Algorithmic Identity Based on Metaparameters: A Path to Reliability, Auditability, and Traceability
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866909998712356864 |
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| author | Braga, Juliao Henriques, Percival Braga, Juliana C. Stiubiener, Itana |
| author_facet | Braga, Juliao Henriques, Percival Braga, Juliana C. Stiubiener, Itana |
| contents | The use of algorithms is increasing across various fields such as healthcare, justice, finance, and education. This growth has significantly accelerated with the advent of Artificial Intelligence (AI) technologies based on Large Language Models (LLMs) since 2022. This expansion presents substantial challenges related to accountability, ethics, and transparency. This article explores the potential of the Digital Object Identifier (DOI) to identify algorithms, aiming to enhance accountability, transparency, and reliability in their development and application, particularly in AI agents and multimodal LLMs. The use of DOIs facilitates tracking the origin of algorithms, enables audits, prevents biases, promotes research reproducibility, and strengthens ethical considerations. The discussion addresses the challenges and solutions associated with maintaining algorithms identified by DOI, their application in API security, and the proposal of a cryptographic authentication protocol. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16234 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Algorithmic Identity Based on Metaparameters: A Path to Reliability, Auditability, and Traceability Braga, Juliao Henriques, Percival Braga, Juliana C. Stiubiener, Itana Cryptography and Security The use of algorithms is increasing across various fields such as healthcare, justice, finance, and education. This growth has significantly accelerated with the advent of Artificial Intelligence (AI) technologies based on Large Language Models (LLMs) since 2022. This expansion presents substantial challenges related to accountability, ethics, and transparency. This article explores the potential of the Digital Object Identifier (DOI) to identify algorithms, aiming to enhance accountability, transparency, and reliability in their development and application, particularly in AI agents and multimodal LLMs. The use of DOIs facilitates tracking the origin of algorithms, enables audits, prevents biases, promotes research reproducibility, and strengthens ethical considerations. The discussion addresses the challenges and solutions associated with maintaining algorithms identified by DOI, their application in API security, and the proposal of a cryptographic authentication protocol. |
| title | Algorithmic Identity Based on Metaparameters: A Path to Reliability, Auditability, and Traceability |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2601.16234 |