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Fair Decision Utility in Human-AI Collaboration: Interpretable Confidence Adjustment for Humans with Cognitive Disparities

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
FairnessAI-assisted decision making
Abstract:

In AI-assisted decision-making, human decision-makers finalize decisions by taking into account both their human confidence and AI confidence regarding specific outcomes. In practice, they often exhibit heterogeneous cognitive capacities, causing their confidence to deviate, sometimes significantly, from the actual label likelihood. We theoretically demonstrate that existing AI confidence adjustment objectives, such as calibration and human-alignment, are insufficient to ensure fair utility across groups of decision-makers with varying cognitive capacities. Such unfairness may raise concerns about social welfare and may erode human trust in AI systems. To address this issue, we introduce a new concept in AI confidence adjustment: inter-group-alignment. By theoretically bounding the utility disparity between human decision-maker groups as a function of human-alignment level and inter-group-alignment level, we establish an interpretable fairness-aware objective for AI confidence adjustment. Our analysis suggests that achieving utility fairness in AI-assisted decision-making requires both human-alignment and inter-group-alignment. Building on these objectives, we propose a multicalibration-based AI confidence adjustment approach tailored to scenarios involving human decision-makers with heterogeneous cognitive capacities. We further provide theoretical justification showing that our method constitutes a sufficient condition for achieving both human-alignment and inter-group-alignment. We validate our theoretical findings through extensive experiments on four real-world tasks. The results demonstrate that AI confidence adjusted toward both human-alignment and inter-group-alignment significantly improves utility fairness across human decision-maker groups, without sacrificing overall utility. The implementation code is available at https://anonymous.4open.science/r/FairHAI.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces inter-group-alignment as a novel objective for AI confidence adjustment, aiming to ensure fair utility across decision-makers with heterogeneous cognitive capacities. It resides in the 'Inter-Group-Alignment and Multicalibration-Based Fairness' leaf, which contains only two papers total (including this one). This represents a relatively sparse research direction within the broader taxonomy of five total papers across five leaf nodes, suggesting the work addresses an emerging rather than crowded problem space in fairness-oriented confidence calibration.

The taxonomy reveals that most related work falls into adjacent categories: standard calibration approaches that treat users uniformly, human-AI interaction studies examining behavioral responses to AI recommendations, and ethical frameworks addressing autonomy concerns. The paper's leaf sits under 'Fairness-Oriented Confidence Calibration for Heterogeneous Decision-Makers,' distinguishing it from standard calibration methods that lack explicit fairness objectives. Its sibling paper 'Human Expertise Matters' also addresses cognitive heterogeneity but emphasizes preserving human expertise value rather than directly optimizing for utility fairness through confidence adjustment.

Among 28 candidates examined across three contributions, the inter-group-alignment objective shows one refutable candidate out of eight examined, while the theoretical utility disparity bound and multicalibration approach each found zero refutable candidates among ten examined. This suggests the core conceptual contribution (inter-group-alignment) has some prior overlap within the limited search scope, whereas the theoretical formalization and algorithmic implementation appear more distinctive. The relatively small candidate pool (28 total) indicates this assessment reflects top-K semantic matches rather than exhaustive coverage of the fairness-in-AI literature.

Based on the limited search scope of 28 semantically similar papers, the work appears to occupy a nascent research direction with sparse prior work specifically addressing utility fairness across cognitively heterogeneous decision-makers. The taxonomy structure confirms this is an emerging subfield, though the single refutable candidate for the inter-group-alignment concept warrants careful examination of how the proposed objective relates to existing fairness formulations in AI-assisted decision-making.

Taxonomy

Core-task Taxonomy Papers
5
3
Claimed Contributions
28
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: AI confidence adjustment for fair utility across decision-makers with heterogeneous cognitive capacities. The field structure reflects a growing recognition that AI systems must account for diversity in human decision-making abilities when communicating uncertainty. The taxonomy organizes work into four main branches: fairness-oriented confidence calibration tailored to heterogeneous users, standard calibration approaches that treat all users uniformly, human-AI interaction dynamics examining how people respond to AI recommendations, and ethical frameworks addressing autonomy and uncertainty. Fairness-oriented methods, including the inter-group-alignment and multicalibration-based approaches where Fair Decision Utility[0] resides, explicitly optimize for equitable outcomes across user groups with different cognitive capacities. In contrast, standard calibration techniques focus on statistical accuracy without considering individual differences, while interaction-focused studies like Enhancing Human-AI Collaboration[1] and Human-aligned Calibration[2] explore behavioral responses to AI confidence signals. Ethical frameworks provide normative guidance on balancing system transparency with user autonomy. Particularly active lines of work examine trade-offs between calibration accuracy and fairness, with some studies prioritizing uniform statistical properties and others emphasizing equitable utility distributions. A central tension involves whether AI systems should adapt their confidence reporting to match user expertise or maintain consistent signals across all users. Fair Decision Utility[0] sits within the fairness-oriented branch alongside Human Expertise Matters[5], both addressing how confidence adjustments can compensate for cognitive heterogeneity. While Human Expertise Matters[5] emphasizes preserving the value of human expertise in collaborative settings, Fair Decision Utility[0] focuses more directly on calibrating confidence to ensure that users with varying capacities achieve comparable decision utility. This contrasts with interaction-focused works like Explainable AI Impact[3], which study how explanations shape trust and reliance without explicitly optimizing for fairness across cognitive groups.

Claimed Contributions

Inter-group-alignment objective for AI confidence adjustment

The authors propose a novel objective called inter-group-alignment that constrains the distribution of positive labels to be statistically equal across different human decision-maker groups when they share the same human confidence and AI confidence. This objective addresses utility fairness issues arising from heterogeneous cognitive capacities among decision-makers.

8 retrieved papers
Can Refute
Theoretical upper bound on utility disparity

The authors derive a theoretical upper bound that shows utility disparity is constrained by both the human-alignment level and inter-group-alignment level. This provides actionable insight into how AI confidence should be configured to achieve fair decision utility across groups with different cognitive capacities.

10 retrieved papers
Multicalibration-based AI confidence adjustment approach

The authors develop a cognition-aware multicalibration method that simultaneously achieves both human-alignment and inter-group-alignment objectives. They provide theoretical justification showing this method constitutes a sufficient condition for achieving both objectives, thereby ensuring utility fairness and optimal overall utility.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Inter-group-alignment objective for AI confidence adjustment

The authors propose a novel objective called inter-group-alignment that constrains the distribution of positive labels to be statistically equal across different human decision-maker groups when they share the same human confidence and AI confidence. This objective addresses utility fairness issues arising from heterogeneous cognitive capacities among decision-makers.

Contribution

Theoretical upper bound on utility disparity

The authors derive a theoretical upper bound that shows utility disparity is constrained by both the human-alignment level and inter-group-alignment level. This provides actionable insight into how AI confidence should be configured to achieve fair decision utility across groups with different cognitive capacities.

Contribution

Multicalibration-based AI confidence adjustment approach

The authors develop a cognition-aware multicalibration method that simultaneously achieves both human-alignment and inter-group-alignment objectives. They provide theoretical justification showing this method constitutes a sufficient condition for achieving both objectives, thereby ensuring utility fairness and optimal overall utility.

Table of Contents