When considering AI, you may have potential questions or simply want to understand AI better. Trusting and understanding AI is key to a successful AI transformation. This is especially pertinent in industries with stringent regulations, such as financial services, banking, and healthcare. After all, if the AI gets it wrong, people may be adversely affected.
Regulated industries such as financial services, banking, and healthcare must adhere to stringent requirements. Regulations often require that you can both describe and document your processes in the case of an audit or an inquiry. An organization may need to explain how a customer’s credit score was determined, why they might have been denied a loan, or prove why a specific healthcare treatment versus an alternative was decided upon.
What is Explainable AI?
Explainable AI is a relatively new field of AI that attempts to provide explanations and definitions for AI-made decisions. In other words, Explainable AI helps you “prove the work”—from calculation to decision-making—of an AI. With Explainable AI, you can see what goes into decision-making—and, ultimately, to trust and understand the AI.
How to Explain Your AI Models
Whether you are a data scientist or a business decision-maker it is important to uncover how you create trust and understanding of the AI. Some questions you may have:
- AI can make decisions, but can I trust, or more importantly, how do I understand those decisions and interpret them? You need to trust that the AI models are making the right choices because you are ultimately accountable for the decisions.
- How do you “prove the work” of AI? You need to rationalize decisions for auditors and regulators—such as show steps to decision making, show the transparency behind the models, or provide reason codes.
What you are really asking is “What is my AI thinking?”—or, even more basic, “Can I understand the AI?”