Legal Knowledge Management and the Rise of Artificial Intelligence
Robotic Refactoring the Workplace
"AI -The Future of Automotive Industry"
WiFi Networks: Shifting from Providing a Service to Improving the...
Breaking the Stereotypes in the Development of AI
Yves Jacquier, Executive Director, Production Studio Services, Ubisoft
Operationalize Machine Learning
Zongjie Diao, Director of Product Strategy and Management, Data Center Compute Group, CISCO
Where is AI Already Having an Impact on Business?
David Wirt, VP, ASEAN & Greater China, Pure Storage
Artificial Intelligence in the Biopharma and Healthcare Sector
Ronald Dorenbos, Associate Director Materials & Innovation, Takeda
Thank you for Subscribing to CIO Applications Weekly Brief
XAI Simplifies the Functionalities of Black-Box ML Models
With technology set to impact the future industries and societies, XAI comes as a boon to simplify the functionalities of the complex ML models.
FREMONT, CA: Machine learning (ML) is contributing immensely to the businesses as well as the world in general. Use of ML is such transformational incorporation that it is going to disrupt the future industries and societies in unimaginable ways. Combined with other groundbreaking technologies, the effect of ML will have augmented impact across the verticals. As the technology is going to impact the future industries and society alike, it is essential to understand their inner functionalities of the ML models. Explainable AI (XAI) addresses the above issue.
XAI in Brief
XAI is a relatively new field in ML where researchers try to design models that will explain the decision-making process behind ML models. XAI has various research branches but, in general, it either tries to incorporate interpretability into existing ML architectures or tries to explain the results of sophisticated, black-box ML models. The second approach is popular among the researchers that try to explain the functioning of an ML model regardless of the underlying architecture of the model. The approach is termed as model-agnostic XAI.
Need for XAI
With current advances in deep learning, a few million parameters are common for a deep learning model. Comparing the number of parameters with the simple linear regression model will depict the level of complexity involved in these deep learning models. Despite having a massive impact across several industries, deep learning models are still used as black-box systems. It is not ideal as they are used in critical scenarios where their decisions can have a massive societal impact.
Check Out : Top Cloud Consulting Companies
Issues Addressed by XAI
XAI addresses three primary questions that generally arise while developing ML products: Why? When? And How? Here are some of the common questions:
• Why did the ML model predict in a certain way?
• When can the predictions of the model be trusted?
• When can the model deviate from the accurate prediction?
• How can the flaws of the model be corrected?
Common Uses of XAI
The use cases of XAI include all those fields where AI/ML is currently being used. It means that the scope of XAI is open to all the industries as most of them are using some, or the other AI-based solutions.