Author
Elizabeth Seger
CASM, Demos
Numerous parties are calling for “the democratization of AI.” But the phrase refers to a variety of goals which can sometimes conflict.
This post, authored by Elizabeth Seger, describes and compares four different meanings of “AI democratization.” Read the full open access paper here.
Introduction
Over the past year discussion about "AI democratization" has surged. AI companies, developer communities, and governments are talking about the importance of, and their commitment to, democratizing AI, but it can be unclear what they mean by it. The term “AI democratization” is being employed in a variety of ways, often causing commentators to speak past one another when discussing the goals, methodologies, risks, and benefits of AI democratization efforts.
This post identifies four different notions of "AI democratization" commonly used—the democratization of AI use, the democratization of AI development, the democratization of AI profits, and the democratization of AI governance.
These notions often complement each other, though sometimes they conflict. For instance, if the public prefers for access to certain kinds of AI systems to be restricted, then the “democratization of AI governance” may require access restrictions to be put in place—but enacting these restrictions may hinder the “democratization of AI development” for which some degree of AI model accessibility is key.
This post drives home two important points: (1) AI democratization is not the same as model dissemination, and (2) the positive value of AI democratization is rooted in how well they respond to the interests and values of those impacted.
Kinds of AI Democratization
Democratization of AI Use
When people speak about democratizing some technology, they often refer to democratizing its use—that is, making it easier for a wide range of people to use the technology. This is how Microsoft uses the term when they discuss their plans to "democratize Artificial Intelligence (AI), to take it from the ivory towers and make it accessible to all." A salient part of the plan is "to infuse every application that we interact with, on any device, at any point in time, with intelligence."
Goals:
The most common goal of democratizing AI use is to distribute the benefits of AI use for many people to enjoy. Benefits include entertainment value (e.g., generating poems with ChatGPT), health and well-being applications, productivity improvement, and other utility functions (writing code, analyzing data, creating art). Many of these benefits could be translated into financial gains for those who effectively integrate AI tools into their workstreams.
However, it is important to recognize that for some AI applications, the benefits of making the technology available for anyone to use can be relatively minor while the risks can be significant. For example, the circle of individuals who would greatly benefit from access to an AI drug discovery tool is relatively small (mainly pharmaceutical researchers), however, these tools can be repurposed to discover new toxins that might be used as chemical weapons.
Methods:
Efforts to democratize AI use involve reducing the costs of acquiring and running AI tools and providing intuitive interfaces to facilitate human-AI interaction without extensive training or technical know-how.
Democratization of AI Development
When the AI community talks about democratizing AI, they rarely limit their focus to democratizing AI use. Much of the excitement is about democratizing AI development—that is, helping a wider range of people contribute to AI design and development processes. For example, Stability AI CEO Emad Mostaque advocates that "everyone needs to build [AI] technology for themselves. . . . It's something that we want to enable because nobody knows what’s best [for example] for people in Vietnam besides the Vietnamese." Toward this end, Stability AI decided to open-source Stable Diffusion meaning that anyone can download and build upon the model so long as they agree to terms of use.
Goals:
The idea is that tapping into a global community of AI developers will accelerate innovation and facilitate the development of AI applications that cater to diverse interests and needs. It is also argued that involving more people (e.g., academics, individual developers, smaller labs) in AI development processes provides a critical external evaluation and auditing mechanism.
Methods:
Various activities can enable productive participation in AI design and development processes. Some strategies provide access to AI models and resources to facilitate AI community engagement—e.g., model sharing, improving computer access, providing project support and coordination. Other strategies help to expand the community of people capable of contributing to AI development processes—e.g., via educational and upskilling opportunities or through the provision of assistive tools.
But again, it should not be assumed that all methods of democratizing AI development are universally desirable. For example, open source model sharing enables more numerous and diverse contributions to model development, but it can also open a door for malicious model modification. The benefits of open-sourcing must be balanced against the risks.
Democratization of AI Profits
A third sense of "AI democratization" refers to democratizing AI profits—facilitating the broad and equitable distribution of value accrued to organizations that build and control advanced AI capabilities. Democratization of profits is often a key concern of governments and civil society worried about widening socioeconomic and power divides and the wellbeing of citizenry.
Goals:
A few sub-aims of democratizing AI profits are to avoid widening a socioeconomic divide between AI-leading and -lagging nations, to ease the financial burden of job loss to automation, to smooth economic transition in case of the rapid growth of the AI industry, and to provide mechanisms for labs to demonstrate their commitment to pursuing advanced AI for the common good.
Methods:
Profits might be redistributed, for instance, through philanthropic giving (e.g., via a commitment to a "Windfall Clause") or the state via taxation. Democratizing AI development may also help democratize profits by decentralizing AI development away from a handful of big tech companies.
Democratization of AI Governance
Finally, some discussions about AI democratization refer to democratizing AI governance. AI governance decisions often involve balancing AI-related risks and benefits to determine if, how, and by whom AI should be used, developed, and shared. The democratization of AI governance is about distributing influence over these decisions to a broader community of stakeholders and impacted populations. It is a concept primarily driven by civil society organizations such as the Collective Intelligence Project and Demos, and also reflected in OpenAI’s Democratic Inputs to AI grant project.
Goals:
The overarching goal of the democratization of AI governance is to ensure that decisions around questions such as AI usage, development, and profits reflect the interests and preferences of the people being impacted.
Important subgoals include decentralizing control over AI away from big tech, navigating complex normative questions about AI that may vary between cultures, and ensuring the benefits and burdens of AI development and deployment are distributed justly and fairly.
Methods:
Proposed methods for democratizing AI governance decisions include harnessing existing democratic government structures, convening international multistakeholder bodies to deliberate on complex AI governance challenges, and employing promising modern participatory and deliberative governance approaches enabled by deliberative tools and digital platforms.
Key Insights
AI democratization is a multifarious term with numerous goals and methods by which those goals might be achieved. This observation highlights two important insights.
1. AI democratization is not the same as open-source model sharing.
An AI model is open source if the developer decides to allow anyone to download, modify, or build on the model on their computer so long as they agree to the terms of use. In popular discourse, "AI democratization" and "open source" are often uttered in the same sentence. The implication is that open-sourcing is a necessary step toward democratizing AI, but this is an oversimplification. Open-sourcing is one form of model sharing; model-sharing is one method of democratizing AI development; and democratizing AI development is one aspect of AI democratization.
2. AI democratization efforts are not inherently good.
Sometimes different forms of AI democratization can conflict. A decision to open-source would counter the democratization of AI governance, for example, if it does not respond to the interest and values of those likely to be impacted.
This leads to the last point. Any AI democratization effort—whether model sharing, distributing profits, eliciting stakeholder input, or building intuitive user interfaces—is not inherently good; its value is derived from alignment with the interests and preferences of those who will be impacted.
For this reason, we might think of the democratization of AI governance as taking precedence over the others as the source from which the moral and political value of the "democratization" terminology is derived; the invocation of the "democratization" terminology implies that any decision (to share, restrict, distribute, develop, etc.) is that which a democratic governance process would select.
Carnegie Council for Ethics in International Affairs is an independent and nonpartisan nonprofit. The views expressed within this article are those of the author and do not necessarily reflect the position of Carnegie Council.
