How to Pivot From a Successful Use Case to a Less Successful One

One of the biggest concerns about AI models is that it could be inherently biased. How do you know if it's biased and what guardrails do you put around that to make sure that your models are less biased than it should be? That is a huge concern, especially in AI in general. In our claims automation solution, we're leveraging a knowledge graph as I had previously stated as part of our solution. So that in itself is removing some of the bias by representing the relationships and connections between entities. The graphs basically provide the systems a more nuanced understanding of context, mitigating against inherent biases.