier for Synthetic-Augmented Inference](https://mlsanity.com/paper/2608.28576/) (2026)

## Abstract (arXiv)

Organizations increasingly deploy AI as a low-cost prediction layer in customer-facing decision processes, including demand sensing, service-quality monitoring, product testing, and market research, but AI-generated signals are unevenly reliable across tasks, products, and customer segments. Firms therefore still need scarce human validation (labels, audits, survey responses, or follow-up measurements) to anchor AI outputs to ground truth. Because human ground truth is itself noisy, varying across labelers and even across repeated judgments, the firm must collect and average several human labels per task, which makes human validation costly. We study how to allocate a limited human-validation budget across many AI-assisted tasks when reliability is heterogeneous and unknown before deployment. We cast this within tuned prediction-powered inference. Each human label both sharpens the AI-assisted estimate and reveals the task's rectification difficulty, the variance that remains after the AI prediction is optimally used as a control variate. If difficulties were known, the optimal allocation would follow a Neyman square-root rule; because they are unknown, we propose a policy based on upper confidence bounds that learns them online and steers validation toward tasks where AI is least reliable. We prove that the policy's terminal efficiency loss relative to the oracle allocation vanishes as the budget grows. In synthetic experiments and a real digital-twin survey with 68 tasks and over 2000 respondents, it closes most of the gap to the oracle when reliability is heterogeneous, outperforming uniform and epsilon-greedy allocation; on the survey data it also outperforms explore-then-commit pilot designs and cuts uniform's 10--12% gap to 2--6%. The value of AI depends not only on model accuracy but also on the operational policy that targets human oversight where AI errors matter most.