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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.
About the authors
Image of Mahesh Kshirsagar
Mahesh Kshirsagar
Mahesh has a vast experience of about 24 years across various technologies. He has played a key role in addressing ever-changing demands from the business by architecting state-of-the-art IT solutions. As a technology leader, he has successfully incubated several high impact, high revenue potential IT service lines and technology centers of excellence (CoE).
Image of Sanjeev Manchanda
Sanjeev Manchanda
Sanjeev develops futuristic solutions for different industries. With more than 19
years’ experience in working with large companies, he has created more than 200
solutions. Sanjeev has contributed to research in data mining, has written for 13
international journals, and has filed three patents. Sanjeev has done his MBA and
MCA and also has a PhD in computer science
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