- Published on January 23, 2023
- In AI Features
Council Post: The Fault in AI Predictions: Why Explainability Trumps Predictions
While everyone focuses on model manufacturing, the right product teams have started emphasising the fundamentals of good AI Solutions. XAI is the 101 feature to achieve it. However, the vision of achieving trustworthy AI is incomplete without Explainability. The idea that Explainability will provide insights into understanding model behaviours is, however, currently only serving the needs of AI experts.
- By Vinay Kumar Sankarapu
The last few years have seen tectonic shifts in fields of artificial intelligence and machine learning. There have also been plenty of examples where models failed and model predictions have created troubling outcomes, creating stumbling blocks to adopting AI/ ML—especially for mission-critical functions and in highly regulated industries. For example, research shows that even though algorithms predict the future more accurately than human forecasters, forecasters decide to use a human forecaster over a statistical algorithm. This phenomenon—which we call algorithm aversion—is costly and it is important to understand its causes. This gave rise to Explainable AI (XAI).
What is XAI?
In machine learning, Explainability (XAI) refers to understanding and comprehending the model's
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Vinay Kumar Sankarapu
Youngest member in 'AI' task force setup by Commerce and Industry Ministry of India to propose and recommend the path/policies for 'AI' adoption in India, Forbes Asia 30 under 30 member in technology; Public speaker in industry and Tech conferences like GTC (Nvidia), TEDx, Re-work, Nasscom etc; Bachelors and masters from IIT Mumbai, Published Author of two novels, Received an excellence award for my research in my third year of college, Research on particle formation and predictive modeling in Laser Ablation.
Founded arya.ai, a Deep Learning startup in my fourth of college. Since then, I have been heading research and product in Arya.ai with key focus in building advanced Technology for enterprises.
I believe, technology should be easy to use and be an enabler for building a great product. The deliverables and capability of the product depends on depth of the technology advancements. 'Deep learning' is one such technology that has huge potential and can solve many of the todays complex problems. We wanted to take the research level advancements of Deep Learning to the hands of every developers and enterprise by simplifying and automating the complex steps such that they can leverage it and build next generations products.
Our key focus is in building tools and systems to simplify the complex tasks of building 'Deep Learning' systems.
Interests: Deep learning, AI, Particle Physics.
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