Articles

Explaining AI decisions after they happen is a complex issue, and without being able to interpret the way AI algorithms work, companies, including insurers, have no way to justify the AI decisions. They struggle to trust, understand and explain the decisions provided by AI. So, how can a heavily regulated industry, which has always been more inclined to conservatism than innovation, start trusting AI for core processes?

While businesses want to know when a problem has arisen, they are more interested in knowing why the problem arose in the first place. This is where ML Observability comes in.

As organizations scale their AI and ML efforts, they are now reaching an impasse - explaining and justifying the decisions by AI models. Also, the formation of various regulatory compliance and accountability systems, legal frameworks and requirements of Ethics and Trustworthiness, mandate making AI systems adhere to transparency and traceability

With packaged AI APIs in the market, more people are using AI than ever before, without the constraint of compute, data or R&D. This provides an easy entry point to use AI and gets users hooked for more. However, the first legal framework for AI is here! One of the many mandates in the proposal is to make AI systems adhere to transparency and traceability. These additional requirements highlight the ever-increasing need for Explainable AI.

The use of AI and underwriting is given, and organizations have already explored or implemented certain models.