ML inherits the biases of the data it is trained on. If you want “unbiased” ML models, you must train them on data that is carefully constructed to avoid the biases deemed undesirable.

I remember I took an introduction class in machine learning when I was a student.

I was surprised how important bias removal was. Preparing the dataset was more than half of the work. And the class was more about statistics than anything else.

It wasn't about racial bias, ethics, or anything like that. More like if you feed too many "5" in your handwriting recognition algorithm, it will will start seeing 5 everywhere.

Preparing the dataset is 99.9% of pretty much all ML and data science.

Just like all software engineering is maintenance, all DS is data cleaning.

I don’t know “who”. Please tell me. I’m sorry to sound flippant but I mean this sincerely since I’ve never observed any correlation between downvote-used-as-disagree and any particular beliefs.

I 'vouch'ed for your comment, for what it's worth. I still don't know 'who'.

The dataset linked from this thread on Twitter may serve your needs too?: https://twitter.com/vinayprabhu/status/1307497736191635458?s...

No it isn’t. Look at the massive empty space this person had to construct between the two content areas of those tall images to get it to behave like this. No reasonable person would ever create something like that accidentally in a way that would create this behavior.