Ideological Bias in LLM Sparks Debate on AI Control and Privacy

• Paolo Ardoino advocates user control over AI, challenging big tech’s influence on thoughts.
• LLMs reflect creators’ ideologies, showing varied biases in different languages and regions.
• Tether’s Local AI kit offers a private, decentralized alternative to centralized AI platforms.

Researchers are raising concerns about ideological bias in large language models (LLMs). These models, widely used for tasks like summarization and question answering, reflect their creators’ worldviews. A new study from Ghent University shows how LLMs have different ideological stances based on language, region, and training data.

The study explored ideological differences in LLM responses in English and Chinese. Researchers asked the models to describe historical figures, analyzing the moral judgments in each response. They observed that LLMs respond differently based on language and geographic training. This shows how Western and non-Western LLMs handle sensitive topics, including global conflicts and political figures.

Ardoino Calls for User Control of AI

Paolo Ardoino, CEO of Tether, recently weighed in on this issue. In a post, Ardoino stressed the need for users to control the AI models they use. He warned against the influence of large tech companies, which he says could shape public thoughts through AI. At the Lugano Plan B event, Ardoino introduced Tether’s Local AI development kit as a solution. This privacy-focused kit, uses peer-to-peer (P2P) technology, offering an alternative to big tech-controlled AI models.

Tether’s AI SDK is highly modular and adaptable. Developers can use it across various devices, from budget phones to advanced computers. The open-source kit supports different models, such as Marian and LLaMA, and allows users to store data in P2P structures, ensuring privacy. Tether’s development kit offers a local and private way to execute AI applications.

LLMs and Historical Perspectives

The Ghent University study also found differences in how LLMs address historical and political events. Western models tend to align with Western ideologies. Non-Western models approach these topics differently, highlighting a divide in narrative perspectives. These findings underscore the challenges in building “neutral” AI systems.

Ardoino’s push for a decentralized AI platform reflects a broader desire for privacy in the tech industry. As Tether’s Local AI kit undergoes testing, it represents an emerging trend toward user-controlled, modular AI. This approach could address privacy concerns while reducing dependence on big tech for AI needs.