Holy $#!t: Are popular toxicity models simply profanity detectors? [D]

One of the problems with real world machine learning is that engineers often treat models as pure black boxes to be optimized, ignoring the datasets behind them. I've often worked with ML engineers who can't give you any examples of false positives they want their models to fix!

Perhaps this is okay when your datasets are high-quality and representative of the real world, but they're usually not.

For example, many toxicity and hate speech datasets mistakenly flag texts like "this …

machinelearning popular toxicity