[2403.13784] The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence
Computer Science > Machine Learning
arXiv:2403.13784
(cs)
[Submitted on 20 Mar 2024 (
v1
), last revised 18 Oct 2024 (this version, v6)]
Title:
The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence
Authors:
Matt White
Ibrahim Haddad
Cailean Osborne
Xiao-Yang Yanglet Liu
Ahmed Abdelmonsef
Sachin Varghese
Arnaud Le Hors
View a PDF of the paper titled The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence, by Matt White and 6 other authors
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Abstract:
Generative artificial intelligence (AI) offers numerous opportunities for research and innovation, but its commercialization has raised concerns about the transparency and safety of frontier AI models. Most models lack the necessary components for full understanding, auditing, and reproducibility, and some model producers use restrictive licenses whilst claiming that their models are "open source". To address these concerns, we introduce the Model Openness Framework (MOF), a three-tiered ranked classification system that rates machine learning models based on their completeness and openness, following open science principles. For each MOF class, we specify code, data, and documentation components of the model development lifecycle that must be released and under which open licenses. In addition, the Model Openness Tool (MOT) provides a user-friendly reference implementation to evaluate the openness and completeness of models against the MOF classification system. Together, the MOF and MOT provide timely practical guidance for (i) model producers to enhance the openness and completeness of their publicly-released models, and (ii) model consumers to identify open models and their constituent components that can be permissively used, studied, modified, and redistributed. Through the MOF, we seek to establish completeness and openness as core tenets of responsible AI research and development, and to promote best practices in the burgeoning open AI ecosystem.
Comments:
28 pages, 4 figures, 2 tables
Subjects:
Machine Learning (cs.LG)
; Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Software Engineering (cs.SE)
Cite as:
arXiv:2403.13784
[cs.LG]
(or
arXiv:2403.13784v6
[cs.LG]
for this version)
arXiv-issued DOI via DataCite
Submission history
From: Cailean Osborne [
view email
[v1]
Wed, 20 Mar 2024 17:47:08 UTC (342 KB)
[v2]
Thu, 21 Mar 2024 18:03:46 UTC (342 KB)
[v3]
Mon, 3 Jun 2024 16:44:31 UTC (56 KB)
[v4]
Wed, 14 Aug 2024 22:47:01 UTC (1,050 KB)
[v5]
Wed, 2 Oct 2024 19:16:19 UTC (1,046 KB)
[v6]
Fri, 18 Oct 2024 08:20:22 UTC (1,464 KB)
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