As artificial intelligence continues to advance, concerns about its ethical implications and transparency have become increasingly prominent. General AI models, while powerful, often operate as "black boxes," making it difficult to understand and trust their decisions. Functional foundation models (fnFM) offer a solution to these challenges by incorporating eXplainable AI (XAI) capabilities. These models provide transparent and interpretable insights into the underlying factors driving their predictions and recommendations. This transparency fosters trust among stakeholders, ensuring that AI-driven decisions are accountable and compliant with regulatory standards. In this blog, we examine how fnFMs address the ethical and transparency concerns associated with general AI, promoting responsible and ethical AI adoption.
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