A Framework for Measuring Appropriate Reliance on Set-Valued AI Advice
Ranjan Mishra, Jakob Schoeffer
Abstract
Appropriate reliance on AI advice has become a central research theme in human-AI collaboration. Existing frameworks have focused exclusively on point predictions as AI advice. However, set-valued AI advice (e.g., discrete sets or continuous intervals) is increasingly being used to communicate uncertainty and improve human decision making. In this paper, we develop the first formal framework for measuring appropriate reliance on set-valued AI advice within the sequential judge-advisor paradigm, spanning both classification and regression tasks. For classification, we first introduce the dimensions that are necessary for evaluating set-valued AI advice. We then define two metrics: correct reliance rate on AI and correct reliance rate on self, which jointly characterize appropriate reliance in this setting. For regression, we introduce quantity of AI reliance and quality of AI reliance, which respectively measure whether a decision maker utilized the AI advice and whether their reliance helped them get closer to the ground truth relative to their initial estimate. Through the application of our framework, we demonstrate how these metrics capture important nuances in human-AI collaboration that existing measures overlook.
AI Impact Assessments
(1 models)Scientific Impact Assessment
1. Core Contribution
This paper addresses a genuine gap at the intersection of human-AI collaboration and uncertainty-aware AI systems. While existing frameworks for measuring appropriate reliance (notably Schemmer et al., 2023; Cabitza et al., 2023) assume AI advice takes the form of point predictions (correct vs. incorrect), AI systems increasingly output set-valued advice—prediction sets for classification and prediction intervals for regression—to communicate uncertainty. The paper's main contribution is a formal measurement framework consisting of:
The key insight is that set-valued advice fundamentally changes what "correctness" of AI advice means: it is no longer binary but involves coverage (whether the ground truth falls within the set), and the human's engagement with the set is more nuanced than simple accept/reject.
2. Methodological Rigor
The framework is mathematically well-defined and internally consistent. The classification taxonomy is derived exhaustively: starting from 32 possible binary combinations and pruning 14 logically impossible ones to arrive at 18 valid patterns. This is clean and verifiable. The decomposition showing that CRR_AI and CRR_self weighted by coverage rates recover overall accuracy (Equation 1) is elegant and provides an important interpretability bridge to existing metrics.
For regression, the metrics are well-motivated extensions of WoA, with explicit handling of edge cases (division by zero). The paper correctly identifies that WoA suffers from overshooting artifacts and lacks quality assessment, and the proposed AIR_quant addresses these through absolute-distance normalization.
However, the paper's empirical validation is limited to stylized examples rather than real experimental data. The four regression cases and the classification isoline analysis are illustrative but constructed. No human subjects study is conducted; the framework is not applied to existing datasets from prior empirical work (e.g., Holstein et al., 2025 or Cresswell et al., 2024). This is the paper's most significant methodological limitation—while the framework is theoretically sound, its practical utility and discriminative power in real settings remain undemonstrated.
3. Potential Impact
The framework fills a timely need. Conformal prediction is gaining rapid adoption in applied ML, and empirical studies of human interaction with prediction sets are multiplying. Currently, these studies largely rely on accuracy or WoA as outcome measures, both of which the paper convincingly argues are insufficient for characterizing reliance behavior. By providing standardized metrics, this work could:
The classification framework's connection to conformal prediction makes it particularly relevant, as conformal methods guarantee coverage at a user-specified rate, meaning CRR_self will naturally have a small denominator—an important nuance the authors acknowledge.
4. Timeliness & Relevance
The paper is well-timed. The convergence of three trends creates demand for exactly this type of framework: (1) the maturation of conformal prediction methods and their deployment in real systems, (2) the growing body of empirical studies on human response to set-valued advice, and (3) increasing regulatory pressure for meaningful human oversight of AI systems. The paper positions itself clearly within this landscape and addresses a bottleneck that multiple research groups have implicitly encountered but not formally resolved.
5. Strengths & Limitations
Strengths:
Limitations:
Additional Observations
The paper is conceptual/theoretical in nature—essentially a position paper with formal definitions. Its impact will ultimately depend on adoption by the empirical human-AI collaboration community. The metrics are simple enough to compute, which favors adoption, but the lack of a reference implementation or application to existing datasets reduces immediate uptake potential. A reanalysis of data from Holstein et al. (2025) or Cresswell et al. (2024) would have substantially strengthened the contribution.
The framing within the AoR-R quadrant space (Figure 2) provides an intuitive visual diagnostic that could become a standard reporting tool if adopted.
Generated Jun 5, 2026
Comparison History (18)
Paper 2 addresses a more fundamental and broadly applicable problem at the intersection of causal inference, strategic behavior, and explainability. Its novel insight connecting post-hoc explanations to strategic OPE is highly creative, bridging multiple active research communities (causal ML, algorithmic game theory, XAI). It provides formal theoretical guarantees (consistency, double robustness) alongside empirical validation. Paper 1, while addressing an important gap in human-AI collaboration measurement, is more incremental—extending existing reliance frameworks to set-valued predictions. Paper 2's broader methodological contributions and cross-disciplinary relevance suggest higher impact potential.
Paper 1 introduces a foundational framework for evaluating human reliance on AI, a critical issue in human-AI collaboration. By addressing set-valued advice across classification and regression, it offers broad, real-world applicability in HCI and decision science. Paper 2, while relevant to AI ethics, applies an existing technique to a specific game environment, resulting in a narrower immediate scope and application compared to Paper 1's overarching metrics.
Paper 2 is more likely to have higher scientific impact because it introduces a first formal, task-general measurement framework for appropriate reliance when AI advice is set-valued—an increasingly common interface for uncertainty communication. The contribution is conceptual and metric-based, making it broadly reusable across HCI, ML, decision science, and AI governance, with immediate relevance to evaluating and designing human-AI systems. Paper 1 is timely and practically useful for time-series workflows, but its impact is narrower (primarily agentic tooling for temporal analysis) and may be more sensitive to fast-moving LLM/agent infrastructure changes.
Paper 1 addresses a fundamental and broadly applicable problem in human-AI collaboration—measuring appropriate reliance on set-valued AI advice—which is novel, timely, and relevant across many domains where AI-assisted decision making occurs. It introduces the first formal measurement framework for this increasingly important setting, filling a clear gap. Paper 2, while methodologically sound, addresses a narrower domain (Japanese veterinary toxicology) with more limited cross-field applicability. Paper 1's contributions are more foundational and likely to influence a larger research community working on human-AI interaction.
While Paper 1 addresses an important human-AI interaction problem, Paper 2 presents a fundamental algorithmic breakthrough by introducing the first machine-learned heuristic guaranteed to be admissible. This represents a major methodological innovation in optimal planning and search. By elegantly combining Lagrangian duals, graph neural networks, and cost constraints to ensure strict admissibility, Paper 2 solves a long-standing challenge in the field. Its rigorous theoretical guarantees and novel integration of deep learning with classical planning give it a higher potential for foundational scientific impact.
Paper 1 addresses a fundamental gap in the growing field of human-AI collaboration by providing the first formal framework for measuring appropriate reliance on set-valued AI advice. This has broad applicability across any domain where AI communicates uncertainty (classification and regression), making it foundational for future research. Paper 2, while rigorous and addressing an important clinical need, is more domain-specific (patient safety triage) and benchmark-focused. Paper 1's theoretical contribution to measuring human-AI interaction with uncertainty communication has wider cross-field impact and longer-term influence on how researchers design and evaluate AI advisory systems.
Paper 1 likely has higher impact: it introduces a timely, broadly useful benchmark (ToolMaze) targeting a major real-world failure mode in LLM agents—tool perturbations and recovery—directly relevant to deployment. The 2D design (topology + perturbation taxonomy) and new metric (PRR) provide a rigorous, reusable evaluation scaffold that can drive progress across agent research, robustness, and systems. Paper 2 is novel and rigorous in HAI measurement, but is more niche to sequential judge-advisor settings and may have narrower immediate uptake than an agent benchmark addressing reliability bottlenecks.
Paper 2 is likely to have higher scientific impact because it offers a general, formal measurement framework for appropriate reliance on set-valued AI advice, spanning classification and regression within a standard human-AI paradigm. Its contributions (new dimensions + metrics) are broadly applicable across HCI, ML, decision science, uncertainty quantification, and evaluation methodology, making it easier to adopt and build upon in diverse studies. Paper 1 is timely and practically valuable with an industry deployment, but it is more domain- and standard-specific (enterprise agentic software knowledge architecture) and closer to a systems/process innovation than a broadly reusable scientific framework.
Paper 2 addresses a critical real-world problem (clinical risk prediction from EHRs) with a concrete methodological contribution (AWARE framework) that demonstrates measurable improvements. It combines novelty (retrieval-aligned tabular foundation models for clinical settings), practical applicability (robust predictions under distribution shift and class imbalance), and rigorous evaluation across multiple cohorts. Paper 1 provides a useful theoretical framework for measuring reliance on set-valued AI advice, which is novel but more niche and incremental—extending existing measurement frameworks rather than solving a pressing applied problem. Paper 2's broader relevance to clinical AI deployment gives it higher impact potential.
Paper 2 is more methodologically rigorous and broadly impactful: it introduces a formal measurement framework and metrics for appropriate reliance on set-valued AI advice across classification and regression, fitting widely used human-AI decision paradigms. This has clear real-world applicability (uncertainty communication, decision support, calibration) across many domains and can become a standard evaluation toolkit. Paper 1 addresses a timely AI-safety problem in self-evolving agents, but its approach relies on an LLM-based simulated oversight framework and empirical results on specific agent systems, which may limit generalizability and foundational impact compared to a general formal framework.
Paper 2 likely has higher impact due to a more broadly applicable methodological contribution: a staged ResNet architecture for constrained optimization with priority-ordered constraint handling, theoretical characterization (infinite-width/GP behavior), and empirical validation across multiple constrained problem classes plus a high-stakes real-world domain (AC optimal power flow). This spans ML theory, optimization, and power systems, offering clear practical utility and timeliness for learning-augmented solvers. Paper 1 is novel and useful for HAI evaluation but is more specialized and primarily metric/framework oriented.
Paper 2 is likely to have higher scientific impact because it introduces a first formal measurement framework for appropriate reliance on set-valued AI advice—an increasingly common uncertainty-aware interface. Its contributions (new dimensions/metrics for both classification and regression in a sequential judge–advisor setting) are broadly applicable across HCI, AI evaluation, decision science, and policy, and can standardize empirical studies. Paper 1 is innovative and practically useful for AutoML/agentic search, but it is more engineering- and benchmark-driven with impact concentrated in ML systems, whereas Paper 2 offers a generalizable conceptual/measurement foundation with wider cross-field uptake.
Paper 1 introduces a novel theoretical framework addressing a significant gap in human-AI collaboration research—measuring appropriate reliance on set-valued AI advice. This is a foundational contribution with broad applicability across classification and regression tasks, offering new formal metrics that can be adopted by the wider AI-human interaction community. Paper 2, while practically relevant, is primarily an empirical comparison study in a narrow clinical domain (headache medicine) with limited methodological novelty beyond applying existing RAG-based LLMs and standard evaluation rubrics.
PersistBench addresses an urgent, timely safety concern in LLM-based conversational systems with long-term memory—a rapidly growing deployment area. Its identification of two novel risk categories (cross-domain leakage and memory-induced sycophancy), evaluation across 18 models, and striking failure rates (53% and 97%) provide immediately actionable findings with broad impact across AI safety, NLP, and product development. Paper 1, while rigorous and novel in extending appropriate reliance frameworks to set-valued advice, addresses a more niche topic with less immediate breadth of impact and practical urgency.
Paper 1 establishes a novel formal framework for measuring appropriate reliance on set-valued AI advice, addressing a significant gap in human-AI collaboration research. As AI systems increasingly communicate uncertainty through prediction sets and intervals, this framework provides foundational metrics that will be widely adopted. Its breadth spans classification and regression, and it addresses a fundamental measurement problem. Paper 2, while technically sound, addresses a more narrow problem (tool-graph planning via diffusion) with incremental improvements over baselines, limiting its broader impact across fields.
Paper 2 likely has higher scientific impact: it introduces a general formal framework and metrics for appropriate reliance with set-valued AI advice, a timely and growing practice for uncertainty communication. The contribution is broadly applicable across human-AI interaction, decision science, and ML evaluation, and can standardize measurement in many experimental paradigms (classification/regression, sequential settings). Paper 1 is novel and practically useful for LLM prompting, but its core claim is tied to chat-template artifacts and may be less generalizable across future model interfaces; its impact is strong but narrower and potentially more transient.
Paper 2 addresses a critical and highly timely challenge in LLMs—reasoning reliability and hallucinations—using a multi-agent framework with critic-guided feedback. This approach has broad applicability across AI domains and aligns with current high-impact trends in agentic workflows. Paper 1, while novel, focuses on a specific niche in human-AI interaction evaluation metrics, limiting its immediate breadth of impact compared to core advancements in LLM reasoning capabilities.
Paper 2 is more novel and broadly impactful: it introduces a first formal measurement framework for appropriate reliance on set-valued AI advice, addressing a timely gap as uncertainty-aware AI outputs become common. The metrics generalize across classification and regression and apply to many human-AI decision domains (medicine, finance, policy, HCI), increasing cross-field uptake and citations. Methodological rigor is higher due to formal definitions and evaluation dimensions, whereas Paper 1 is a task-specific engineering approach (multi-model + curriculum) with incremental novelty and narrower applicability, despite clear practical relevance to medical QA.