Can Generative AI be Egalitarian?
Philip Feldman, James R. Foulds, Shimei Pan
TL;DR
The paper critically examines whether generative AI can be aligned with egalitarian values amid the profit-driven, data-extractive practices of current foundation models. Through OpenAI and Google case studies, it highlights tensions between public-benefit goals and corporate incentives, including governance struggles and safety concerns. It then proposes an egalitarian alternative inspired by the Free and Open-Source Software movement and Wikipedia, emphasizing volunteer-contributed data, transparency, and community governance, supported by examples like Wikimedia and Hugging Face. While acknowledging challenges in data rights, licensing, scalability, and evaluation, the paper argues that a cooperative, open ecosystem could yield more diverse, user-aligned, and trustworthy AI that serves broader societal interests.
Abstract
The recent explosion of "foundation" generative AI models has been built upon the extensive extraction of value from online sources, often without corresponding reciprocation. This pattern mirrors and intensifies the extractive practices of surveillance capitalism, while the potential for enormous profit has challenged technology organizations' commitments to responsible AI practices, raising significant ethical and societal concerns. However, a promising alternative is emerging: the development of models that rely on content willingly and collaboratively provided by users. This article explores this "egalitarian" approach to generative AI, taking inspiration from the successful model of Wikipedia. We explore the potential implications of this approach for the design, development, and constraints of future foundation models. We argue that such an approach is not only ethically sound but may also lead to models that are more responsive to user needs, more diverse in their training data, and ultimately more aligned with societal values. Furthermore, we explore potential challenges and limitations of this approach, including issues of scalability, quality control, and potential biases inherent in volunteer-contributed content.