Source URL: https://www.schneier.com/blog/archives/2024/12/trust-issues-in-ai.html
Source: Schneier on Security
Title: Trust Issues in AI
Feedly Summary: For a technology that seems startling in its modernity, AI sure has a long history. Google Translate, OpenAI chatbots, and Meta AI image generators are built on decades of advancements in linguistics, signal processing, statistics, and other fields going back to the early days of computing—and, often, on seed funding from the U.S. Department of Defense. But today’s tools are hardly the intentional product of the diverse generations of innovators that came before. We agree with Morozov that the “refuseniks,” as he calls them, are wrong to see AI as “irreparably tainted” by its origins. AI is better understood as a creative, global field of human endeavor that has been largely captured by U.S. venture capitalists, private equity, and Big Tech. But that was never the inevitable outcome, and it doesn’t need to stay that way…
AI Summary and Description: Yes
Summary: The text explores the historical development of AI, its corporate control, and the implications of this dominance on trust and transparency in AI systems. It argues for the need for public-interest-driven AI models that prioritize accountability and openness over profit.
Detailed Description:
The text outlines the evolution of AI from military origins to a corporate-dominated field, expressing concerns about the implications of this shift for trustworthiness and public welfare. Key points include:
– **Historical Context**: AI has deep roots in military funding and research, evolving from tools of the military to products dominated by corporate interests.
– **Corporate Control**: Currently, the AI landscape is characterized by powerful corporations like OpenAI and Meta, which control the training and operation of leading AI models.
– **Trust Issues**: There is a significant lack of historical experience in managing untrustworthy AI systems, raising concerns about how biases are incorporated and whose interests AI truly serves.
– **Transparency and Openness**: The text criticizes the notoriously closed nature of corporate AI systems and calls for more openness. While companies like Meta present their models as “open-source,” they often lack transparency regarding data sources and operational practices.
– **Public Interest Models**: Examples of emerging models from collaborations (e.g., BigScience’s BLOOM and Singapore’s SEA-LION) suggest a path toward creating more transparent and ethically aligned AI systems, prioritized for public good rather than profit.
– **Future Investments**: It advocates for the development of AI as a public good, emphasizing the role of governments and civil society in creating AI systems that serve the public interest, thereby ensuring accountability and trust.
In conclusion, the text presents a critical examination of the current AI landscape, urging stakeholders to pursue alternatives that prioritize public welfare and ethical responsibility in AI development. The shift from profit-driven motives to a focus on societal benefit could lead to more trustworthy and beneficial AI technologies in the future.