A Redundancy-Adjusted Artificial Age Score Decision Support System for Oversight-Aware Human–Robot Collaboration

• The proposed AAS-DSS converts six normalized event components into an interpretable structural decision burden score for human-robot collaboration.
• Conceptual redundancy is accounted for using Jaccard overlap to adjust component contributions and prevent redundant double-counting.
• The framework separates total structural burden from review decisions, distinguishing ordinary operation from supervisory review and mandatory human review.
• Illustrative warehouse scenarios demonstrate that mandatory review can be triggered by extreme environmental uncertainty even when task hazard severity is low.
• The AAS score measures structural decision burden and oversight needs rather than chronological age, biological aging, or direct physical safety certification.

1 Introduction
2 Methodology
3 Illustrative Scenarios and Computational Results
4 Discussion and Implications
5 Limitations and Future Work
6 Conclusion
Appendix A Computational Implementation of the AAS-DSS