AI washing explained: Everything you need to know

What is AI washing?

AI washing is a marketing tactic companies employ to exaggerate the amount of AI technology they use in their products. The goal of AI washing is to make a company's offerings seem more advanced than they are and capitalize on the growing interest in AI technology. AI washing takes its name from greenwashing, which is when companies make false or misleading claims about the positive impact they have on the environment.

AI is a broad term that has touched many different computer technologies. Its definition can be nebulous and flexible, which gives companies room to stretch its meaning and capitalize on its popularity. When the average consumer hears "AI" in relation to a product or service, they might assume the vendor means "generative AI," because the term has been in the public eye recently. However, AI can refer to a broader spectrum of technologies, some of which may not generate heightened interest among consumers and investors if they knew exactly how AI is used in the product.

A 2023 press release from the FTC details some basic questions an organization can answer to determine if it is engaging in AI washing:

• Are you exaggerating what your AI product can do? Performance-enhancement claims need to be unconditional and have scientific proof to back them up.
• Are you promising that your AI product does something more than a non-AI product? There needs to be adequate proof to make this claim, as it may be used to justify a product price hike or influence labor decisions.
• Are you aware of the risks? AI vendors are responsible for the effects of their products once on the market.
• Does the product even use AI? Using an AI tool in the development process does not make a product offering "AI-powered."

The problem with AI washing

On a macro level, AI washing obscures monoculture in the industry. A monoculture in computer science is when a group of computers run identical software. That is, many vendors, each touting their own "unique" AI models, might be using only a few different underlying AI models. They may use marketing to differentiate products that use the same technology. This could lead to a future financial crisis if many financial institutions all rely on the same underlying models.

"Thousands of financial entities are looking to build downstream applications relying on what is likely to be but a handful of base models upstream," Gensler remarked in his Yale speech. This could create an overreliance on the few models and providers being used.

The SEC drafted a rule in July 2023 that would require financial firms to eliminate conflicts of interest in the use of AI tools.

On a micro level, AI washing can deceive consumers, mislead investors and break existing laws surrounding vendor transparency and product disclosures.

Why does AI washing happen?

Vendors might engage in AI washing for a few different reasons:

• Anticipation. Some vendors might advertise AI in their products before it is incorporated. They intend to include AI in the product at some point but advertise as if the product already contains it.
• Raising funds. AI -- and specifically generative AI -- has been a popular trend since ChatGPT's release. Investors want to back products and vendors they believe will have similar success, so advertising generative AI -- whether the product contains it or not -- will attract them. AI washing has drawn comparisons to the dot-com bubble, when businesses would append the words "dot-com" to the end of the business name to boost their valuation.
• Wrong definition. AI is a vague term comprising a lot of specific technologies. If companies don't have clear messaging around the specific AI technology in their product, they may be accused of AI washing by leading consumers or investors who believe that the product uses AI in a different way than advertised, or less than advertised. For example, generative AI is a relatively new term and might be misconstrued or purposely warped to describe a product as AI-powered. Terms such as machine learning or neural networks have been around longer and have more academic and scientific material supporting them to educate potential consumers or investors on their capabilities.

How to avoid AI washing

Following are a few ways that consumers, investors and CIOs can avoid succumbing to AI washing when looking for an AI product:

• Request evidence. When considering AI tools, ask for hard evidence of the way AI is used in product offerings. For example, the buying team might ask what models or code libraries the product uses. Watch out for vague answers and note whether the product relies heavily on a ubiquitous AI model used in many different products.
• Involve IT in purchasing. Because of the buzz around AI, it might be tempting for the buying team to aggressively pursue an AI-powered product and disregard more nuanced technical concerns. Establish a collaborative culture that involves IT in the buying process and keeps the buying team from falling for marketing hype.
• Look at the product holistically. Buyers shouldn't pursue a product solely because it incorporates AI. Buying teams should look at the whole product and consider all the potential benefits and challenges associated -- especially if the AI features are in question.
• Stay up to date on industry news. As AI develops and more companies incorporate the technology, buying teams can learn what does and doesn't work from other companies' trials and tribulations.

Vendors can avoid AI washing by being truthful when labeling a product, avoiding exaggeration and preparing a strong compliance strategy with the in-house legal team to shield against future lawsuits.

Ben Lutkevich is the site editor for Software Quality. Previously, he wrote definitions and features for Whatis.com.