Rethinking use-restricted open-source licenses for regulating abuse of generative models-Reference-Cited by-同舟云学术

Rethinking use-restricted open-source licenses for regulating abuse of generative models

Published:2024-02-04 Issue:1 Volume:11 Page:
ISSN:2053-9517
Container-title:Big Data & Society
language:en
Short-container-title:Big Data & Society

Author:

Cui Jonathan¹^ORCID,Araujo David A²^ORCID

Affiliation:

1. Penn State University, University Park, PA, USA

2. Penn State University, Harrisburg, PA, USA

Abstract

The rapid progress of Artificial intelligence in generative modeling is marred by widespread misuse. In response, researchers turn to use-based restrictions—contractual terms prohibiting certain uses—as a “solution” for abuse. While these restrictions can be beneficial to artificial intelligence governance in API-gated settings, their failings are especially significant in open-source models: not only do they lack any means of enforcement, but they also perpetuate the current proliferation of tokenistic efforts toward ethical artificial intelligence. This observation echoes growing literature that points to useless efforts in “AI ethics,” and underscores the need to shift from this paradigm. This article provides an overview of these drawbacks and argues that researchers should divert their efforts to studying deployable, effective, and theoretically grounded solutions like watermarking and model alignment from human feedback to effect tangible changes in the current climate of artificial intelligence.

Publisher

SAGE Publications

Subject

Library and Information Sciences,Information Systems and Management,Computer Science Applications,Communication,Information Systems

Link

http://journals.sagepub.com/doi/pdf/10.1177/20539517241229699

Reference43 articles.

1. Bai Y, Jones A, Ndousse K, et al. (2022) Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862.

2. GPT-NeoX-20B: An Open-Source Autoregressive Language Model

3. Chen M, Tworek J, Jun H, et al. (2021) Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.

4. Clark K, Luong MT, Le QV, et al. (2020) ELECTRA: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555.