News about artificial intelligence (AI) usually falls into predictable categories. Most stories cover news from developers. Others discuss the implications of AI for art and employment. Occasionally, too, hallucinations (AI results that lack context and are sometimes purely imaginary, like Google AI’s suggestion that people eat rocks ) are mentioned. However, one topic that receives little attention is how to detect AI and even guard against it. Behind the publicity, such goals have become a major subject of research, a subset of security and privacy concerns, especially in the case of deepfakes, such as the image of the Pope in a puffer jacket. On GitHub, for example, a search for “detecting AI” returns 196 results, while a search for “deepfakes” returns 1,800. Such efforts fall into three main categories: the need for critical analysis, the development of detection tools, and technologies to identify or disrupt AI.

Researching the Context

The most basic tool for detecting AI is critical analysis, or web literacy, as some call it. Regardless of the name, it amounts to skeptical investigation of text or images. In a series of blogs and YouTube videos, Mike Caulfield, a researcher at the University of Washington’s Center for an Informed Public, defines four moves to detect AI using the acronym SIFT (stop; investigate the source; find better coverage; and trace claims, quotes, and media to original source). Caulfield elaborates on these moves, “First, when you first hit a page or post and start to read it — STOP. Ask yourself whether you know the website or source of the information, and what the reputation of both the claim and the website is. If you don’t have that information, use the other moves to get a sense of what you’re looking at. Don’t read it or share media until you know what it is.” He also warns against getting distracted by side issues in your investigation. One way to begin is with an investigation into the reputation of the author or website. If the claim is your main interest, find “if it represents a consensus viewpoint, or if it is the subject of much disagreement.” Another approach is to discover and evaluate the original source or context, which is often lost on the Internet. What all these tactics have in common, Caulfield summarizes, is that they are all means to discover the context of the text or image.

Such critical analysis is useful in general, and, to someone with an advanced degree, may be second nature. Its disadvantage, of course, is that it takes time, which can make it inconvenient on the fast-paced Internet. Moreover, as countless security cases have shown, when a choice between security and convenience is available, security usually loses. For this reason, software detection or disruption can often be a sensible alternative.

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