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πŸ”₯ Kareem Carr πŸ”₯ Profile picture
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16 Apr, 4 tweets, 1 min read
live footage from boston. lol. why do i even live here? πŸ˜‚
decided to crack open the windows, turn on my winter playlist and set up my desktop fireplace.
here's the link. it's pretty simple. just a concrete bowl that you can pour rubbing alcohol into. (i'm not getting any money to share this. unfortunately. πŸ˜…. ) amazon.com/colsen-Tableto…
there are other shapes so search the brand for more options!

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More from @kareem_carr

πŸ”₯ Kareem Carr πŸ”₯ Profile picture
5 Apr
This thread walks you through a concrete example of how an algorithm can learn racism. It uses some math but only the minimum amount of math possible and has lots of pictures. It is *very* accessible. If that sounds like your thing read on. πŸ§΅πŸ‘‡
Let's start by learning about statistical bias. Statistical bias is a measure of how well a guessing algorithm is at guessing. It's very straightforward. The bias is the average difference between what an algorithm guesses a value is and what that value actually is. 2/11
The example I'm going to talk about is an algorithm that learns how to measure feelings based on text. We call this measurement a "sentiment score". 3/11
Read 13 tweets
πŸ”₯ Kareem Carr πŸ”₯ Profile picture
4 Apr
I recently saw some reporting by a white conservative journalist which alleges that black Americans don't value education and I wanted to share my personal experience with that as a non-American black man. In 2018, I graduated with a masters in Biostatistics from Harvard. πŸ§΅πŸ‘‡πŸΎ
Traffic was bad on graduation day so I ended up walking through the streets in my full cap and gown. The thing I remember most about that day is all the happy black faces that were congratulating me. Boston is mostly white so it really stood out. 2/6
Black people were literally congratulating me in the streets as I walked passed. Black people that I didn't know were honking their car horns. It clearly meant a lot to all of these people who were probably on their way to work or doing errands. 3/6
Read 6 tweets
πŸ”₯ Kareem Carr πŸ”₯ Profile picture
1 Apr
As a black man, I'm concerned about the tendency for algorithms to exhibit what looks like racial bias. As a statistician, I'm naturally drawn to investigate why this happens But what is "bias"? Surprisingly, the answer depends on what you think it means to be "rational". 1/7
We can think of bias as a type of irrational behavior. So broadly speaking, there are two ways one could define bias in algorithms and this arises from the two major definitions of rationality. These are epistemic rationality and instrumental rationality. 2/7
Epistemic rationality is defined as the part of rationality which involves achieving accurate beliefs about the world. Instrumental rationality is the art of choosing and implementing actions that steer the future toward outcomes that you want. 3/7
Read 9 tweets
πŸ”₯ Kareem Carr πŸ”₯ Profile picture
1 Apr
Want to know what kinds of bias are fixable with statistics and how?

Read on... πŸ§΅πŸ‘‡

This is a simple mental map of how different biases affect the process of using algorithms to make changes to the physical world. The way we can fix each bias is as follows...
- Data selection bias: you need an accurate mathematical model of the data creation process

- Statistical bias: you need good statistics

- Bias due to generalization: you need an accurate mathematical model of the observations in the data and in the target population 2/7
To fix the "bias due to causal assumptions", we need to fix all 3 smaller biases. At that point, if your model fits the data well then it should be a very close match to the world. In this case, correlation IS causation and we can say the inputs CAUSE the outputs. 3/7
Read 9 tweets
πŸ”₯ Kareem Carr πŸ”₯ Profile picture
30 Mar
Jon (@jonst0kes) wrote a thoughtful article about this weekend's events. I don't think he's a fan of "woke" politics but he's pretty good about not making his views the main focus of the piece. "On Saturday, March 27, Kareem Carr stepped on a...landmine" doxa.substack.com/p/understandin…
I don't know what I think of John's sociological analysis but I also don't have a better explanation for why people who I've been friendly with and supportive of for most of my time on Twitter suddenly turned on me. I don't think it's because I was "wrong" because I wasn't.
John argues that I was attacked because I'm proposing a solutions-oriented approach. I can definitely find tweets where my critics were saying one of the "dangerous" myths I was promoting was that there were fixes for bias in algorithms.
Read 9 tweets
πŸ”₯ Kareem Carr πŸ”₯ Profile picture
27 Mar
FOUR things to know about race and gender bias in algorithms:

1. The bias starts in the data

2. The algorithms don't create the bias but they do transmit it

3. There are a huge number of other biases. Race and gender bias are just the most obvious

4. It's fixable! πŸ§΅πŸ‘‡
By race and gender bias in algorithms, I mean the tendency for heavily data-driven AI algorithms to do things like reproduce negative stereotypes about women and people of color and to center white male subjects as normal or baseline. 2/9
While race and gender bias in algorithms *is fixable*, the current fixes aren't easy. They require us to understand and then mathematically model the processes that generate the biases in the data in the first place. 3/9
Read 16 tweets

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