The excitement around Productive AI — particularly Large Language Models (LLMs) — reveals the fact that organisations are advancing the use of AI at an unprecedented pace. Excitement is undoubtedly very precious, but at the same time, it is also important that our feet step on the ground. The truth is that flaws in artificial intelligence technologies and the data on which they are built can pose real risks. This means that organizations are thinking more than ever about building their AI systems in a responsible and controlled manner.

In this paper, we will examine in depth the risks mentioned above and explore how they can be applied to both traditional and productive AI systems by introducing the RAFT (Reliable, Accountable, Fair and Transparent) framework for responsible AI.

So what will you learn when you read this document?

• How can I avoid the pitfalls of productive artificial intelligence?
• What steps can I take to ensure the reliability of my AI models?
• How can I protect my models from prejudice and discrimination?
• What regulations and standards should I pay attention to when developing artificial intelligence?
• How can I maximize the positive impact of productive AI in my organization?