Council Post: Improve Explainability of Machine Learning Models Using LLMs/GPT Prompts

Responsible AI, emphasizing ethical, fair, and trustworthy AI systems, has gained significant traction due to the growing awareness of potential risks and challenges associated with this technology. Explainability plays a crucial role in achieving this goal, ensuring transparency, accountability, and fairness in AI systems. This text explores how LLMs (Large Language Models) can bridge the explainability gap between data scientists and business users, fostering responsible AI adoption. Current Challenges in Explainability: The current approach to explainability in AI models often suffers from several limitations, creating a communication gap between data scientists and business users. These limitations include: Non-intuitive data variable names: Technical terms and abbreviations