Algorithmic Identity Based on Metaparameters: A Path to Reliability, Auditability, and Traceability
Juliao Braga, Percival Henriques, Juliana C. Braga, Itana Stiubiener
TL;DR
The paper tackles accountability, ethics, and transparency challenges posed by widespread algorithm use, especially in AI and multimodal LLMs. It proposes a three-level DOI-based identification scheme (logic, code, and model artifacts) with enriched metadata, including training data fingerprints and ethical alignment, to enable traceability and audits. A cryptographic authentication protocol and a structured metadata schema are introduced to secure API interactions and support governance. The discussion acknowledges limitations such as centralization costs and the risk of false security, and points to future work integrating DOIs with decentralized identifiers to balance governance with scalability. Overall, the approach seeks to move from formal transparency to qualified transparency that supports democratic governance of algorithmic systems.
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.