With all the unhinged hype over ChatGPT stealing everyone’s jobs and AI taking over the world, it’s great to see postiive use cases for Machine Learning (ML) technologies. As usual, eXplainable Artificial Intelligence (XAI) has something to contribute to the ethical landscape of fairness and transparency. In this recent news article we see a concerted attempt to combat fake news with XAI and a pretty sophisticated tech stack.

With the rise of social media and other online platforms, the spread of fake news has become a major problem. Fake news is defined as news stories that are intentionally false and designed to mislead readers. It is often spread through social media and can have serious consequences, such as influencing public opinion and even swaying elections.

XAI is a field of AI that focuses on making machine learning models transparent and explainable. By using XAI, it is possible to detect and filter out fake news, while also providing a clear explanation of how the model came to its decision. One way XAI can help in combatting fake new is through the generation of counterfactual explanations. Counterfactual explanations are used to explain how a model would have made a different decision if the input data had been different. In the context of fake news detection, counterfactual explanations can be used highlight words and phrases that were critical in the classification as fake or not fake. The counterfactual interpretation is that when those phrases are substituted, the news article would flip its classification. In the article, you can see a good example of the SHAP Force Plot highlighting specific text elements that add to or detract from the suspect nature of text extract (the figure labelled “Explainability module developed for multiclass classification").

If we are going to have any chance of finding solutions to the technology driven problems of today, then we need to embrace positive applications of ML and not get carried along with all the negative hype. XAI provides us with many such positive examples.