Explainable AI, in simple terms, is the application of artificial intelligence and its subsets in processes in which the outputs or insights given should be understandable and trusted by human users.
AI is known to be operated in a black box approach where it’s thought processes are hidden and sometimes ominous, leaving the users clueless on how it came up with a particular result. Explainable AI avoids this, as users can know how and why the AI application gave a specific output and also the path it took to end with the result.
This is important so that the user will know the process behind the AI’s decisions and using this, a user will know the variables that are considered before coming up with a result. This can be done through various techniques that show the elements of a decision.
Let’s say in an AI application, a business is using algorithms or machine learning to know the best date to launch a product. The company will input necessary data to a learning model and with this, the AI model will start its analysis. After this, the company is given a particular date that is the best to launch a product.
Now, without explainable AI, the company can be left clueless about how this result is achieved. Instead, explainable AI will give the variables for this decision, such as customers’ active times, reach on social media and so on.
This way, businesses can now trust their AI applications as the results and the thought processes are transparent, making them trust the technology more.
