Fake “AI Experts” on LinkedIn – and How to Spot Them Quickly

Daniel Faggella

Daniel Faggella is the founder and CEO at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and many global enterprises, Daniel is a sought-after expert on the competitive strategy implications of AI for business and government leaders.

Fake "AI Experts" on LinkedIn - and How to Spot Them Quickly

Finding the right people to do a job has always been a problem, especially when it requires a high level of expertise. Hiring professionals (84%) are relying more and more on social media to find the right talent, and B2B executives often look to LinkedIn for leads when it comes to finding the right companies to provide crucial services. It is no wonder that people and companies hoping to catch their attention make a point of putting up a robust profile on LinkedIn. However, businesses looking for companies to provide artificial intelligence services need to look at these profiles carefully.

Here at Emerj, we specialize in assessing artificial intelligence companies to figure out what firms have real promise and are likely to deliver on their value proposition. We survey numerous artificial intelligence companies every week and we’ve found that 60% or more of the companies that list AI on their homepage are not in fact doing any AI at all. They lack the requisite talent to leverage and apply AI in the first place, which means they lack the ability to back up any claim that they are doing AI.

For that reason, in this article, we’re going to share some of our tips for determining which companies and “experts” on LinkedIn actually have artificial intelligence experience and which don’t. These tips are based in large part on one of our core articles on three rules of thumb for cutting through the AI hype. Business executives looking to assess AI vendor companies should take the time to read that article as well.  

It is important to note that we are not saying business leaders should definitely do something with AI at their company and engage with a vendor simply because they do AI. Business leaders should only do so if AI will help them achieve their business goals, and not at any other time.

However, if a business needs to hire AI talent or work with a company with AI competence, then they should know how to discern if that self-purported AI company is legitimately offering AI. This article should help business leaders see through three common deceptions that companies and individuals might employ:

  • Use an Impressive Title
  • Fluff Up Their Job Descriptions
  • Fabricate Relevant Academic History

By the end of this article, business leaders should be able to determine if an individual or company on LinkedIn is likely to have AI expertise or not.

Using an Impressive Title

A business leader looking for an AI expert on LinkedIn might first make a search for “data scientist,” “data expert,” or “data engineer.” These are all impressive-sounding titles, which is why people use it for themselves or their employees. However, anybody can appropriate a fancy title. If one looks closely, one might find the individual doesn’t have the experience to support that title.

For example, we once looked through a medium-sized AI-related company with 50 or 60 employees on LinkedIn. The company listed 20 of those employees as data experts or data consultants on LinkedIn. At first glance, the company seemed to have the requisite talent necessary for offering an AI solution. However, a closer look told a different story.

As it turns out, many of these “data experts” were newly minted college graduates from a nondescript university in Israel. That is not a bad thing, necessarily, except that some of them had degrees in political science, history, and other degrees that had nothing to do with data science. But their job titles implied they are data experts.

Obviously, a person who has only been on the job for a couple of months cannot be an expert on data, let alone someone with a degree in Asian Art History. This is not even an isolated case. Many companies do this, and while it is not illegal, it is clearly a tactic to mislead a potential client into thinking a company is full of people who are familiar with data and data science.

Another title often used fraudulently is “data scientist.” One marketing firm we looked at employed over 100 people, including over a dozen with the title “data scientist” on LinkedIn. When we looked at these people with data scientist titles, it turns out they were all people with regular marketing backgrounds. Some of them had a PR, press, or digital marketing background, and some had little more than an undergrad degree. Instead of having marketing titles, however, they had data scientist titles.

The title of “developer” is also often misused in this space, implying that their employees are experts in AI development or machine learning engineering. In most cases, however, this refers to people with experience in developing WordPress websites or iPhone apps.

Many of these “developers” or “ML engineers” will be in an Eastern European country such as the Ukraine or Belarus or occasionally from India. The one thing they usually have in common is they do not have the background to qualify them as AI developers or machine learning engineers.

It is easy enough to check one way or the other by looking more closely at the profile. When a business leader scrolls through a LinkedIn page of an individual or company, they need to look carefully at people who are “data scientists.” Check their academic information to see if they have a robust background in computer science, physics, statistics, or machine learning. See if they have at least a master’s degree in any of these majors. As we mentioned in our rules of thumb article, the science is hard, the math is hard, and the level of data infrastructure and subject-matter expertise needed to derive real value from AI in business is rare.

Aside from academic background, business leaders can also check to see if that “data scientist” has the work experience that qualifies them for the title. A person who worked in a large company such as Google or Facebook as a data scientist has probably earned the right to the title. However, most vendors don’t employ data science talent from marquee companies, and so they will often take entry-level roles and enhance them with fancy titles to fluff the artificial intelligence expertise behind the company.

Fluffing Up Job Descriptions

Another tricky thing companies and individuals do to make them seem more interesting in the AI space is to fluff their job descriptions. This involves changing the description of what one does in their current company or changing what they did at a previous company.

For example, a CTO or “artificial intelligence expert” at a small company might use quite a bit of ML or AI terminology in their job description on LinkedIn, claiming responsibility for building ML functionality or data pipelines. There might also be some mention of other impressive-sounding work they supposedly did with ML and AI.

However, business leaders will find that in many cases when they look at the job descriptions of the companies they worked for previously, these have no basis in fact. Alternatively, they might say they worked with machine learning at their previous company, but upon further researching that company, they don’t meet the standard for legitimacy we lay out in our rules of thumb article linked above. This “CTO” might have had absolutely nothing to do with data science or machine learning in past work. This is a red flag.

It is quite easy to identify a company that is likely to employ this tactic by looking at its size and source of funding. It is highly unlikely that one will find CTOs or other experts with little or no actual expertise in AI or ML at a company that has raised $50 million or more from top investment firms in Silicon Valley. Generally, the smaller and less funded a company is, the more likely it is to see these kinds of lies.

Business leaders can also look at past roles the person in question occupied. If the person worked in a very large established company such as Google, Fidelity, Amazon, or Geico, business leaders can probably trust the job description there. Someone who has worked for a large company is not likely to lie about that because there is much more accountability. If a potential employer or client were to check references for a made-up job title or job description from such a company, and they probably will, that person’s lie is not going to hold up. It may even have legal consequences.

However, if that person’s past work involves running a small startup or working for friends, business leaders have no assurance it is true. It is much easier for that person to fluff up the job description in those situations because there is no accountability. No one can find out for sure if the claims on the job description are real.

Anyone can claim, for example, to have developed a special data science method or machine learning models for a 12-person startup they previously worked at. Anyone can make their last role sound interesting and data-science-related in that situation, even if there is no basis for it.

We have come to learn that it is much more likely to see people embellish or outright lie about recent AI experience if they previously worked at smaller firms. They are much less likely to do so if they worked for larger, established firms.

Fabricating Relevant Academic History

A final tip we can give you for assessing LinkedIn profiles for AI expertise is to be on the lookout for creative academic backgrounds. We have actually seen this with disappointing frequency, most commonly with people that have been in business for quite a while.

The most common tactic is to claim to have graduated with a science degree, such as a masters in electrical engineering or a similar degree, and to frame a description of that degree as something related to AI or machine learning. The trick is to convey the impression that they were on the cutting edge of the AI or ML scene when it was in its early stages.

The purpose of this is to capitalize on the fact that so much time has passed that no one can actually refute it. There is practically no risk of exposure. That is until one speaks with the person.

We had somebody on the podcast once who worked at a large IoT company. They claimed to have a reasonably advanced degree in artificial intelligence from the late 90s. That person purported to work on early machine learning models back in the day.

However, it became evident that this person had no knowledge of machine learning whatsoever several minutes into the interview. This person had no knowledge of even the basic terminology of AI or ML. They got on our program by falsifying a purported academic degree with real experience in the space. We ended up not using that audio and not talking to that company again.

The reason this tactic is more common with older people in the space is not that younger people are more honest. It is because younger people are more likely to be found out if they make false claims about their academic background. If a person graduated from college 5 or 10 years ago, it is quite likely that someone is around to refute the claims: college friends and professors who might be looking at that person’s profile will know it is a lie. However, when things happened over two decades ago, it is much easier to change the facts to suit a purpose.

In some cases, some people have even gone so far as to falsify claims of having patents in the AI field from 10 years ago. They might put down the fact that no one can find the patent to a clerical error. That is certainly possible, but not very probable.

The point is if a business leader finds a LinkedIn profile that describes an impressive academic background in AI or ML dating back 20 or so years ago, but not much else, it pays to be a bit skeptical. If a person’s previous work experience seems somewhat unrelated to the academic degree, and they are now working as CTO or the machine learning engineer with an AI company, consider it a red flag. Some creative engineering of the academic past might be at work.

They’re Lying for a Reason

Many people and companies on LinkedIn use these three tactics to create a more impressive profile in the AI space. However, this does not necessarily mean they are not justified in doing so.

The competition out there is fierce, and sometimes a little creative marketing is necessary. Some of these people and companies may exaggerate a little to get the attention they need to get hired or secure a business client. They need to look very advanced and sophisticated, and so they are going to use these kinds of techniques and tactics to survive and get ahead. They are not outright lying about their AI or ML expertise to dupe others.

This article is not about demonizing everyone on LinkedIn who is not completely honest on the profile page. It is about identifying common traps we have seen among the AI vendor companies and “AI experts” that do lie outright about their AI and ML expertise.

Our job here at Emerj is educating the executive audience to help them make smarter, better decisions when using AI to reach business goals. A key step in that is assessing the vendors and experts. If a business leader is looking for AI expertise at a certain level, then this is how they can find the real thing. They should not end up engaging with people that use these fraudulent tactics to exaggerate their artificial intelligence experience if they keep these tips in mind.

We hope that some of these basic filters will be helpful when leaders are looking through LinkedIn. They should help guide them in assessing the actual level of expertise of a particular AI expert or vendor.

Readers that would like to go a little bit deeper into assessing vendor companies, check out our in-depth article, 7 Ways to Tell if an AI Company is Lying About Using AI.

 

Header Image Credit: Shutterstock

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