The increasing consumption of news online in the 21st century coincided with increased publication of disinformation, biased reporting, hate speech and other unwanted Web content. We describe BiasScanner, an application that aims to strengthen democracy by supporting news consumers with scrutinizing news articles they are reading online. BiasScanner contains a server-side pre-trained large language model to identify biased sentences of news articles and a front-end Web browser plug-in. BiasScanner can identify and classify more than two dozen types of media bias at the sentence level, making it the most fine-grained model and only automatic application deployed as a browser plug-in. One special feature is the high-quality, LLM-generated explanations of the model’s decisions. While prior research has addressed news bias detection, we are not aware of any automatic work that resulted in a deployed browser plug-in (c.f. also biasscanner.org for a Web demo).