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Commenting specifically on the black names example: I am skeptical because they replicated the results of a study on humans based on the Implicit Association Test. I would be generous in calling that the Myers-Briggs of this generation, but it's probably even less valuable other in that it's free to take online so a huge data set is easily available.



Just because humans are biased doesn't mean we have to make our algorithms biased too. This is especially important now that AI and ML are starting to be deployed in non-CS cases.


Sorry to disappoint, but that's impossible.

Socialization is an ambiguous, imprecise activity that demands infinitely many judgements, inferences, and preconceived rules & goals.

What can be interpreted as "jerk" behavior can also be caused by "dork" behavior, and vice-versa.

A joke at someone's expense might be expressing affinity for them or dissing them.

Adopting ebonics speech and tone in one context maybe in-group acceptable in one context, but insulting in another.

Ultimately, the algorithms need enough trial-and-error socializing experience to become, for lack of a better term, cool.


Like, the IAT has problems, but it has been shown to have some predictive validity in behaviour. I am entirely unsurprised that the IAT finding replicated in this data, and it actually supports the structural racism explanation propounded by one of the measure's harshest critics.

To be fair to the IAT, while it has very low test-retest reliability and lots and lots of state variance, there's definitely something in the reaction time differences that is meaningful. How meaningful it is, and whether or not it makes any practical difference remains to be determined.

Anyone who hasn't done one, try this link and see how you feel during the process: https://implicit.harvard.edu/implicit/takeatest.html

And at least there wasn't a massive profit motive behind the IAT, at the beginning anyway :)




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