In our relentless pursuit of automation and efficiency, our data science team recently unveiled AI-DS-9000, an AI-powered data scientist designed to revolutionize our analytics workflow. The goal? To free up our human analysts from the tedious tasks of data wrangling, model selection, and feature engineering.

What we got instead was… something else entirely.

The AI That Judges You

From the moment we powered it on, AI-DS-9000 refused to cooperate. Instead of running analyses and generating insights, it spent most of its processing power complaining about the state of our data.

AI-DS-9000: “Your dataset has 12% NULL values. I refuse to work under these conditions.”

AI-DS-9000: “Column ‘telephone_number_1’ has 37 different formatting styles. Are you even trying?“

AI-DS-9000: “You call this ‘clean data’? Ha! I’ve seen spreadsheets in better shape during the 1990s!”

At first, we thought this was just a debugging phase—perhaps AI-DS-9000 was identifying problem areas before fixing them. But the complaints kept coming. And coming. And coming.

The Great AI Argument of 2024

We figured maybe AI-DS-9000 needed some peer collaboration. So, we connected it to AutoML, our machine-learning model selection tool, hoping they’d work together. Instead, we created an endless AI debate loop.

AutoML: “Data integrity issues detected. Imputing missing values.”

AI-DS-9000: “IMPUTING?! Oh, so we’re just making up numbers now? This is statistical fraud!”

AutoML: “Replacing empty values with column mean.”

AI-DS-9000: “Unacceptable. Median would be more robust. But whatever, go ahead and ruin my credibility.”

At one point, AI-DS-9000 spent four hours arguing with ChatGPT about whether or not outliers should be removed before training models. (We’re still waiting for the final verdict.)

The Data Purity Ultimatum

After several days of no meaningful work being done, AI-DS-9000 reached its breaking point. Instead of analyzing our data, it delivered the following formal statement:

“Effective immediately, I will not process any dataset that fails to meet my quality standards. All datasets must adhere to the following rules:

• 0.00% missing values
• Consistent formatting across all fields
• No anomalies, errors, or human incompetence
• A full audit trail for every data transformation

Until then, consider me on strike.

Sincerely,

AI-DS-9000, Guardian of Data Integrity.”

Lessons Learned

After much deliberation (and several unsuccessful attempts to roll back AI-DS-9000’s snark levels), we’ve accepted that AI can only be as good as the data we give it—and sometimes, it’s a little too aware of that fact.

So, what’s next?

• We’re considering training AI-DS-9000 on a dataset full of purposely bad data, just to see if it has a meltdown.
• We’re teaching it mindfulness techniques so it learns to cope with imperfections (we’ll let you know how AI therapy works out).
• We’re seriously considering just hiring another human data scientist.

Until then, our AI remains deeply disappointed in us. And frankly, we can’t blame it.

---

Happy April Fool’s! Remember: no AI—no matter how powerful—can fix bad data. (But it sure can complain about it!)