Operational Identity

Defines a finite audit comparing a record system's declared rule of sameness with the rule of sameness induced by its implementation.

Purpose

This repository contains the source for the third paper in the Structural Explainability paper series.

Prior Papers

• Neutral Substrates
• Referential Regimes

Current Version

• PDF

Building Locally

Requires a LaTeX distribution with latexmk:

• MiKTeX on Windows
• TeX Live on Linux
• MacTeX on macOS

On Windows, install Strawberry Perl and MiKTeX.

latexmk -pdf se210-operational-identity.tex

texcount -inc -sum -total se210-operational-identity.tex

Windows:

Get-ChildItem -Path . -Recurse -File | Unblock-File
.\tools\build\clean.ps1
.\tools\build\build.ps1

Companion Resources

• Operational Identity Verification Repository
• Archived Verification Software (Concept DOI)

The verification repository provides executable verification of the finite procedures, classifications, constructions, algorithmic claims, and complexity claims developed in this paper.

Annotations

ANNOTATIONS.md

Citation

See CITATION.cff.

License

CC BY 4.0

SE Manifest

SE_MANIFEST.toml

Validate with:

uvx se-manifest-schema validate-manifest --path SE_MANIFEST.toml --strict

References

1. Arp, R., Smith, B., and Spear, A. D. (2015). Building Ontologies with Basic Formal Ontology. MIT Press.

2. Bowker, G. C., and Star, S. L. (1999). Sorting Things Out: Classification and Its Consequences. MIT Press, Cambridge, MA.

3. Case, D. M. (2026a). Neutral Substrates: A Design Constraint for Shared Records Under Persistent Interpretive Disagreement. arXiv:2601.14271. https://arxiv.org/abs/2601.14271

4. Case, D. M. (2026b). Referential Regimes: Transformation-Invariant Identity for Neutral Substrates. arXiv:2601.16152. https://arxiv.org/abs/2601.16152

5. Case, D. M. (2026c). Structural Explainability Verification: Operational Identity. Software, version 0.1.0, 2026. https://doi.org/10.5281/zenodo.21499599

6. Christen, P. (2012). Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, Berlin.

7. Cochinescu, S. (2026). ECO/CPO-DAG: A Contradiction-Based Accountability Layer for Adversarial Supply Chains. arXiv:2607.06804. https://arxiv.org/abs/2607.06804

8. Elmagarmid, A. K., Ipeirotis, P. G., and Verykios, V. S. (2007). “Duplicate record detection: A survey.” IEEE Transactions on Knowledge and Data Engineering, 19(1):1–16.

9. Fellegi, I. P., and Sunter, A. B. (1969). “A theory for record linkage.” Journal of the American Statistical Association, 64(328):1183–1210.

10. Ferrario, A. (2025). “A trustworthiness-based metaphysics of artificial intelligence systems.” In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’25), pages 1360–1370. ACM. https://doi.org/10.1145/3715275.3732091 https://arxiv.org/abs/2506.03233

11. Ferrario, A. (2026a). High-Risk AI Systems and the Problem of Identity in the European AI Act. arXiv:2605.23922. https://arxiv.org/abs/2605.23922

12. Ferrario, A. (2026b). A Category Theory Account of AI Identity. arXiv:2607.00220. https://arxiv.org/abs/2607.00220

13. Gangemi, A., Guarino, N., Masolo, C., Oltramari, A., and Schneider, L. (2002). “Sweetening ontologies with DOLCE.” In Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web (EKAW 2002), volume 2473 of Lecture Notes in Computer Science, pages 166–181. Springer.

14. Getoor, L., and Machanavajjhala, A. (2012). “Entity resolution: Theory, practice, and open challenges.” Proceedings of the VLDB Endowment, 5(12):2018–2019.

15. Grieves, M., and Vickers, J. (2017). “Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems.” In F.-J. Kahlen, S. Flumerfelt, and A. Alves (Eds.), Transdisciplinary Perspectives on Complex Systems, pages 85–113. Springer.

16. Guarino, N. (1998). “Formal ontology and information systems.” In N. Guarino (Ed.), Formal Ontology in Information Systems: Proceedings of FOIS ’98, pages 3–15. IOS Press.

17. Guarino, N. (1999). “The role of identity conditions in ontology design.” In C. Freksa and D. M. Mark (Eds.), Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science (COSIT ’99), volume 1661 of Lecture Notes in Computer Science, pages 221–234. Springer. https://doi.org/10.1007/3-540-48384-5_15

18. Guarino, N., and Welty, C. A. (2002). “Evaluating ontological decisions with OntoClean.” Communications of the ACM, 45(2):61–65.

19. Guizzardi, G. (2005). Ontological Foundations for Structural Conceptual Models. PhD thesis, University of Twente.

20. Hu, J., Huang, X., He, Q., Sun, Y., Dong, Y., and Huang, X. (2026). Responsible Agentic AI Requires Explicit Provenance. arXiv:2605.17169. https://arxiv.org/abs/2605.17169

21. Kolt, N. (2026). “Superintelligence and Law.” Harvard Journal of Law & Technology, forthcoming. https://arxiv.org/abs/2603.28669

22. Longino, H. E. (1990). Science as Social Knowledge: Values and Objectivity in Scientific Inquiry. Princeton University Press.

23. Masolo, C., Borgo, S., Gangemi, A., Guarino, N., and Oltramari, A. (2003). WonderWeb Deliverable D18: Ontology Library. IST Project 2001-33052 WonderWeb.

24. Moreau, L., and Missier, P., editors. (2013). PROV-DM: The PROV Data Model. W3C Recommendation, 30 April 2013.

25. Nian, Y., Yuan, A., Zhang, H., Li, J., and Zhao, Y. (2026). Auditable Agents. arXiv:2604.05485. https://arxiv.org/abs/2604.05485

26. Ojewale, V., Suresh, H., and Venkatasubramanian, S. (2026). Audit Trails for Accountability in Large Language Models. arXiv:2601.20727. https://arxiv.org/abs/2601.20727

27. Otsuka, T., Toyoda, K., and Leung, A. (2026). AI Identity: Standards, Gaps, and Research Directions for AI Agents. arXiv:2604.23280. https://arxiv.org/abs/2604.23280

28. Papadakis, G., Skoutas, D., Thanos, E., and Palpanas, T. (2020). “Blocking and filtering techniques for entity resolution: A survey.” ACM Computing Surveys, 53(2).

29. Souza, R., Gueroudji, A., DeWitt, S., Rosendo, D., Ghosal, T., Ross, R., Balaprakash, P., and Ferreira da Silva, R. (2025). “PROV-AGENT: Unified provenance for tracking AI agent interactions in agentic workflows.” In Proceedings of the 2025 IEEE 21st International Conference on e-Science, pages 467–473. Chicago, IL. https://doi.org/10.1109/eScience65000.2025.00093 https://arxiv.org/abs/2508.02866

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