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

TL;DR

This paper proposes using Digital Object Identifiers (DOIs) to uniquely identify algorithms, enhancing accountability, transparency, and traceability, especially in AI and large language models, through cryptographic authentication and tracking.

Contribution

It introduces a novel approach of leveraging DOIs for algorithm identification, addressing challenges in accountability, auditability, and ethical oversight in AI systems.

Findings

DOIs enable effective tracking of algorithm origins.

The approach improves transparency and reproducibility in AI research.

Cryptographic protocols enhance algorithm authentication and security.

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.