A Monotone Redundancy-Aware Decision-Burden Method for Explainable Human Oversight in Human–Robot Collaboration
This methodological study develops a monotone, redundancy-aware decision-burden method to provide explainable human oversight recommendations in human-robot collaboration. By replacing an unstable activity-normalized ratio with a pairwise overlap correction, the proposed Artificial Age Score (AAS) extension guarantees mathematical continuity and coordinatewise monotonicity. Through a controlled computational evaluation of 65,536 Sobol profiles, the corrected score achieved a held-out ROC AUC of 0.892, outperforming unadjusted logarithmic and linear baseline aggregation methods without triggering score decreases under input perturbation.
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Who issued it
Who it governs
Researchers and robotics developers seeking mathematically rigorous and explainable decision-support frameworks for human-robot collaboration safety oversight.
What the document contains
The document proposes a mathematical method to integrate six normalized burden inputs—task hazard severity, human safety risk, environmental uncertainty, task criticality, exposure frequency, and intervention complexity—into an explainable supervisory oversight score for human-robot collaboration. It evaluates the score using numerical boundary analyses, coordinatewise perturbation audits, and held-out classification concordance against a rule-based benchmark.
The document does not provide empirical workplace trials, clinical evaluations, or evaluations using human participants; all validations are conducted entirely via synthetic computational profiles.
- Replacing an activity-normalized redundancy ratio with a pairwise overlap correction eliminates sparse-boundary discontinuities in the Artificial Age Score.
- The corrected decision-burden method guarantees coordinatewise monotonicity and non-negative attribution across all input variables.
- A controlled computational study on 65,536 Sobol profiles demonstrated that the corrected score achieved a held-out ROC AUC of 0.892.
- Across 1,179,648 perturbation checks, the corrected method produced zero score decreases, compared to 3,484 decreases for the legacy ratio adjustment.
- A two-stage architecture separates an independent mandatory safety gate from the calibrated score-based supervisory threshold.
· Abstract · 1 Introduction · 2 Related work · 3 Design requirements and event representation · 4 Monotone redundancy-aware method · 5 Computational validation · 6 Results · 7 Discussion · 8 Conclusion
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-24
Human Oversight
5/5Humans remain in control of consequential decisions
Trustworthy human–robot collaboration requires decision-suppo rt methods that expose why human oversight is recommended without allowing an aggregate score to override direct safety conditions.
Abstract
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-24
Transparency
4/5Openness about how AI systems work and are used
First, a pairwise overlap correction replaces the unstable ratio and yields a simple sufficient condition for coordinatewise monotonicity. Second, a compone nt attribution makes the redundancy discount auditable.
1 Introduction
screening score · 2026-10-24