How to Choose Mushroom Field Guides in the Age of AI
When people learn that I wrote my PhD on the history of field guides, they often ask me to recommend one. I always say the same thing: I don’t have a comprehensive view of all modern field guides, so choose one – actually, preferably more than one – that focuses on your organisms of interest in your region of interest. Look for field guides that you find easy to use. Field guides are not all structured the same, and everyone draws on different background information and has their own preferences about how to search through them. Whichever field guides you choose should point out the features of each organism that are the most useful in coming to an identification. Ask for recommendations from people in your local clubs or other organizations that deal with your organisms of interest. And don’t expect 100% accuracy from apps that use machine vision to identify organisms – humans make use of more senses than images and their metadata can capture.
Field guides – what they are and are not
Traditional field guides to fungi have been written by experienced mycologists who took the time to examine fungi in the wild and to summarize their features in a consistent way to make that information easy to find. Field guides are structured so that, even if you don’t know the name of a fungus, you have at least two other ways to look it up. The first way is with a key, and the second is to flip through the book to look for similar organisms, since similar organisms are usually clustered together. If you know the name of the fungus, you can also look it up in the index. Good field guides also include information about how to tell apart fungi that can be confused with each other. A good field guide will have illustrations that single out these distinguishing features. Additional contextual information, such as the habitats where the fungi grow and the substrates they grow on, what time of year they appear, and how they smell or even taste is often useful, especially since fungi have a limited number of features that are easy to spot without a microscope. This sort of information is particularly important in mycological field guides because many edible fungi look similar to poisonous species. Mistakes can be deadly. Quality field guides present precise and accurate information, even when they use older names for organisms that are no longer valid. If you look up an old name for an organism online, you should also easily find the current name and further information about it.
Other, less technical types of books about mushrooms, such as foraging books, are much less reliable for identifying fungi. These books may use simpler language and have illustrations more focused on esthetics, but, unlike field guides, they do not have safeguards built in to prevent readers from misidentifying fungi. Even though some of them use the term “field guide” in their titles, they lack keys and other ways of grouping organisms’ descriptions together to help readers find similar-looking species. They are also not focused on specific taxonomic groups or regions, and the illustrations are not designed to enable similar taxa to be told apart. That said, they are marketed as field guides and people are buying them assuming that they can be used to identify fungi. For this reason, they deserve to be discussed along with field guides here even if they were published ‘for entertainment purposes only’ and are full of misleading or even blatantly incorrect information.
This brings us to the recent scandal. A controversy blew up on social media in late August about AI-generated paper field guides to mushrooms spreading inaccurate information about how to tell apart poisonous and nontoxic mushrooms. Most of them are narrative-type foraging books, though some have “field guide” in their titles. The flood of AI-generated mushroom publications was identified as part of a wave of low-effort content produced in get-rich-quick schemes that bank on people buying books on trending topics from Amazon without checking to see whether they are any good. Other material produced as part of these schemes include AI-generated children’s storybooks, colouring books, travel guides, and books on trending subjects in other genres, in both print-on-demand and audiobook formats. Initially, participants in the schemes hired ghostwriters, but this role devolved to AI as soon as the technology became available (MycoMutant, 2023b. The references they provide about how the scam evolved are worthwhile reads/listens).
These books were heavily criticized on social media. Mycology enthusiasts were warned to look for older mushroom guides produced before the scam books started to be published and were provided with lists of field guides vetted by experienced mushroom huntersi. The criticism quickly spread to more mainstream news sources in articles that cite mycologists or mycological societies about the dangers of trusting AI-generated mushroom books (Cole, 2023; Milmo, 2023). Amazon soon withdrew a number of them, but some are still for sale (September 9th) and the incentive to produce low-effort content as a money-making scheme remains.
Given all this, we should ask whether the problem is truly AI, or whether AI is just magnifying pre-existing problems. Consider that people use AI-driven image classification apps all the time now, often uncritically. (We will examine this technology in another article). The AI technology in the low-quality book scandal is generative AI, which is AI that creates content, in this case, text, though generative AI can also create images and audio. It’s worth looking more in depth at this use of generative AI, and what it means mushroom identification.
Generative AI and AI-generated paper field guides
Large language model-style generative AI programs have been developed to mimic human speech or writing. One of the most famous such programs is Chat GPT, which can “chat” like a person (the GPT stands for generative pre-trained transformer, the technical term for the type of AI used). Generative AI is being used in various fields to both summarize and generate nontechnical writing. AIthough AI writing tends to be monotonous in structure and can also include details that do not necessarily back up the points being made, writers can fix these problems easily enough that it can save time. Generative AI is already commonly used to produce marketing materials (Botco.ai, 2023).
What generative AI models have not been developed to do is to prioritize accurate information. They will “hallucinate” (make things up or lie) because plausible-looking output was all that mattered when they were being trained (O’Brien, 2023). Generative AI can and often does produce harmful content. It has already been shown to create inedible, and, in some cases, toxic recipes (McClure, 2023). “Oreo vegetable stir-fry” and “bleach-infused rice surprise” sound absurd because we humans have the life experience to recognize that these are not worth eating. Likewise, experienced writers, editors, academics, and other subject matter experts can generally tell AI-generated content apart from quality writing in their areas of expertise. Unfortunately, it is practically impossible to tell it apart from low-quality writing. AI is, of course, also being trained and used to detect AI-generated writing. But generative AI keeps getting better and better. As of now, even OpenAI, the company that produces ChatGPT, has withdrawn its AI writing detection tool because it produces too many false positives and negatives (Kirchner, Ahmad, Aaronson, & Leike, 2023 & Leike; see also Karjian, 2023). In the long run, generative AI is expected to get good enough at producing original content to put many careers at risk – a major concern behind the ongoing Hollywood writers’ strike (Klippenstein, 2023).
But what does this mean for field guides?
Despite the outcry about AI-generated mushroom identification books, I believe that the concern about AI in field guides is misplaced. For instance, I interviewed the author of one “field guide” sold on Amazon (with “field guide” in the title) that was rumoured to be written either entirely or in part by AIii because of its inaccuracies and the insufficient details it provided about the mushrooms covered. The author was actually unfamiliar with AI; they agreed to author a book after a publisher contacted them on Instagram and gave them a template to fill in, in line with other “field guides” the publisher also put out on a variety of other trending topics. The author also told me that the majority of details they had included about the ecology of the mushrooms they wrote about (habitat, associations with plants, altitude, etc.), were removed by the publisher because they did not fit into the publisher’s template. The publisher’s removal of content and poor copyediting made the book look like it was written by AI when it was not!
Why don’t publishers do better?
Whether or not AI was used in producing a text, the important thing is to consider whether a book is doing what it says it will do. In the case of the not-AI field guide, it is recognizably not a good quality guide – but it still got published. In many ways, this is no surprise. Bad-quality field guides are not new. There have always been people who overestimate their own abilities to communicate useful information, and people more interested in being a published author than any other consideration. The real problem is not AI, a tool that can be used for good or bad, but publishers and distributors who do not care about quality content as much as they should, coupled with a lack of critical evaluation by purchasers. Low-quality field guides would not have the reach they currently have if publishers did not publish them and Amazon and other sites did not distribute them. Publishers would have no incentive to publish them if the public recognized how bad they were and did not buy them. Unfortunately, the internet incentivizes clicks, not accuracy. Most of the people who would purchase this sort of book are finding them either through Amazon directly (where 50% of all online product searches start in 2023) or through a search engine (the second-most popular place to start searches, at 31.5%) (Power Reviews, 2023). The low-quality books deliberately make use of terms that people will search for in their titles so that they come up in these searches (a technique known as SEO, search engine optimization). Quality content alone does not drive clicks, disincentivizing publishers to act as gatekeepers of quality. Ultimately, the responsibility of choosing good field guides falls on consumers. The main victims of inaccurate information in low-quality books will be people new to mushroom identification who buy them because they don’t know better. In the case of the low-quality not-AI guide, mycologist Christian Schwarz, who reviewed it, said both in his review and in personal conversation that he learned of the book’s existence when he saw it in the hands of a child on a foray he was leading (2023). Things could have gone very badly for that child and their family if Mr. Schwarz were not there to help them.
The take-home message is that it is much better to give a beginner an older, more accurate guide to mushrooms that has stood the test of time than a newer book of questionable provenance, even if it is aesthetically pleasing or inexpensive. When in doubt, ask your local mycological society for help.
Will AI-written guides ever be useful?
In a word, yes. But not any time soon.
AI-based tools that search vetted literature for accurate information and that accurately and precisely summarize that information using technical language are under development in medicine, math, and other fields, but it will take a while before they become available for use for the identification of biological specimens. The two major obstacles are costs and the quality of data used the AI models. Large language models cost hundreds of thousands to billions of dollars to develop and train (Patel, 2023) and to run (Goldstein, 2022; Li, Yang, Islam, & Ren, 2023). As for data quality, improvements in AI image identification rely on humans accurately labelling the images the AI is trained on. Producing a large number of accurately identified photos of a diverse array of fungi is going to take time. (I will discuss further details of how data quality affects the accuracy of field guide apps in the next issue of Mycelium).
Are AI-assisted mushroom ID technologies worthwhile?
So where does all this leave us?
My initial advice on how to choose a good field guide still stands. Anyone familiar with mushrooms should be able to look through a book or app and assess whether or not it is reasonable. Beginners should ask people experienced with mushroom identification for recommendations. Whether a particular book or app is comfortable to use depends on personal preference and what background information a user can draw on. It’s always good to consult multiple guides because none of them is comprehensive or equally good at everything.
The main thing to know about learning about mushrooms is that the best way to learn is to get outside and encounter the organisms for yourself. Field guides are, well, guides, not replacements for experience. And AI is not at the stage where we have to worry about whether or not we’re in the Matrix. . . at least, not yet.
Sara Scharf is the Director of Business Intelligence at a Toronto cybersecurity company and has been freelancing as an academic editor for over 20 years. She wrote her master’s on mushroom farming technology, and her PhD focused on the history of field guides. Sara Scharf is also the Vice-President of the Mycological Society of Toronto.
