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26 Nov, 4 tweets, 2 min read
Compared to ethics principles in medicine, AI ethics principles lack:
1. common aims & fiduciary duties
2. professional history & norms
3. proven methods to translate principles into practice
4. robust legal & professional accountability mechanisms

nature.com/articles/s4225…
"The truly difficult part of ethics—actually translating theories, concepts & values into good practices AI practitioners can adopt—is kicked down the road like the proverbial can." @b_mittelstadt 2/
"Ethics has a cost. AI is often developed behind closed doors without public representation... It cannot be assumed that value-conscious frameworks will be meaningfully implemented in commercial processes that value efficiency, speed and profit." 3/
"AI inevitably becomes entangled in the ethical & political dimensions of vocations & practices in which it is embedded. It is foolish to assume that very old & complex normative questions can be solved with technical fixes or good design alone." 4/
nature.com/articles/s4225…

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More from @math_rachel

Rachel Thomas Profile picture
25 Nov
🧵on Google, Tech Regulation, & Risk of Industry Capture in 🇦🇺

"Google’s investment in Australia’s tech future — a boon for universities or a Faustian bargain?" by @jordanguiao 1/

abc.net.au/religion/googl…
Australia's competition regulator found:
- Google engages in anti-competitive behavior in digital advertising, which harms consumers & businesses
accc.gov.au/media-release/…

- monopoly on search has "stifled innovation and reduced consumer choice"
ia.acs.org.au/article/2021/a… 2/
Australian federal court found that Google misled Australian mobile and tablet users on how it collects location data 3/

abc.net.au/news/2021-04-1…
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Rachel Thomas Profile picture
22 Nov
Many people have a false dichotomy that you are either FOR or AGAINST covid restrictions, with no nuance about the TYPE of restrictions or level of effectiveness, much less that eschewing all restrictions → hospitals collapse & lockdown more likely. 1/
There has been a lot of terrible public health messaging & contradictory government policies in the West, from the start of the pandemic, continuing now, and these erode public trust, create false expectations, & contribute to “pandemic fatigue” 2/
The “only elderly & chronically ill are at risk” was both false AND ineffective messaging. This has been clear from the VERY START of the pandemic. (I RTed @jenbrea at the time) 3/
Read 16 tweets
Rachel Thomas Profile picture
16 Nov
The false hope of current approaches to explainable AI in health care: current explainability approaches can produce broad descriptions of how an AI system works in general, but for individual decisions, the explanations are unreliable or superficial 1/
thelancet.com/journals/landi… The false hope of current approaches to explainable artifici
Explainability methods of complex AI systems can provide some insight into the decision making process on a global level. However, on an individual level, the explanations we can produce are often confusing or even misleading. @MarzyehGhassemi @DrLaurenOR @AndrewLBeam 2/ What are explanations for? These limitations do not render e
Increased transparency can hamper users’ ability to detect sizable model errors and correct for them, "seemingly due to information overload." 3/ The intuitive simplicity of inherently explainable models is
Read 6 tweets
Rachel Thomas Profile picture
13 Nov
"Who benefits from data sharing in Africa? What barriers exist in the data sharing ecosystem, and for whom? If much of the data sharing practice is shaped by the Global North, how can we ensure that the narrative for Africa is controlled by Africans?" 1/

arxiv.org/abs/2103.01168 Narratives and Counternarratives on Data Sharing in Africa R
Stakeholders in the African data sharing ecosystem. Those at the top of the iceberg hold significant power & leverage in guiding data sharing practices & policy compared to those in the hidden part of the iceberg. More powerful stakeholders wield disproportionate power. 2/ picture of iceberg. From top: (above water) government bodie
Dominant narratives around data sharing in Africa often focus on lack, insufficiency, deficit.

This framing minimizes the strength, agency, and scientific & cultural contributions of communities within the continent, and overlooks community norms, values, & traditions. 3/ a lack of knowledge about the value of data and training, as
Read 7 tweets
Rachel Thomas Profile picture
13 Nov
🧵automation of gov social services (eg food benefits, disability services, unemployment, etc) can be:
- implemented with no way to correct errors (software treated as error-free)
- smokescreen for policy changes
- justify austerity under guise of efficiency
- operate at scale 1/
In France, updates to an automated system for benefit payments caused errors, delays, & incorrect debts for at least 60,000 people

Case workers are unable to correct errors in the system. Some victims coped by *cutting back on food* 2/

hrw.org/news/2021/11/1… @hrw In France, the Caisse des Allocations Familiale (CAF), the g
Flawed algorithm in UK ignores how often ppl get paid and has led to people going hungry & falling into debt

This is not just a technical error; the government deliberately chose this method of calculation because it was easier to automate, increased efficiency, & reduced costs In the United Kingdom, the government is using a flawed algo
Read 12 tweets
Rachel Thomas Profile picture
12 Nov
At the @QUTDataScience Data Science for Social Good showcase, @oforbes22 sharing about ways to visualize spatial uncertainty for the Cancer Atlas map, using glyphs or hues & whiteness in a project with @CCQld Image
This has been the inaugural year for @QUTDataScience Data Science for Social Good, with grad students & recent grads partnering with 2 non-profits: @CCQld Cancer Atlas & @fareshare_aus Qld food charity

@KerrieMengersen & @Farzana18_jahan kicking off our showcase ImageImage
Mapping food insecurity in Queensland with an interactive map @fareshare_aus (largest meal charity in 🇦🇺) & @QUTDataScience Image
Read 5 tweets

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