Pith. sign in

REVIEW 1 major objections 50 references

Appropriate reliance on set-valued AI advice is measured by new rates for classification and quantity-plus-quality metrics for regression.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.3

2026-06-28 01:49 UTC pith:ZH47X45Z

load-bearing objection This paper gives the first explicit metrics for appropriate reliance on set-valued AI advice but does not show that the four quantities are sufficient without missing dimensions. the 1 major comments →

arxiv 2606.06081 v1 pith:ZH47X45Z submitted 2026-06-04 cs.AI cs.HC

A Framework for Measuring Appropriate Reliance on Set-Valued AI Advice

Ranjan Mishra , Jakob Schoeffer This is my paper
classification cs.AI cs.HC
keywords appropriate relianceset-valued advicehuman-AI collaborationclassificationregressionjudge-advisor paradigmuncertainty communication
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a formal framework to assess how humans rely on AI advice presented as sets or intervals rather than single points, within the sequential judge-advisor setup for both classification and regression. It introduces dimensions needed to evaluate such advice and defines two reliance rates for classification that check whether turning to the AI or staying with one's own judgment is the right move. For regression it adds measures of how much the advice is taken and whether taking it moves the final answer closer to the true value. These metrics are shown to reveal patterns in collaboration that prior point-prediction approaches do not detect. The work matters because AI systems increasingly output uncertain advice in set form, and without tailored measures it is hard to know when that advice is being used well.

Core claim

The central claim is that appropriate reliance on set-valued AI advice is jointly characterized by the correct reliance rate on AI and the correct reliance rate on self in classification tasks, together with the quantity of AI reliance and the quality of AI reliance in regression tasks, and that these metrics capture important nuances in human-AI collaboration overlooked by existing point-prediction measures.

What carries the argument

The four metrics (correct reliance rate on AI, correct reliance rate on self, quantity of AI reliance, quality of AI reliance) that evaluate whether and how set-valued advice is used correctly in the judge-advisor sequence.

Load-bearing premise

The newly defined metrics are sufficient by themselves to characterize appropriate reliance and to capture the nuances that prior measures miss.

What would settle it

An experiment in which participants receive set-valued AI advice, make decisions, and the four metrics fail to separate cases where reliance behavior is intuitively appropriate from cases where it is not.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Human-AI teams using interval advice in classification can now be scored on whether reliance choices are correct.
  • Regression decisions can be broken down into whether the AI set was consulted at all and whether consultation improved accuracy over the initial estimate.
  • Different formats of set-valued advice can be compared directly for how well they support appropriate reliance.
  • Studies of human-AI collaboration can move beyond point-prediction baselines to account for uncertainty communication.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Designers of AI systems might tune set outputs specifically to raise the correct reliance rates or the quality metric.
  • Training for decision makers could use these metrics as feedback to improve how people weigh uncertain AI advice.
  • The same measurement approach could be tested on multi-step decisions that go beyond the single judge-advisor round described.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 0 minor

Summary. The paper develops the first formal framework for measuring appropriate reliance on set-valued AI advice (discrete sets or continuous intervals) within the sequential judge-advisor paradigm. It spans classification tasks by defining two metrics—correct reliance rate on AI and correct reliance rate on self—that jointly characterize appropriate reliance, and regression tasks by defining quantity of AI reliance and quality of AI reliance. The framework is presented as capturing important nuances in human-AI collaboration overlooked by existing point-prediction measures.

Significance. If the metrics are shown to be jointly sufficient for characterizing appropriate reliance without unmodeled dimensions, the framework would fill a clear gap in human-AI collaboration research by extending beyond point predictions to uncertainty-aware advice. The purely definitional approach with no free parameters or fitted quantities is a methodological strength, as is the explicit coverage of both classification and regression.

major comments (1)
  1. [Abstract] Abstract: The central claim that the four defined metrics 'jointly characterize appropriate reliance' and 'capture important nuances... that existing measures overlook' is load-bearing but unsupported. The framework provides no argument or test establishing that these quantities (correct reliance rates for classification; quantity/quality for regression) exhaust the space of relevant human decision-making dimensions, such as confidence calibration, set-size sensitivity, or asymmetric error costs. Without such justification, the sufficiency assumption remains unverified.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and for identifying an important point about the strength of the claims in the abstract. We respond to the major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the four defined metrics 'jointly characterize appropriate reliance' and 'capture important nuances... that existing measures overlook' is load-bearing but unsupported. The framework provides no argument or test establishing that these quantities (correct reliance rates for classification; quantity/quality for regression) exhaust the space of relevant human decision-making dimensions, such as confidence calibration, set-size sensitivity, or asymmetric error costs. Without such justification, the sufficiency assumption remains unverified.

    Authors: We agree that the abstract's phrasing is too strong and that the manuscript provides no formal argument or empirical test establishing that the proposed metrics are exhaustive of all relevant dimensions of human decision making. The framework is a definitional contribution that derives the metrics directly from the structure of set-valued advice in the judge-advisor paradigm: for classification, the two rates capture whether a decision maker correctly follows (or does not follow) the AI when the ground truth is inside or outside the set; for regression, quantity and quality capture the magnitude and benefit of adjustment relative to the initial estimate. These dimensions are not addressed by existing point-prediction measures, which is the sense in which the framework captures nuances they overlook. However, we do not claim the metrics are jointly sufficient for every possible aspect of reliance behavior. We will revise the abstract to replace 'jointly characterize appropriate reliance' with 'provide metrics that characterize appropriate reliance along the dimensions of correctness and benefit of reliance decisions' and will add a dedicated limitations paragraph in the discussion section that explicitly notes additional factors (e.g., calibration, set-size sensitivity, asymmetric costs) that may require separate modeling in future work. revision: yes

Circularity Check

0 steps flagged

No circularity: purely definitional framework with no reductions to fitted inputs or self-citations

full rationale

The paper introduces a new framework by defining four metrics (correct reliance rate on AI/self for classification; quantity/quality of AI reliance for regression) that are stated to jointly characterize appropriate reliance. These are presented as definitional choices within the sequential judge-advisor paradigm, with no equations, fitted parameters, or predictions that reduce by construction to the inputs. No self-citations are invoked as load-bearing for uniqueness or ansatzes, and the central claim rests on the explicit definitions rather than any derivation that collapses into prior results. The framework is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only; no explicit free parameters, axioms, or invented entities are detailed beyond reliance on the judge-advisor paradigm and the assumption that set-valued advice improves decision making.

axioms (1)
  • domain assumption The sequential judge-advisor paradigm is the appropriate setting for evaluating reliance on set-valued AI advice.
    Framework is explicitly developed within this paradigm as stated in the abstract.

pith-pipeline@v0.9.1-grok · 5707 in / 1101 out tokens · 31666 ms · 2026-06-28T01:49:07.361197+00:00 · methodology

0 comments
read the original 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.

Figures

Figures reproduced from arXiv: 2606.06081 by Jakob Schoeffer, Ranjan Mishra.

Figure 1
Figure 1. Figure 1: The sequential judge-advisor setup for set-valued AI advice in classification. A human decision maker [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The AoR-R space for regression, defined by the [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Case of algorithm aversion. The appraiser’s initial estimate ($800K) is far from the ground truth ($610K). Despite well-calibrated AI advice centered at $600K, the appraiser moves their final estimate to $810K, away from both the advice and the ground truth, yielding AIRquant = −0.05 and AIRqual = −0.05. first records an initial estimate H, then sees the interval [L, U] with midpoint M, and eventually subm… view at source ↗
Figure 5
Figure 5. Figure 5: Case of automation bias. The appraiser starts with a good initial estimate ($400K) close to the ground truth ($420K) but defers to a poorly calibrated AI interval centered at $750K, moving the final estimate to $720K. The high AIRquant = 0.91 reflects strong behavioral re￾liance, while AIRqual = −14.00 shows the adjustment was harmful. Case: Appropriate reliance on AI 790 400 500 600 700 800 400 780 House … view at source ↗
Figure 6
Figure 6. Figure 6: Case of appropriate reliance on AI. The ap￾praiser’s initial estimate ($400K) is far from the ground truth ($790K). They adjust their final estimate to $780K, close to the ground truth, yielding high positive scores for both quantity (AIRquant = 0.95) and quality (AIRqual = 0.97) of reliance. while AIRquant = 0.91 shows significant behavioral re￾liance, the catastrophic quality score of AIRqual = −14.00 re… view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

50 extracted references · 2 canonical work pages

  1. [1]

    Proceedings of the 41st International Conference on Machine Learning , pages=

    Conformal prediction sets improve human decision making , author=. Proceedings of the 41st International Conference on Machine Learning , pages=

  2. [2]

    Evaluating the utility of conformal prediction sets for

    Zhang, Dongping and Chatzimparmpas, Angelos and Kamali, Negar and Hullman, Jessica , booktitle=. Evaluating the utility of conformal prediction sets for

  3. [3]

    Conformal prediction for

    Folgado, Duarte and Famiglini, Lorenzo and Campagner, Andrea and Dores, H. Conformal prediction for. International Conference on Artificial Intelligence in Medicine , pages=. 2025 , organization=

    2025

  4. [4]

    Towards human-

    De Toni, Giovanni and Okati, Nastaran and Thejaswi, Suhas and Straitouri, Eleni and Gomez-Rodriguez, Manuel , journal=. Towards human-

  5. [5]

    International Conference on Machine Learning , pages=

    Improving expert predictions with conformal prediction , author=. International Conference on Machine Learning , pages=. 2023 , organization=

    2023

  6. [6]

    arXiv preprint arXiv:2410.01888 , year=

    Conformal prediction sets can cause disparate impact , author=. arXiv preprint arXiv:2410.01888 , year=

    work page arXiv

  7. [7]

    Conformal prediction and human decision making, 2025

    Conformal prediction and human decision making , author=. arXiv preprint arXiv:2503.11709 , year=

    work page arXiv

  8. [8]

    Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty , author=. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    2021

  9. [9]

    Does the whole exceed its parts?

    Bansal, Gagan and Wu, Tongshuang and Zhou, Joyce and Fok, Raymond and Nushi, Besmira and Kamar, Ece and Ribeiro, Marco Tulio and Weld, Daniel , booktitle=. Does the whole exceed its parts?

  10. [10]

    To trust or to think: Cognitive forcing functions can reduce overreliance on

    Bu. To trust or to think: Cognitive forcing functions can reduce overreliance on. Proceedings of the ACM on Human-Computer Interaction , volume=. 2021 , publisher=

    2021

  11. [11]

    To engage or not to engage with

    Lebovitz, Sarah and Lifshitz-Assaf, Hila and Levina, Natalia , journal=. To engage or not to engage with. 2022 , publisher=

    2022

  12. [12]

    Journal of Artificial Intelligence Research , volume=

    Schoeffer, Jakob and Jakubik, Johannes and V. Journal of Artificial Intelligence Research , volume=

  13. [13]

    Explanations, fairness, and appropriate reliance in human-

    Schoeffer, Jakob and De-Arteaga, Maria and Kuehl, Niklas , booktitle=. Explanations, fairness, and appropriate reliance in human-

  14. [14]

    Too Sure for Our Own Good: A User Study on

    Fregosi, Caterina and Vicente, Lucia and Campagner, Andrea and Cabitza, Federico , booktitle=. Too Sure for Our Own Good: A User Study on

  15. [15]

    ``Are you really sure?''

    Ma, Shuai and Wang, Xinru and Lei, Ying and Shi, Chuhan and Yin, Ming and Ma, Xiaojuan , booktitle=. ``Are you really sure?''

  16. [16]

    Understanding uncertainty: How lay decision-makers perceive and interpret uncertainty in human-

    Prabhudesai, Snehal and Yang, Leyao and Asthana, Sumit and Huan, Xun and Liao, Q Vera and Banovic, Nikola , booktitle=. Understanding uncertainty: How lay decision-makers perceive and interpret uncertainty in human-

  17. [17]

    Journal of Forecasting , volume=

    Visualizing uncertainty in time series forecasts: The impact of uncertainty visualization on users' confidence, algorithmic advice utilization, and forecasting performance , author=. Journal of Forecasting , volume=. 2025 , publisher=

    2025

  18. [18]

    Balancing the unknown: Exploring human reliance on

    Holstein, Joshua and B. Balancing the unknown: Exploring human reliance on. ACM Transactions on Computer-Human Interaction , volume=. 2025 , publisher=

    2025

  19. [19]

    Designing for appropriate reliance: The roles of

    Cao, Shiye and Liu, Anqi and Huang, Chien-Ming , journal=. Designing for appropriate reliance: The roles of. 2024 , publisher=

    2024

  20. [20]

    A decision theoretic framework for measuring

    Guo, Ziyang and Wu, Yifan and Hartline, Jason D and Hullman, Jessica , booktitle=. A decision theoretic framework for measuring

  21. [21]

    A survey of

    Eckhardt, Sven and K. A survey of. ACM Computing Surveys , volume=. 2025 , publisher=

    2025

  22. [22]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    Fair conformal predictors for applications in medical imaging , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  23. [23]

    Appropriate reliance on

    Schemmer, Max and Kuehl, Niklas and Benz, Carina and Bartos, Andrea and Satzger, Gerhard , booktitle=. Appropriate reliance on

  24. [24]

    How do we measure over-reliance?

    Mikhaylova, Daria and Turchi, Tommaso and Malizia, Alessio , booktitle=. How do we measure over-reliance?

  25. [25]

    International

    Bengio, Yoshua and Clare, Stephen and Prunkl, Carina and Andriushchenko, Maksym and Bucknall, Ben and Murray, Malcolm and Bommasani, Rishi and Casper, Stephen and Davidson, Tom and Douglas, Raymond and others , journal=. International

  26. [26]

    Do people appropriately rely on

    Raees, Muhammad and Khan, Vassilis-Javed and Lykourentzou, Ioanna and Papangelis, Konstantinos , booktitle=. Do people appropriately rely on

  27. [27]

    Cabitza, Federico and Campagner, Andrea and Angius, Riccardo and Natali, Chiara and Reverberi, Carlo , booktitle=

  28. [28]

    Human Factors , volume=

    Trust in automation: Designing for appropriate reliance , author=. Human Factors , volume=. 2004 , publisher=

    2004

  29. [29]

    Organizational Behavior and Human Decision Processes , volume=

    Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences , author=. Organizational Behavior and Human Decision Processes , volume=. 2006 , publisher=

    2006

  30. [30]

    Foundations and Trends in Machine Learning , volume=

    Conformal prediction: A gentle introduction , author=. Foundations and Trends in Machine Learning , volume=. 2023 , publisher=

    2023

  31. [31]

    Automation and human performance , pages=

    Human decision makers and automated decision aids: Made for each other? , author=. Automation and human performance , pages=. 2018 , publisher=

    2018

  32. [32]

    Journal of Experimental Psychology: General , volume=

    Algorithm aversion: People erroneously avoid algorithms after seeing them err , author=. Journal of Experimental Psychology: General , volume=. 2015 , publisher=

    2015

  33. [33]

    Current Psychology , volume=

    A meta-analysis of the weight of advice in decision-making , author=. Current Psychology , volume=. 2023 , publisher=

    2023

  34. [34]

    Proceedings of the Conference on Fairness, Accountability, and Transparency , pages=

    Bias in bios: A case study of semantic representation bias in a high-stakes setting , author=. Proceedings of the Conference on Fairness, Accountability, and Transparency , pages=

  35. [35]

    A meta-analysis of the utility of explainable artificial intelligence in human-

    Schemmer, Max and Hemmer, Patrick and Nitsche, Maximilian and K. A meta-analysis of the utility of explainable artificial intelligence in human-. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    2022

  36. [36]

    Journal of Machine Learning Research , volume=

    A tutorial on conformal prediction , author=. Journal of Machine Learning Research , volume=

  37. [37]

    Advances in Neural Information Processing Systems , volume=

    Conformalized quantile regression , author=. Advances in Neural Information Processing Systems , volume=

  38. [38]

    Automation bias in the

    Laux, Johann and Ruschemeier, Hannah , journal=. Automation bias in the. 2025 , publisher=

    2025

  39. [39]

    Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency , pages=

    On the quest for effectiveness in human oversight: Interdisciplinary perspectives , author=. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency , pages=

    2024

  40. [40]

    In search of verifiability: Explanations rarely enable complementary performance in

    Fok, Raymond and Weld, Daniel S , journal=. In search of verifiability: Explanations rarely enable complementary performance in. 2024 , publisher=

    2024

  41. [41]

    To err is

    He, Gaole and Bharos, Abri and Gadiraju, Ujwal , booktitle=. To err is

  42. [42]

    Knowing about knowing: An illusion of human competence can hinder appropriate reliance on

    He, Gaole and Kuiper, Lucie and Gadiraju, Ujwal , booktitle=. Knowing about knowing: An illusion of human competence can hinder appropriate reliance on

  43. [43]

    Dealing with uncertainty: Understanding the impact of prognostic versus diagnostic tasks on trust and reliance in human-

    Salimzadeh, Sara and He, Gaole and Gadiraju, Ujwal , booktitle=. Dealing with uncertainty: Understanding the impact of prognostic versus diagnostic tasks on trust and reliance in human-

  44. [44]

    2022 , publisher=

    Tejeda, Heliodoro and Kumar, Aakriti and Smyth, Padhraic and Steyvers, Mark , journal=. 2022 , publisher=

    2022

  45. [45]

    2025 , publisher=

    Gerlich, Michael , journal=. 2025 , publisher=

    2025

  46. [46]

    Explanations can reduce overreliance on

    Vasconcelos, Helena and J. Explanations can reduce overreliance on. Proceedings of the ACM on Human-Computer Interaction , volume=. 2023 , publisher=

    2023

  47. [47]

    You can only verify when you know the answer: Feature-based explanations reduce overreliance on

    Zhang, Zelun Tony and Buchner, Felicitas and Liu, Yuanting and Butz, Andreas , booktitle=. You can only verify when you know the answer: Feature-based explanations reduce overreliance on

  48. [48]

    Effective human oversight of

    Langer, Markus and Baum, Kevin and Schlicker, Nadine , journal=. Effective human oversight of. 2024 , publisher=

    2024

  49. [49]

    Computer Law & Security Review , volume=

    The flaws of policies requiring human oversight of government algorithms , author=. Computer Law & Security Review , volume=. 2022 , publisher=

    2022

  50. [50]

    Understanding trust and reliance development in

    Kahr, Patricia K and Rooks, Gerrit and Willemsen, Martijn C and Snijders, Chris CP , journal=. Understanding trust and reliance development in. 2024 , publisher=

    2024