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30 Oct, 8 tweets, 3 min read
Thread of some posts about diversity & inclusion I've written over the years. I still stand behind these.

(I'm resharing bc a few folks are suggesting Jeremy's CoC experience ➡️ partially our fault for promoting diversity, we should change our values, etc. Nope!)

1/
Math & CS have been my focus since high school/the late 90s, yet the sexism & toxicity of the tech industry drove me to quit. I’m not alone. 40% of women working in tech leave. (2015)

medium.com/tech-diversity… 2/
Superficial, showy efforts at diversity-washing are more harmful than doing nothing at all. Research studies confirm this (2015)

medium.com/tech-diversity… 3/
The primary reason women leave the tech industry is because they are treated unfairly, underpaid, less likely to be fast-tracked, and not given a fair chance to advance. (2016)

medium.com/tech-diversity… 4/
Diversity Crisis in AI, 2016 (written the week we publicly launched fast ai, this has been a core value from the start): fast.ai/2016/10/09/div…

Diversity Crisis in AI, 2017 edition: fast.ai/2017/08/16/div… 5/
Tech Interviews are Terrible. One key problem is that people primarily like to hire people like themselves. Some research, case studies, & guidelines. (2017)

medium.com/@racheltho/how…
6/
Discrimination in tech against people with disabilities and chronic illnesses (2019)

medium.com/@racheltho/the… 7/
The tech industry’s glorification (and often requirement) of long hours means that many people with chronic illnesses & disabilities are unable to work at many tech companies. (2019)
medium.com/s/story/techs-… 8/

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More from @math_rachel

Rachel Thomas Profile picture
19 Aug
new free online course: Practical Data Ethics, from fast ai & @DataInstituteSF covering disinformation, bias, ethical foundations, privacy & surveillance, silicon valley ecosystem, and algorithmic colonialism

cc: @craignewmark

ethics.fast.ai
As @cfiesler showed w spreadsheet of >250 tech ethics syllabi & her accompanying meta-analysis, tech ethics is a sprawling subject. No single course can cover everything. And there are so many great courses out there!

medium.com/cuinfoscience/…

cmci.colorado.edu/~cafi5706/SIGC… Slide showing paper title: "What do we teach when we te
I spent a lot of time trying to cut my assigned reading list down to a reasonable length, as there are so many fantastic articles & papers on these topics. The following list is not at all exhaustive.
Read 13 tweets
Rachel Thomas Profile picture
12 Aug
Videos from @StanfordAIMI Symposium are up! I spoke on why we need to expand the conversation on bias & fairness.

I will share some slides & related links in this THREAD, but please watch my 17-minute talk in full (the other talks are excellent too!) 1/

Slide saying "AI, Medicine, & Bias: Diversifying Your D
While using a diverse & representative dataset is important, there are many problems this WON'T solve, such as measurement bias

Great thread & research from @oziadias on what happens when you use healthcare *cost* as a proxy for healthcare *need* 2/

Another form of measurement bias is when there is systematic error, such as how pulse oximeters (a crucial tool in treating covid) and fitbit heart rate monitors (used in 300 clinical trials) are less accurate on people of color 3/

Read 13 tweets
Rachel Thomas Profile picture
31 Jul
Structural racism can be combated only if there is political will, not more data. Ending racism has to begin and end with political will. Data, while helpful in guiding policy focus, are not a shortcut to creating this will.

aljazeera.com/indepth/opinio… @InterwebzNani 1/ First, "proving" that racism exists is not the sam
Data are not merely recorded or collected, they are produced. Data extraction infrastructures comprise multiple points of subjectivity: design, collection, analysis, interpretation and dissemination. All of these open the door to exploitation. 2/ Second, data extraction can pose a threat to the rights of m
In South Korea, digital COVID tracking has exacerbated hostility towards LGBTQ people.

When UK researchers set out to collect better data on Roma migrants to assess social needs, missteps in data presentation gave rise to political outcry over an "influx" of migrants. 3/ In this context, we also need to consider the precedents we
Read 5 tweets
Rachel Thomas Profile picture
20 Jul
Things to know if you work on medical ML:
- Medical data can be incomplete, incorrect, missing, & biased
- Medical system is disempowering & often traumatic for patients
- Crucial to involve patients & to recognize risk of how ML can end up further disempowering 1/
On bias in medicine (& thus medical data): research shows that the pain of women is taken less seriously than pain of men. The pain of people of color is taken less seriously than pain of white people.

Result: longer time delays, lower quality of care, & worse outcomes 2/
A meta-analysis of 20 years of published research found that Black patients were 22% less likely than whites to get *any* pain medication and 29% less likely to be treated with opioids 3/

bbc.com/future/article… There is particularly robus...
Read 25 tweets
Rachel Thomas Profile picture
19 Jul
Important work is happening in *Participatory ML* and in recognizing that AI ethics is about the *distribution of power*. I want to create a thread linking to some of this work 1/

Don’t ask if artificial intelligence is good or fair, ask how it shifts power. @radical_ai_'s essay in Nature is a great place to start: 2/

Also check out @radical_ai_'s talk from @QueerinAI #NeurIPS2019 on how ML shifts power, and on the questions we should be asking ourselves: 3/

Read 13 tweets
Rachel Thomas Profile picture
19 Jul
Datasets (particularly benchmarks) are infrastructure: a foundation for other tools & tech, tending to seep into the background, shaped by specific aims, seeming natural from one perspective but jarring from another
@cephaloponderer @alexhanna @amironesei
arxiv.org/abs/2007.07399 2. Data Infrastructure In this work, we situate our understa
Focusing on *transparency* of a ML system without plausible actions of being able to change aspects of that system
are a Pyrrhic victory. *Contestability*, however, allows us
to critically engage within the system. to focus on the contingent, historical, and value-laden work
Let's move beyond insufficient training data as
the sole "solution" to discriminatory outcomes.

Gathering
more training data from populations which are already extensively surveilled ignores how data-gathering operations can serve as another
form of "predatory inclusion" Second, we aim to push AI ethics conversation about data bey
Read 4 tweets

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