AI proponents claim to solve all problems just with the addition of their magical-AI pixie dust. But that claim doesn’t hold up in a majority of the cases when dealing with real world situations. The latest example of this is highlighted in Citizen.org’s report “Mushrooming Risk: Unreliable A.I. Tools Generate Mushroom Misinformation” published earlier this week where they found that: “Emerging A.I. technologies are being deployed to help beginner foragers find edible wild mushrooms. Distinguishing edible mushrooms from toxic mushrooms in the wild is a high-risk activity that requires real-world skills that current A.I. systems cannot reliably emulate. Individuals relying solely on A.I. technology for mushroom identification have been severely sickened and hospitalized after consuming wild mushrooms that A.I. systems misidentified as edible”
Some risk comes from the seeming simplicity of using identification apps. Automation bias – the human tendency to place excess faith and trust in decisions made by machines – must be resisted. Because of how these apps are marketed, users may understandably believe that identifying a mushroom is as simple as snapping a photo of the mushroom and allowing the A.I. to deliver a reliable identification.
To identify a mushroom with confidence, a basic understanding of its anatomy is required – an understanding that many casual users lack. A photo of the top of a mushroom’s cap, for example, will almost never provide enough information to identify its species with any degree of confidence. Physical features on the underside of the cap, the cap margin, the stipe (stem), and the base of the stipe all should be taken into consideration, as should the mushroom’s substrate (i.e., whether it’s growing on the ground or on wood, and what species of wood). Some mushrooms bruise when cut, such as from yellow to blue, and whether they bruise and how quickly are additional identifying characteristics. Smell also can be a key identifying feature – and, for experienced identifiers, so can taste (followed by immediately spitting out the tasted portion). A.I. species-identification tools are not capable of taking any factors into consideration aside from the mushroom’s immediate appearance.
Australian poison researchers tested three applications that are often used by foragers to identify wild mushrooms and they found the following:
- The best-performing app (Picture Mushroom) provided accurate identifications from digital photos less than half (49%) of the time, and identified toxic mushrooms 44% of the time;
- In terms of which app was most successful at identifying the death cap (Amanita phalloides), Mushroom Identificator performed the best, identifying 67% of the specimens, compared to Picture Mushroom (60%) and iNaturalist (27%);
- In some of the apps’ misidentification errors, toxic mushrooms were misidentified as edible mushrooms;
A 49% accuracy might sound ok for a first run of the AI datamodel which has no real world impact, but when you take into account that there is a 51% chance that the app is incorrectly identifying toxic mushrooms as edible mushrooms which can (and in fact has resulted) in deaths, you realize that the Apps are actively dangerous and about as accurate as flipping a coin.
My request to everyone trying out AI applications is to use that for reference only and don’t rely on them for expert opinion but instead leverage human expertise in situations where there is a realworld impact.
Source: Washington Post: Using AI to spot edible mushrooms could kill you
– Suramya