Today Artificial Intelligence (AI) has evolved into a key differentiator for organizations to achieve and showcase competitive advantage. With technology and innovation ruling the roost, an AI winter seems a distant memory. Yet, as the predictive potential of AI models grow exponentially with new machine learning algorithms, so does the complexity making them challenging to interpret. The models’ almost perfect, precise findings open a debate on the need for an explanation to comprehend and trust the conclusions. Especially when the consequences involve and affect human safety.
With the trust factor under the cloud, the moot question is whether AI has to be explainable and what explainability entails. The answer lies in explainable Artificial Intelligence (XAI). By presenting unique techniques to explain the basic thinking process of AI systems, XAI can help eliminate bias and enable trust.
Take a deep dive into this AI explainability whitepaper that explains in detail about
- Explainable Artificial Intelligence (XAI)
- The Need for Explainable AI
- The Trust hierarchy and XAI’s role as an enabler
- How businesses can get a strategic advantage
- The best practices and the trade-offs involved
- Interpretability and future-proofing AI assurance
