What Is AI Washing? How to See Through the Hype

Artificial intelligence is transforming industries across the board. From healthcare to finance, companies are racing to incorporate AI into their products and services. AI promises immense benefits like heightened efficiency, deeper insights, and automation of tedious tasks. However, amidst the AI gold rush, a troubling trend has emerged: AI washing.

What Is AI Washing?

AI washing refers to the deceptive practice of misrepresenting a product or service as utilizing more advanced AI than it truly does.

Companies may exaggerate their AI capabilities and benefits to appear more technologically sophisticated. With AI dominating tech headlines, it has become a powerful marketing tool. But as tempting as it is to jump on the AI bandwagon, AI washing can have serious consequences for businesses and consumers alike.

AI washing takes its name from the term greenwashing, which refers to companies making dubious or misleading claims about having a positive environmental impact. Just as some brands exaggerate their eco-credentials through greenwashing, AI washing involves companies exaggerating their use of artificial intelligence for marketing purposes without substance behind the claims. Understanding this parallel helps explain why the practice of inflating AI capabilities became known as "AI washing."

A recent example of AI washing is Equinix, an American multinational company that claims to be a global leader in the data center market. Hindenburg Research published a report accusing Equinix of riding the AI bandwagon by making false claims about its power capacity to meet AI demands. This led to a 30% increase in Equinix's valuation to $80 billion in the past year.

The Rise of AI Washing

Vendors may claim a product uses AI when it actually relies on simple rules-based programming and algorithms. While rules and algorithms do play a role in AI, true AI systems involve technologies like machine learning and neural networks that enable more advanced capabilities like natural language processing, computer vision, and predictive analytics.

AI washing takes advantage of the hype and ambiguity surrounding true AI. By making dubious AI claims, companies aim to appear more innovative, get more media coverage, attract investors, and charge higher prices for their products and services.

Several factors have contributed to the proliferation of AI washing in recent years:

• AI hype: Excitement and optimism around AI have led to intense media coverage and exaggerated claims. This hype motivates companies to jump on the AI bandwagon.

• AI ambiguity: There is no single definition of what constitutes true AI. The lack of clarity enables companies to call any type of automation or rules-based software "artificial intelligence."

• Demand for AI: As interest in AI grows, companies feel pressure to showcase AI capabilities even if their technology is not that advanced. AI washing fills this demand.

• Lack of regulation: Currently, there are no laws or standards governing AI claims in marketing. This regulatory gap enables companies to freely engage in AI washing without legal consequences.

As AI becomes more prevalent, consumers and businesses should be aware of these motivations and strategies for AI washing. Evaluating AI claims critically is key to avoiding deception.

The US Securities and Exchange Commission (SEC) recently penalized two investment advisory companies, Delphia (USA) Inc. and Global Predictions Inc., with a $400,000 fine for AI washing. Delphia claimed to use AI to predict successful companies and trends for early investment, while Global Predictions boasted of being the first AI-regulated financial advisor. However, the SEC found that both companies misled clients and the public about their AI usage.

Examples of AI Washing

Many industries are now rife with AI washing. Here are some common examples of exaggerated or misleading claims about artificial intelligence:

• Chatbots: Customer service chatbots are often touted as using AI when they actually rely on simple rules and scripts. They may have limited abilities to understand natural language or handle new queries. True AI chatbots utilize machine learning to train on diverse conversations and improve their capabilities over time.

• Recommendation engines: Many companies promote product recommendation features as AI-driven. In reality, they frequently rely on simplistic rules like identifying commonly purchased items together. Genuine AI recommendation systems apply complex algorithms to large datasets to uncover non-obvious patterns and predictively model customer preferences.

• Image recognition: Basic image analysis software is sometimes positioned as artificial intelligence for marketing appeal. Most can only identify objects, people, or text that were explicitly programmed for recognition. Advanced AI image recognition leverages deep learning neural networks to understand images contextually and generalize to novel inputs.

• Predictive analytics: Software that forecasts outcomes like future sales by extrapolating historical data is not necessarily AI-enabled. True AI predictive analytics systems can process more variables, recognize complex patterns, and continuously learn from new data to improve predictions.

• Big data analytics: While big data platforms enable the collection and storage of vast datasets, they do not automatically include AI capabilities. AI-powered big data analytics applies machine learning algorithms to large datasets to uncover insights that would not be visible to humans or standard analytics.

These examples reveal how the lines between conventional software and true artificial intelligence are often blurred. As AI washing becomes more common, it's essential for businesses to evaluate vendor claims critically before making purchase decisions. Looking past the AI hype to understand underlying technologies is key.

How to Spot AI Washing

Since there are no official auditing standards for AI, identifying exaggerated claims comes down to asking the right questions and investigating vague assertions about "AI" or "machine learning." Here are some tips for seeing through AI washing:

• Ask how it works: If a company claims their product uses AI, ask for specifics on the techniques used and how they enable intelligent, adaptive behavior. The lack of technical details is a red flag.

• Validate capabilities: Don't take capabilities like natural language processing at face value. Test the product extensively to verify whether supposed AI features actually work as advertised.

• Request third-party evaluation: Ask vendors if their AI claims have been validated by independent testing agencies like AI laboratories and universities. The lack of external validation is suspicious.

• Check qualifications: Examine the qualifications of the vendor's AI team. Do they have recognized experts in AI and advanced degrees in the field? Insufficient in-house talent makes lofty AI assertions doubtful.

• Review training approach: Ask how the AI model was trained. Thorough training on extensive, high-quality data is essential for developing systems that work well.

• Compare competitors: Research competitor products making similar AI claims. If others have more modest claims for similar offerings, it may indicate exaggeration.

• Get examples: Ask for specific case studies and examples demonstrating the claimed AI capabilities in real-world applications over time. The lack of solid examples exposes weak claims.

By probing vendors with these questions and scrutiny, buyers can push past the hype and determine if true AI or mere AI washing is at play.

What AI Really Looks Like

Artificial intelligence is a continually advancing field with many techniques and applications. There is no single formula, but authentic AI solutions exhibit some key characteristics:

• Learning ability: The system’s algorithms have the ability to learn from data without explicit programming. Machine learning techniques enable adaptation and improvement.

• Contextual comprehension: The AI demonstrates an understanding of context and meaning, not just data patterns. For example, comprehending language or analyzing images situationally.

• Generalizability: The AI can extend its training on particular datasets to unfamiliar data and scenarios it has not directly experienced before.

• Memory: The system can store what it learns over time and apply that learning in the future, rather than just responding based on immediate inputs.

• Problem-solving: The AI acts proactively to analyze ambiguous, complex problems and delivers solutions based on reasoning and judgment, not just preset rules.

• Uncertainty handling: The system can make reasonable predictions and suggestions even with uncertain, incomplete data. AI handles ambiguity well.

• Explanation: In many applications, the AI can explain its processes, rationale, and decisions in understandable terms to human users when needed for trust and accountability.

While the techniques behind AI solutions may differ, these hallmarks indicate that a system incorporates true artificial intelligence that evolves and adapts over time, rather than just simple automation. Understanding these AI standards helps separate legitimate applications from AI washing.

The Hype and History of AI Washing

AI washing is not a new phenomenon. It follows a similar pattern to previous technological hype, such as the metaverse mania of recent years, the dot-com craze of the late 1990s, and even the airplane hype of the 1920s.

During the metaverse mania, companies like Facebook (now Meta) heavily promoted the concept, leading to a surge in mentions of the term by various companies, many of which may not have had serious metaverse ambitions. Similarly, during the dot-com bubble, companies added ".com" or "Internet" to their names to attract investors, resulting in significant stock price increases.

In the 1920s, investors were drawn to companies associated with airplanes, even if their actual business had little to do with aviation. One example is Seaboard Airlines, which attracted investor interest despite being a railroad company.

These historical examples demonstrate that companies often try to capitalize on the hype surrounding new technologies, even if their actual capabilities or involvement in those areas are limited. AI washing is the latest iteration of this trend, fueled by the excitement and potential of artificial intelligence.

Moving Forward With True AI

As artificial intelligence advances, AI washing is likely to remain an issue plaguing many industries. To avoid deception and move forward with authentic AI, consumers and businesses should:

• Educate themselves on the true nature of AI and its techniques like machine learning so they can scrutinize vendor claims knowledgeably.

• Ask tough questions that force vendors to back up AI assertions and not rely on buzzwords.

• Consult AI experts to evaluate vendor offerings and determine if they constitute true AI versus exaggeration.

• Advocate for auditing standards within industries to certify legitimate AI and combat false claims.

• Support investments in research and talent development to drive continued AI innovations that live up to the technology’s true potential.

Ultimately, focusing on the reality of artificial intelligence capabilities, not the hype, will lead to smart adoption of AI that delivers genuine benefits and business value. With scrutiny and standards, AI washing can be recognized so authentic AI advances can flourish.