Debunked: Everyone has a similar experience training AI models

A lot of people read reviews looking for the answer they want. Think about it: if you’re excited to try a new restaurant and you’ve thought about it for weeks, you might skim through the negative reviews and think “oh, those people are just too picky.”

We often see only what we want to see. Most of us don’t open a review page feeling neutral. We arrive with a hunch, whether it sounds too good to be true or this sounds perfect.

Psychologists call this confirmation bias: the tendency to seek out and interpret evidence in ways that fit what we already believe. The skeptic lands on a one-star review and thinks, I knew it. The optimist lands on a five-star review and thinks the same thing. Both walk away feeling the reviews proved them right.

In essence: one person’s experience is only one person’s experience, but the review that sticks is the one that agrees with our preconceptions.

The AI training experience isn’t the same for everyone. Often, it might not even be the same on different projects! It all depends on several things that differ from person to person, project to project, and platform to platform:

• Your domain and background determine which projects you can be matched to

• Timing matters — projects launch when clients need them and close when the required data is collected, so some contributors start tasks right away while others wait for a project in their field

• Assessments and performance play a part, since specialist projects require real professional depth

• Location affects eligibility, for legal and payment-processing reasons

• How you use the platform shapes how it feels — treating it as supplemental income lands very differently from relying on it as your main source

Two people can join the same platform in the same week, have completely different experiences, and both describe it honestly. That includes the less glowing side. Inconsistent task availability after onboarding and limited feedback when an application or task is rejected are real frustrations that contributors can encounter.

Reviews often tend to skew high or low, while the middle is hard to find. That’s because something has to influence a person to write a review and that’s typically an emotion — negative or positive. Someone who just had an okay experience typically won’t write a review.

A study of Amazon reviews for books, DVDs, and videos found most products had a J-shaped distribution, meaning lots of five-star ratings, some one-star ratings, and hardly any in between. The authors concluded that the average star rating is a poor proxy for quality, because of biases in how review systems collect opinions.

Our own public ratings show exactly that J-shaped distribution. As of publishing, we have roughly 1,100 reviews on Trustpilot and a score of about 3.5. Look at the visual breakdown and you’ll see:

• 5-star ratings make up about 44% of all Mindrift reviews

• 1-star ratings come in at about 27%

• The scores in the middle — the people who had an average experience — are much less prevalent

In other words, a mixed set of reviews isn’t a sign that some reviewers are lying. It’s what you’d expect when people with different backgrounds, timing, and expectations all describe the same platform.

If you’re reading platform reviews before applying, treat them as a range, not a verdict. A few tips to keep expectations in check:

• Look for patterns — a complaint or compliment repeated by many people tells you more than a single outlier

• Check whether the reviewer’s field and situation look like yours

• Notice whether the reviewer was treating it as supplemental or primary income

• Read the average reviews, not just the 1-star and 5-star ratings

But most importantly, take reviews with a grain of salt — positive and negative. Negative reviews can scare people off from even trying, while positive reviews can set unrealistic expectations. The experience will be different for everyone!