So far, we have only learned that AI is not infallible. It does make errors, and those errors can have life-changing or life-threatening consequences. But, can we crucify AI models for that? Or, should we punish the AI developers who created it?
Data Analytics courses
That’s why AI needs to explain itself!
Explainable AI is the most neglected specialization with Data Science. While most Data Analytics courses emphasize on fast-growing and popular subjects in AI such as supervised learning, cognitive learning, Deep Learning and Natural Language Translation, Explainable AI continues to remain a dark subject for analysts and AI trainers.
What is Explainable AI?
AI trainers are on a fast-paced highway trying to make every machine learn to express intelligence in an analytical manner. On a scale of one to hundred, we can safely give their efforts 100, but the resultant output is much less than satisfactory.
That’s because AI algorithms can’t yet explain themselves and why it needs to pick a different algorithm to solve different problems.
Hard to understand! Yes, we agree.
Therefore, it’s very important to define and lay the foundations of Explainable AI (ExAI/ XAI).
XAI is defined as a programmable AI algorithm that is used to describe the various topologies and pathways leading to decision making. XAI is governed by F-A-T. F stands for Fairness, A for Accountability and T for Trust/ Transparency.
While AI not only makes decisions on its own but it may not be explainable at all – and it times, it could even be incomprehensible. Truth is, not all AI need to explain itself. XAI needs to explain itself in these three scenarios:
- Will it make data analytics better and accurate?
- Will it treat data fairly and without any bias by taking shortcuts?
- Will the AI be able to treat data based on constants and variable factors in the analytics?
Data analysts can use various Auto ML models to find the first two points. The third scenario remains the unanswered puzzle for now.
The role of AI in data analytics can’t be overlooked. Yet, we don’t fully comprehend the importance of F-A-T in designing projects for Cloud Computing, Augmented Memory Management, Network Protocols and Spatial intelligence – all these are important components in operations of companies leveraging AI as a service, solution or as a product.
Why Data Analytics Needs XAI?
We are constantly bombarded with malicious ‘deepfakes’ incidents and incidents about how self-driving cars are causing accidents and deaths on road. Sadly, they could all be prevented if AI could explain itself much before the results occur. These are treated as ‘adversarial’ results of AI and deep learning technologies. Some trainers involved with Data Analytics of connected cars proposed that AI tend to take shortcuts.
With highly complex data analytics, AI trainers will be able to make XAI consistent to theoretical models for End-user explanation, Neural translation, Visual Analytics, Image recognition, Human simulation, and finally, the most critical part of AI ML science – Machine-to-Machine Intelligence, driving Edge Computing of the future Industrial Revolution.