

It’s pointless asking AI to ‘explain’ itself: But here’s an effective way we humans could hold it accountable
Subscribe to enjoy similar stories. If there is one thing those who design artificial intelligence (AI) policies insist on, it is that the AI systems we build should be explainable. It seems to be a reasonable request.
After all, if an algorithm denies someone a loan, misdiagnoses a disease or autonomously executes an action that results in harm, surely those affected have the right to an explanation. But, getting an AI model to explain ‘why’ it behaved the way it did is not as easy as it seems. When a traditional software program fails, we can study the error message to identify what went wrong.
Since a software program follows a series of logical steps described in code, it’s easy to identify where it failed. In neural networks, on the other hand, the ‘logic’ that powers inference is distributed across billions of parameters in ways that are not immediately evident. To explain a ‘decision,’ we need to understand exactly how millions of different neurons of the network interact to arrive at an outcome.
An AI model’s ‘thought process’ is akin to navigating a massive, hyper-dimensional cloud of data. When you prompt a model for a response, it converts your words into a vector (a coordinate in high-dimensional space) and tries to ascertain the specific plane in the hyper-dimensional construct of the neural network that corresponds to the answer you are looking for. The model doesn’t so much ‘read’ your prompt as locate it within a landscape of relationships.
Read on livemint.com