Allocating Human Oversight in AI-Enabled Analytics
Authors: Zikun Ye, Jiameng Lyu, Rui Tao
Organizations: Michael G. Foster School of Business, University of Washington · Department of Management Science, School of Management, Fudan University, Shanghai, China · Guanghua School of Management, Peking University
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.
Figures & tables
Figure 1: Adaptive human-validation allocation pipeline. The AI layer generates cheap predictions for Q analytics tasks, but task-level AI reliability (rectification difficulty Aq ) is heterogeneous and unknown. Our policy allocates scarce human validation online: each human label both sharpens the estimate and reveals the task’s difficulty (dashed feedback), directing effort where AI is least reliable.
Application
Allocation unit q
AI prediction layer
Human ground truth
Estimand
Demand sensing from reviews/chat logs
Product category, region, issue type
LLM-coded sentiment, issue, or demand signal
Human audit label
Mean, proportion
Service-quality monitoring
Queue, agent team, issue class
AI quality score or escalation flag
Human QA audit
Defect or recovery-risk rate
Product / concept testing
Concept, segment, choice task
LLM-predicted rating or choice
Human rating or choice
Mean preference, MNL/conjoint partworths
Content / promotion evaluation
Creative, campaign, segment
LLM persuasiveness or brand-safety score
Human evaluation or follow-up
Mean score, regression coefficients
Survey / digital-twin study (this paper)
Question, module, segment
LLM-predicted response
Human survey response
Mean, category probability
Table 1: Examples of AI-enabled human-validation allocation problems.
Algorithm 1 UCB-Based Online Human-Validation Allocation with PPI++
Parameter
Symbol
Baseline
Role
Questions
Q
100
Number of survey questions
LLM quality
ρ
0.7
Correlation between S and Ysig
Heterogeneity
h
2.0
Spread of vq across questions ( CV≈1.04 )
Human noise
ση
0.5
Additional noise on human response Y
Budget (Exp. 1)
B
2,000
≈20 samples per question
Budget (Exp. 2–3)
B
1,000
≈10 samples per question
Table 2: Baseline parameters for synthetic experiments.
Figure 2: Experiment 1 ( Q=100 , B=2,000 , ρ=0.7 , h=2.0 , 200 reps). Left: expected MSE Eq. ( 13 ) vs. budget (linear). Center: same on log–log scale. Right: UCB terminal regret R(B) across budgets B∈{500,…,10,000} with O(lnB/B2) reference line. Shaded bands: ±1 SE across replications.
Figure 3: Effect of LLM quality ρ ( Q=100 , B=1,000 , h=2.0 , 200 reps). Left: expected MSE. Right: terminal regret R(B) . Shaded bands: ±1 SE across replications. All policies benefit from higher ρ through the PPI++ estimator; relative performance gaps are stable across ρ .
Figure 4: Effect of heterogeneity h ( Q=100 , B=1,000 , ρ=0.7 , 200 reps). Left: expected MSE. Right: terminal regret R(B) . Shaded bands: ±1 SE across replications. UCB’s advantage over uniform and ε -greedy grows with heterogeneity; at low h , all policies perform similarly as the oracle allocation is nearly uniform.
Figure 5: Rectification difficulty Aq versus optimal PPI++ tuning parameter λq∗ for the 68 Wave 4 questions, colored by task type (number of questions in parentheses). Questions with λq∗≈0 (left cluster) receive no variance reduction from the LLM.
B
Oracle
UCB
ETC
ε -Greedy
Uniform
500
89.1
94.0 (5.5%)
107.9 (21.1%)
106.7 (19.8%)
99.6 (11.8%)
1,000
44.6
46.3 (3.9%)
53.3 (19.7%)
47.3 (6.2%)
49.3 (10.6%)
1,500
29.7
30.5 (2.7%)
33.8 (13.9%)
30.9 (4.0%)
32.7 (10.0%)
2,000
22.3
22.8 (2.1%)
24.5 (9.9%)
23.1 (3.7%)
24.6 (10.3%)
Table 3: Expected MSE ( ×10−2 ) across budgets on Twin-2K-500 (500 reps). Gap to oracle (%) in parentheses.
(a) Varying weights, cq=1
(b) Varying costs, wq=1
a
Oracle
UCB
ETC
ε -Gr.
Unif.
a
Oracle
UCB
ETC
ε -Gr.
Unif.
0.00
44.6
46.3 (3.9%)
53.3
47.3
49.3 (10.6%)
0.00
44.6
46.3 (3.9%)
53.3
47.3
49.3 (10.6%)
0.25
43.7
45.4 (3.8%)
52.5
46.6
48.8 (11.6%)
0.25
44.9
46.6 (3.9%)
53.5
47.7
49.6 (10.5%)
0.50
42.4
44.1 (4.1%)
51.1
45.6
48.4 (14.1%)
0.50
44.7
46.5 (4.0%)
53.2
47.7
49.9 (11.5%)
0.75
40.3
42.0 (4.0%)
49.1
44.2
47.9 (18.8%)
0.75
44.0
45.7 (3.9%)
52.3
47.4
50.1 (13.8%)
1.00
37.1
38.7 (4.2%)
46.4
42.4
47.5 (27.9%)
1.00
42.3
44.0 (4.1%)
50.4
46.9
50.1 (18.4%)
Table 4: Sensitivity to weight and cost heterogeneity: expected MSE ( ×10−2 ), B=1,000 , 500 reps.
h
Oracle
UCB
ETC
ε -Greedy
Uniform
0.00
49.1
0.95 (1.9%)
8.71 (17.7%)
1.81 (3.7%)
0.00 (0.0%)
0.25
48.7
0.95 (2.0%)
8.75 (18.0%)
1.79 (3.7%)
0.37 (0.8%)
0.50
47.8
1.05 (2.2%)
8.76 (18.3%)
1.93 (4.0%)
1.31 (2.7%)
1.00
44.6
1.72 (3.9%)
8.78 (19.7%)
2.74 (6.2%)
4.70 (10.6%)
1.50
40.1
2.85 (7.1%)
8.90 (22.2%)
4.30 (10.7%)
9.31 (23.2%)
2.00
35.3
4.12 (11.7%)
9.12 (25.8%)
6.39 (18.1%)
14.22 (40.3%)
Table 5: Terminal regret and oracle MSE ( ×10−2 ) under varying Aq heterogeneity ( B=1,000 , 500 reps). Mean Aq is held constant at 0.106 across all h .
Policy
MSE
Gap (%)
Oracle
15.74
—
UCB
16.06
2.0
ETC
16.44
4.5
Uniform
17.79
13.1
Table 6: MNL partworth estimation: expected MSE at B=2,000 ( Q=50 , K=3 , d=2 , 200 reps). Gap to oracle (%) in parentheses.
Policy
MSE ( ×10−2 )
Gap (%)
Oracle
44.6
—
UCB
46.3
3.9
ETC ( α=0.1 )
74.4
66.9
ETC ( α=0.2 )
60.2
35.2
ETC ( α=0.3 )
53.3
19.7
ETC ( α=0.4 )
49.5
11.1
Table 7: ETC sensitivity to pilot fraction α on Twin-2K-500 ( B=1,000 , 500 reps). UCB and Uniform shown as reference.
AI systems increasingly search among candidate answers and deploy the highest-scoring one. Increasing search changes which errors matter, so a precise evaluation at one computation budget can leave another budget unresolved. We connect this information gap to the cost of closing it. For independent best-of-n search, aggregate reliability measurements identify deployment only through the directions they observe; we derive an exact ambiguity frontier when only small search widths are audited. Retaining candidate ranks and truth labels enables a constructive alternative: one audit can estimate reliability across all widths up to N. With known score percentiles, the minimax worst-coordinate mean squared error scales as (1 + log N)/T + N/M, capped at a constant, for expected budgets of T truth labels and M candidate observations. Matching lower bounds allow adaptive label acquisition, establishing that the distinct label and candidate costs are intrinsic to this experiment. An explicit design attains this order; a complementary record-based procedure supplies simultaneous guarantees without a known score distribution. Retrospective mathematical-reasoning and code-generation analyses show why search-dependent validation matters. In held-out CodeRM pools, a shared audit reduces the 95th-percentile maximum error across 100 widths by 58% and 40% relative to uniform labeling. These results turn structural ambiguity into a quantitative prescription for reusable validation.
Ricardo Fitas
Technical University of Darmstadt, 64289 Darmstadt, Germany
We consider online monitoring of an AI system that produces a sequence of decisions. At each round, the system outputs a proposed decision together with a confidence score estimating the trustworthiness of the decision. The decision and confidence score need not be produced by the same model: for example, one agent may propose a decision, while a separate verifier agent evaluates it and outputs a correctness or safety score. Based on this information, we must decide whether to accept the AI decision automatically or consult an expert, such as a human auditor, who reviews the proposal and either accepts or corrects it. We formulate this task as an online constrained optimization problem in which the goal is to minimize the frequency of expert auditing while guaranteeing that the fraction of AI errors that go unaudited stays below a user-defined threshold. We derive a family of algorithms with provable error-control guarantees with no assumptions, including a method that is optimally efficient: it asymptotically attains the best achievable auditing rate for both adversarial and i.i.d. data streams. Experiments on screening harmful requests to large language models, qualitative text annotation, and data labeling show that our methods achieve valid error control in practice while substantially reducing the need for human auditing compared to baselines.
Isaac Gibbs, Elise L. Han, Ryan J. Tibshirani +1
Departments of Statistics and Management Science & Engineering, Stanford University
Human-AI complementarity, the idea that combining human and AI judgments can outperform either alone, offers a promising pathway toward robust oversight of advanced AI systems. However, whether human-AI complementarity can be achieved on realistic tasks remains an open question. We investigate this through two approaches: hybridization and two AI assistance methods (top-2 assistance and subtask delegation), evaluated on a multi-domain dataset of 1,886 samples spanning knowledge, factuality, long-context reasoning, and deception detection. We find only modest complementarity gains. Baseline hybridization yields just +0.4 percentage points (pp) over AI alone (69.3% vs 68.9%), limited both by a small complementarity region (only 8.9% of items where AI errs but humans do not) and the inability of confidence-based routing to identify it, since the model's confidence is similarly distributed across correct and incorrect predictions. Applied when AI has low confidence, top-2 assistance increases human accuracy from 28.4% to 38.3%, surpassing AI alone (37.7%) -- but primarily because humans adopt correct AI suggestions, not because they successfully override AI errors. These findings suggest that the primary bottleneck is not human task accuracy per se, but the ability to route decisions to humans when it matters and to design assistance methods that enable humans to catch AI mistakes. Our quantitative and qualitative analyses pinpoint where and why each method succeeds or fails, offering concrete targets for future work. We will release our dataset and code upon request to support progress toward more effective human-AI collaboration for AI oversight.
Yuzheng Xu, Annya Dahmani, Matthew D. Blanchard +13
The University of Tokyo · UC Berkeley · University of Sydney +6