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28 Apr, 12 tweets, 6 min read
Question: what are your favorite articles/papers/essays about the idea of external audits for algorithmic systems?
In "The Case for Digital Public Infrastructure", @EthanZ proposes building auditable & transparent search & discovery tools... for the emergence of a strategy that allows review & resists gaming

knightcolumbia.org/content/the-ca… One obvious, but difficult ...
Algorithmic audits will not produce accountability on their own; however if government creates meaningful regulatory oversight, algorithmic audits could become much more impactful

@AlexCEngler analysis on auditing employment algorithms for discrimination brookings.edu/research/audit…
Alex also wrote a case study on how "HireVue’s shady behavior encapsulates the challenges facing the emerging market for algorithmic audits" @AlexCEngler

fastcompany.com/90597594/ai-al…
The algorithmic auditing trap: @mona_sloane warned about the risk of enshrining eugenicist tech in weak regulation & superficial standards

onezero.medium.com/the-algorithmi… By gracing such technologie...
@themarkup and @propublica do fantastic independent, investigative journalism, auditing algorithms of major tech companies. Thank you @LeonYin!

This is an excellent mini-thread from @GretchenMarina with more recommendations on algorithmic audits:
Helpful collection of resources for investigating algorithms in society, including example investigations, method references, and guidance on FOIA, from @ndiakopoulos & @DanielTrielli
algorithmtips.org/resources/ Resources Investigating opa...
Mini-thread from @ndiakopoulos with more references:
An important point

Great mini-thread from @brianavecchione with more resources:
I agree, Gender Shades set a gold standard:

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More from @math_rachel

Rachel Thomas Profile picture
29 Apr
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing, from AIES 2020, by @rajiinio @timnitGebru @mmitchell_ai @jovialjoy Joonseok Lee @cephaloponderer

🧵with some quotes, but I recommend reading the full paper: arxiv.org/abs/2001.00964 1/ Saving Face: Investigating ...
External algorithmic audits only incentivize companies to address performance disparities on the tasks they were publicly audited for

Microsoft & Amazon addressed gender classification disparity after audit, but still had huge performance gap by skin color for age classification We found that this result h...
Audits have to be deliberate so as not to normalize tasks that are inherently harmful to certain communities.

Gender classification has harmful effects in both incorrect AND correct classification. Promotes stereotypes and excludes trans & non-binary individuals. 3/ While it is important to st...
Read 6 tweets
Rachel Thomas Profile picture
24 Mar
To rush CS students through simplified, condensed overviews of ethical understanding & position them to be the primary arbiter of change promotes engineers' inclination towards seeing themselves as solitary savior, to the detriment of the quality of the solution. 1/ Incidents of algorithmic mi...
Incidents of algorithmic misuse, unethical deployments, or harmful bias cannot be addressed by developing moral integrity at an individual level. The current issues are the result of collective failure. 2/

@rajiinio @morganklauss @amironesei Incidents of algorithmic mi...
Less about a single engineer’s efforts to enforce their understanding of diverse representation into the model, and more about a form of participatory design where other stakeholders are actively & humbly welcomed to join in creation of more just & equitable systems 3/ s. A step towards more incl...
Read 6 tweets
Rachel Thomas Profile picture
24 Feb
Big news: @jeremyphoward & I have moved to his home country of Australia (he is not a USA citizen & has been wanting to return for years). I’m excited about the move, although it is bittersweet 1/
The last year in the USA has been horrifying. I’m lucky in so many ways: we were able to isolate pretty strictly as a family of 3 (a privilege that many did not have) & our daughter thrived at home with us, although it was still hard to go for a year with... 2/
no in-person childcare; not seeing any of my friends; hearing regularly that mass preventable death is okay as long as it is mostly people with chronic illness (like me), the elderly, & BIPOC dying; and worry of not being able to access an ER or ICU 3/

Read 6 tweets
Rachel Thomas Profile picture
20 Feb
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 Slide reading "calculating the souls of Black folk: pre
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.
Read 4 tweets
Rachel Thomas Profile picture
19 Feb
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 slide reading: "what's the problem? are you creating mo
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"

onezero.medium.com/21-states-are-…
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 slide: The Must Be's: Central Considerations. Who is the pro
Read 8 tweets
Rachel Thomas Profile picture
4 Feb
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/

Read 21 tweets

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