I was disappointed to hear the news that @timnitGebru was fired from her job as co-lead of the Ethical Artificial Intelligence Team at Google effective immediately.
There are a lot of reasons for my disappointment about @timnitGebru's firing but the most prominent one is representation matters. I get that things are not always going to work out but I like to see our leaders and pioneers treated well.
I know that this kind of unceremonious firing is business as usual in tech but presumably having an Ethical Team at all suggests a commitment (or at least an interest in committing) to not doing business as usual.
Ethics doesn't thrive in darkness so hopeful more information will emerge soon. I'm generally cynical of ethical expressions by corporations (whom I consider psychologically similar to sociopaths) so this outcome is not entirely surprising to me.
However things turn out, I'm sure @timnitGebru will land on her feet. Google needs her more than she needs them. I'm not being snarky. This is just a fact.

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More from @kareem_carr

6 Dec
People keep pinging me about "irregularities" in the election. I guess they figure I'm a straight shooter which is flattering. OK. You want my raw opinion. Let’s go. You might want to sit down for this one.
The design of the US electoral system is IDIOTIC. So...WEIRD, CONTRADICTORY results are just expected behavior.
If the United States of America wanted a reasonable electoral system with reasonable outcomes, they wouldn't have FIFTY DIFFERENT STATES with FIFTY DIFFERENT SYSTEMS.
Read 6 tweets
4 Dec
I think I echo the sentiments of many people of color in tech when I say I'm waiting...waiting to see what Google's level of interest is in making things right with @timnitGebru after forcing her out in such a callous way. Image
I'm sure I'm not the only visible person of color in tech who's gotten a feeler or two from a big tech company. Speaking from experience, such overtures can be rather flattering but I've always sensed the inherent danger.
Speaking truth to power is easy as a free agent and dangerous when that power is the source of your next paycheck.
Read 9 tweets
25 Nov
WHY IS IT SO HARD TO KNOW WHAT TO DO FOR THANKSGIVING 🦃 THIS YEAR AND WHY DON'T WE ALL AGREE? A data analyst's perspective. Thread. 🧵
MANAGING CORONAVIRUS RISK IS HARD BECAUSE IT'S NOT JUST ABOUT THE SCIENCE. Don't get me wrong. It's not completely unrelated to the science either. It's in between. Why? Because it requires us to answer questions that are unanswerable by science alone. Questions like...🧵
1. WHAT'S AN ACCEPTABLE RISK? SCIENCE can't decide this for us. Logic can give us a guide for defining our risk preferences so that they're consistent. For instance, if I'm afraid of LESS risky stuff than coronavirus, it's illogical for me not to fear coronavirus as well.
Read 13 tweets
4 Oct
Don't know what a P-VALUE is?

Don't know why P-VALUES work?

Don't know why sometimes P-VALUES don't work?

THIS IS THE THREAD FOR YOU. 🧵
DEFINITION OF A P-VALUE. Assume your theory is false. The P-VALUE is the probability of getting an outcome as extreme or even more extreme than what you got in your experiment.
THE LOGIC OF THE P-VALUE. Assume my theory is false. The probability of getting extreme results should be very small but I got an extreme result in my experiment. Therefore, I conclude that this is strong evidence that my theory is true. That's the logic of the p-value.
Read 11 tweets
3 Oct
Statisticians like me say CORRELATION ISN'T CAUSATION but that's not the whole story.

There are at least FOUR different scenarios!

A thread. 🧵
1. CORRELATED BY CHANCE. There's always a possibility that variables will correlate by chance. If you have a lot of data, you're almost certain to get a few high correlations. You will know you're in this situation if the same variables are much less correlated in new data.
2. CORRELATED DUE TO STRUCTURE. Clocks are correlated with each other but there's nothing about Clock A that can be changed in order to cause a change in Clock B or vice versa. There is no third thing you can change that will cause both clocks to change. There is no causation.
Read 8 tweets
30 Sep
The reason machine learning algorithms show bias is that the goal of these algorithms is to learn ALL the patterns in the data including the biases. The "bias" is actually the gap between what the data scientist THINKS is being learned and what's actually being learned. 🧵
An interesting feature of this bias is it's subjective. It depends on what the data scientist INTENDED to learn from the data. For all we know, the data scientist intended to learn all the patterns in the data, racism and all. In which case, there is no bias.
Generally, machine learning does not require us to be specific about what patterns we are trying to learn. It just vaguely picks up all of them. This means we often have no clue what was learned and if it is what we intended to learn.
Read 8 tweets

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