Artificial intelligence (AI) has been helping us in various applications such as customer service, securities trading, and healthcare. Now researchers have found that AI can be used to predict natural disasters. AI can forecast the occurrence of several different types of natural disaster, using vast volumes of high-quality data, and is therefore now at the nerve center of life and death decisions for human populations. This interesting topic came to us from the MIT Technology Review in their article, “How AI can actually be helpful in disaster response.”

Although various technologies exist for detecting disasters using AI, there are still some limitations to their disaster prediction capability. One of the limits is that, while they can compete with humans in terms of volume and speed of operations, their prediction quality is limited by the input data. Humans collect the data, and it can suffer from inaccuracies and human error. As a result, AI-generated predictions may be erroneous.

This is why explainable AI is so important. It allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.