Researchers Raise 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.

Large language models (LLMs) are powerful tools that can be used for a wide variety of tasks, from summarizing text to answering questions. However, a recent study from Ghent University has shown that LLMs can also be biased, reflecting the ideological worldviews of their creators.

The study examined ideological differences in LLM responses in English and Chinese. The researchers asked the models to describe historical figures, and then analyzed the moral judgments in each response. They found that LLMs responded differently based on language and geographic training. This was evident in how Western and non-Western LLMs handled descriptions of global conflicts and political figures.

Paolo Ardoino, CEO of Tether, raised concerns about this issue in a recent post. He highlighted the importance of user control over AI models, and expressed wariness about the influence of large tech companies, which he said could be used to shape public thoughts.

At the Lugano Plan B event, Ardoinoを紹介ed Tether’s Local AI development kit as a solution. The privacy-focused kit utilizes peer-to-peer (P2P) technology to offer an alternative to big tech-controlled AI models.

We need to control the models we execute and rely on.

And not let big tech overlords to force and control our thoughts.

A solution https://t.co/1MyRIUXwit

The Tether AI SDK is highly modular and adaptable, allowing developers to use it across various devices, ranging from budget phones to advanced computers. The open-source kit supports different models, such as Marian and LLaMA, and enables users to store data in P2P structures for enhanced privacy. This decentralized approach provides a local and private method of executing AI applications.

The Ghent University study also found differences in how LLMs addressed historical and political events. Western models tended to align with Western ideologies in their descriptions, while non-Western models approached these topics differently, highlighting a divide in narrative perspectives. These findings underscore the challenges in constructing "neutral" AI systems.

Ardoino’s initiative for a decentralized AI platform aligns with a broader trend in the tech industry toward greater privacy. As Tether’s Local AI kit is being tested, it showcases an emerging avenue for modular AI that is controlled by the user. This approach could potentially address privacy concerns and reduce dependence on big tech for AI needs.

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