With black box models, you don’t have this kind of transparency. And when they fail, it can be very difficult for you to identify why the model did not perform as you’d expected.
3) Raise trust and reduce bias in your AI systems by being able to check models for fairness and accuracy. XAI explanations show the patterns your model found in your data. This helps your MLOps team (machine learning operations team) trace any errors and evaluate for bias and data integrity.
4) Increase adoption of AI systems as your organization, customers, and partners gain more understanding of and trust in your ML and AutoML systems. Your AI models can then power your predictive, prescriptive, and augmented analytics systems.
5) Ensure regulatory compliance as the reasoning behind your AI–based decisions can be audited to ensure conformity with the growing slate of laws and regulations. For example, California’s recent Consumer Privacy Act (CCPA) stipulates that your users be able to know conclusions made about them by your AI systems and know what data was used to make those conclusions.