Calculating the souls of Black folk: predictive analytics in the child welfare system
powerful & informative talk by @UpFromTheCracks at @DataInstituteSF Center for Applied Data Ethics seminar series, video now online
If there was a material benefit from the family regulation system (child welfare system), middle class white people would be seeking it out for their kids.
The child welfare system is not biased, it is racist.
Racist in the Ruth Wilson Gilmore sense of the word: racism is a state-sanctioned and/or extralegal production & exploitation of group differentiated vulnerability to premature death.
Recommended books:
Writings, Du Bois
In the Wake
Captivating Technology
Automating Inequality
Catching a Case
Killing the Black Body
The Protest Psychosis
Getting Wrecked
Shattered Bonds
A Cultural Interpretation of the Genocide Convention
The First Civil Right
Misdemeandorland
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Is your machine learning solution creating more problems than it solves? @JasmineMcNealy on how in focusing narrowly on one problem, we may be missing many others @DataInstituteSF CADE Seminar Series
21 States Are Now Vetting Unemployment Claims With a ‘Risky’ Facial Recognition System:
"legitimate claimants have also been rejected by the company’s machine learning & facial recognition systems — leading to massive delays in life-sustaining funds"
Central considerations when designing algorithms:
- *Who* is the prototype?
- Continuous evaluation & auditing
- We need to normalize STOPPING the use of a tool when harm is occurring. We don't need to keep using a tool just because we've already started. @JasmineMcNealy
The founder of @gridai_ (@_willfalcon) has been sharing some falsehoods about fastai as he promotes the pytorch lighting library. I want to address these & to share some of our fast ai history. 1/
Because our MOOC is so well-known, some assume fast.ai is just for beginners, yet we have always worked to take people to the state-of-the-art (including through our software library). 2/
I co-founded fast.ai w/ @jeremyphoward in 2016 & worked on it full-time until 2019. I've been focused on the USF Center for Applied Data Ethics for the past 2 yrs, so I can’t speak about the current as much, but I can share our history. 3/
In computational systems, we are often interested in unobservable theoretical constructs (eg "creditworthiness", "teacher quality", "risk to society"). Many harms are result of a mismatch between the constructs & their operationalization -- @az_jacobs@hannawallach
A measurement model is "valid" if the theoretical understanding matches the operationalization. There are many ways validity can fail.
Some types of Validity
content: does measurement capture everything we want
convergent: match other measurements
predictive: related to other external properties
hypothesis: theoretically useful
consequential: downstream societal impacts
reliability: noise, precision, stability
I made a playlist of 11 short videos (most are 7-13 mins long) on Ethics in Machine Learning
This is from my 2 hrs ethics lecture in Practical Deep Learning for Coders v4. I thought these short videos would be easier to watch, share, or skip around
What are Ethics & Why do they Matter? Machine Learning Edition
- 3 Case Studies to know about
- Is this really our responsibility?
- What is ethics? @scuethics
- What do we teach when we teach tech ethics? @cfiesler
Software systems have bugs, algorithms can have errors, data is often incorrect.
People impacted by automated systems need timely, meaningful ways to appeal decisions & find recourse, and we need to plan for this in advance
For example, if you start a “women & allies” email list and then fire a Black woman for being honest on it, it probably would have been better not to have the email list in the first place 3/
Some folks have asked about data vs. algorithms. Treating these as separate silos doesn't really make sense, and it contributes to a common perception that the data is someone else's problem, an unglamorous & lesser task:
Machine learning often fails to critique the origin, motivation, platform, or potential impact of the data we use, and this is a problem that we in ML need to address.