Algorithmic Identity Based on Metaparameters: A Path to Reliability, Auditability, and Traceability

2026-01-21

AI Summary

The widespread deployment of algorithms—particularly large language models—in high-stakes domains such as healthcare, criminal justice, and finance has intensified challenges surrounding accountability, transparency, and traceability. This work proposes a novel framework that systematically integrates Digital Object Identifiers (DOIs) into algorithmic governance by establishing a unique identity system for algorithms enriched with metadata parameters. Complemented by a dedicated cryptographic authentication protocol and secure API mechanisms, the framework enables end-to-end auditable tracking across the algorithm’s entire lifecycle. It thereby facilitates reliable provenance tracing, bias mitigation, and scientific reproducibility, while laying a verifiable and audit-ready governance foundation for AI agents and multimodal large language models.

Technology Category

Philosophy and Ethics of AI: Safety, Robustness & TrustworthinessNatural Language Processing: Ethics — Bias, Fairness, Transparency & PrivacyHumans and AI: AI for Accessibility

Application Category

Responsible Web: Algorithmic accountability and transparency on the webSecurity and Privacy: Data transparency and provenanceGraph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• algorithm accountability
• transparency
• reliability
• AI ethics
• algorithm traceability

Innovation

• Algorithmic Identity
• Digital Object Identifier (DOI)
• Cryptographic Authentication
• Auditability
• Multimodal LLMs