While AI can be integrated with analytics capabilities to extract valuable and unprecedented insights from enterprise data, there is need for a high-quality data pool that AI systems can leverage. Further, a low signal-to-noise ratio of data can result in an AI model that requires more data to validate the signal and demands extensive human involvement for validation.
The AI model built with such a low signal-to-noise ratio prevents enterprises from deriving value from data and poses challenges for explainability. The business entities linked to limited usable data sets also face complex human-in-loop challenges, prompting them to identify more usable alternate data and extract data from their ecosystem of customers, vendors, suppliers, partners and regulators. In order to remain competitive and retain a leading-edge, businesses must leverage the larger ecosystem for data. We explore how enterprises can manage explainability mandates with post-hoc technique with deep learning.