Discover and read the best of Twitter Threads about #algorithmictransparency
Most recents (3)
#Enshittification is platforms devouring themselves: first they tempt users with goodies. Once users are locked in, goodies are withdrawn and dangled before businesses. Once business customers are stuck, all value is claimed for platform shareholders:
pluralistic.net/2023/01/21/pot…
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pluralistic.net/2023/01/21/pot…
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If you'd like an essay-formatted version of this thread to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
pluralistic.net/2023/04/12/alg…
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pluralistic.net/2023/04/12/alg…
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Enshittification isn't just another way of saying "fraud" or "price gouging" or "wage theft." Enshittification is intrinsically digital, because moving all those goodies around requires the flexibility that only comes with a *digital* businesses.
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Exciting update to our open source ranking algorithm that improves note quality, making it less likely that mediocre or unhelpful notes appear on Tweets. In the spirit of #AlgorithmicTransparency, here’s how and why it works…
Some notes appear on Tweets, then later disappear. As more ratings come in, they lose their Helpful status. This kind of self-correction is a good thing, but we consider these “false positives” — not necessarily bad notes, but ultimately not broadly helpful enough to show.
Analyzing data over the past few months, we’ve found that rating tags (for example, "sources not included") can be powerful signals to identify false positive notes early in the rating process.
Since the launch of the Algorithmic Transparency Standard in Nov, we (CDDO & @CDEIUK) have been piloting it with public sector orgs. We’re finally sharing the first algorithmic transparency reports & what we learned in the process bit.ly/3NMbGCS #algorithmictransparency
The first report we published is from the QCovid team @DHSCgovuk & @NHSDigital, who piloted the algorithmic transparency standard with a COVID-19 clinical tool used to predict how at risk individuals might be from coronavirus: gov.uk/government/pub…
The second report is from the GOV.UK Data Labs team, who piloted the standard for their Related Links tool, a recommendation engine built to aid navigation of https://t.co/6oTZERx0Up: gov.uk/government/pub…
