Is this data science professor a victim or a villain?

Apparently, some secretive entity is trying to maximally embarrass Forbes magazine as much as possible. One by one, they are exposing people on Forbes' 30 under 30 or 40 under 40 list of young entrepreneurs as fraudsters. Elizabeth Holmes has already been found guilty of fraud in her medical tech startup. Sam Bankman-Fried is being sued for running a type of Ponzi scheme in his crypto startup. And now, Charlie Javice is being sued by JPMorgan for faking customer lists in her college financial aid app startup (named Frank).

According to the Wall Street Journal (link with paywall), an unnamed data science professor played a role in this fraud, and that definitely caught my attention.

The unnamed professor, who was paid $18,000, used computer-generated information to show Frank’s purported user base, including details like names, birthdays and schools they attended, the suit alleged...“Our plan was to sample first name and last name independently and then ensure none of the sampled names are real,” the suit alleges the professor wrote to Ms. Javice.

This sounds very bad. Apparently, JPMorgan alleged that about 90% of the data were faked.

However, I don't think we can read from this excerpt whether the professor has also been duped, or the professor was part of the con. It could be the case that Javice contracted the professor to generate simulated data, supposedly for some legitimate purpose.

The other reason this case strikes a chord with me is that many of the emerging data science/AI technologies can easily be exploited to deceive and hurt people, and there does not appear to be much serious discussion of this problem.

 

P.S. [1/19/2023] My friend Mark P. sent me a note saying that some people on Twitter who has read the lawsuit learned that Excel is featured in it. Apparently, JP Morgan got suspicious when they received an Excel spreadsheet with the customer data, which cut off at ~1 million rows, which is the row limit for Excel files.

I have to say I don't really understand this point because I'd have thought that their IT people didn't realize that 4 million names don't fit into an Excel spreadsheet; I wouldn't have immediately assumed they had fewer customers than they claimed.

Comments

[Michael Droy]

This reads to me much more like a JPM / banking problem than a Data Science problem. JPM was stupid. Much like the SBF scandal (banking/Ponzi/political donations, nothing to do with Crypto but many in authority and in banks were stupid).

[Kaiser]

MD: The data science angle is the professor who used data science tools to enable the fraud. Other than that, there is plenty of blame to go around when it comes to scamming.

[RK]

Usually using econometric statistically method, you can home in some of the doctored data if created by human or some navie company random generator data. The use of AI data modeling using statistical model or well fitted synthetic data set will not create such red flag if part of the due diligence is to run the data for authenticity.

So in some ways, AI could eventually cover up a lot of things when one wants to scam - be it election or data fraud