🧵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/
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
This reminds me of RoboDebt, when ~600,000 Australians were wrongly put into debt & many lives destroyed 4/
An example from the USA, where a software bug led to people with cerebral palsy losing access to needed medical & disability support, with no system to catch errors 6/
In India, many poor & elderly people are no longer able to access needed food benefits due to the digital identity program (if they can't travel to scanners, if their fingerprints are worn down, if the internet connection fails) 7/
Automation operates at scale. With RoboDebt, Australia started creating as many debts for welfare recipients EACH WEEK as they previously had in A YEAR 8/
These projects often begin with a starting point of artificial scarcity, taking as a premise that we will not provide people with enough food, benefit payments, healthcare, etc 9/
"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/
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/
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/
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
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
Classifications are not neutral. The way in which categories are defined and who defines them tell a story of power. 2/
Increasingly, populations are segmented & differentially treated. Surveillance sorts people into categories, assigning worth or risk, in ways that have real effects on their life chances. 3/
"Data do not speak for themselves. Data must be narrated—put to work in particular contexts, sunk into narratives that give them shape and meaning, and mobilized as part of broader processes of interpretation and meaning-making." @dourish@Imagenaciones
Two scalar moves in data science: 1. move datum➡️ data set, the claim that these data are sufficiently "alike" as to be able to be combined, compared, added, & divided 2. move large ➡️ small implicit in the drawing of conclusions or categories from data analysis 2/
The granularity of the data, both spatially and temporally, radically reconfigured the work that they had to do. The very fact of a digital trace produced the necessity of an account, leaving them with less time for their previous responsibilities to parolees and to the public 3/
As a mother, I can't wait until my 6 year old can be vaccinated against covid. My #1 reason is that I hope to reduce her chances of getting long covid or suffering any long-term effects. I want to share some of my personal thoughts here. 1/
First, in the USA the FDA has approved the vaccine for 5-11 year olds. This vaccine has been incredibly well studied and is safe. 2/ theguardian.com/world/2021/oct…
It will take years before we fully understand the long-term consequences of covid, but the studies so far (primarily in adults) of damage to a variety of organs, including the brain, vascular system, and immune system are deeply disturbing. 3/
- Millions of people are disabled with long covid (a horrific illness)
- This is going to be VERY expensive
- There are big financial incentives to downplay the severity & prevalence
- Concerted efforts similar to the climate denial playbook
It is critical for physicians, scientists, & public health officials to realize they are not dealing with an orthodox scientific debate, but a well-funded sophisticated denialist campaign based on ideological & corporate interests -- @GYamey@gorskon