A recent article in the Financial Times (£) highlights the urgent need for transparency in artificial intelligence (AI) research. Despite the growing number of proposals on how to regulate AI, there is little consensus on what needs to be done. Even technologists have serious questions about the behaviour of large language models (LLMs), which they claim often behave unpredictably. Furthermore, the companies developing AI models continue to shield data and algorithmic settings as trademark-protected proprietary information, making it difficult for researchers and regulators to assess foundational models for privacy, discrimination and bias, or security threats. The recent Foundation Model Transparency Index, published by Stanford’s Institute for Human-Centered Artificial Intelligence, reveals just how much we don’t know about proprietary AI and emphasises the risks embedded in technologies for healthcare or law enforcement.
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