We hear a lot about Facebook as a platform for manipulation - using machine learning to bypass our critical faculties and trick us into believing things that are bad for us - but the real show is in Facebook's ability to target, not manipulate.

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People who hold disfavored views struggle to find one another and mobilize. To find other people that feel the same way as you and make common cause with them to effect political change, you have to reveal your views and suffer social sanction.

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Search allows people who hold these views to find one another. If you have a deep feeling about your gender being nonbinary but don't know the words for it, you can search for communities of people who have those words, join them, and discover who you've been all along.

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This is why, in this moment, so many ideas are migrating from the fringe to the center: ideas about racial justice, gender identity, alternatives to market systems, etc. People have harbored these views all along, but have held back on expressing them.

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Being able to express yourself in private, among people who share your views, is a prelude to going public and putting your case to the wider world in hopes of effecting change.

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This goes for ALL disfavored views: not just ones we laud, but also the ones we deplore. Many Americans have nursed secret white supremacism but only whispered about it, because saying it aloud would attract social sanction, with real consequences.

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Search let these people find each other. Having formed groups, they were able to brave social consequences and begin to shout about it. When they did, they converted people who were sort-of racist all along to their cause. "Radicalization" is closely related to "convincing."

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But search isn't the only way that groups with hard-to-find traits can be discovered: there's also targeting. Ad-tech companies spy on us, ascribe traits to us, then sell the right to target those traits to advertisers.

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"Show my ad to midwestern high school cheerleaders"

"To people shopping for a new fridge"

"To the owners of senior dogs"

"To people with diabetes"

"To readers of cyberpunk science fiction novels"

"To people skeptical of Big Tech"

"To Bernie Sanders supporters"

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"To violent, Trump-addled conspiracists plotting insurrection"

https://t.co/Q8lsg2yCXg

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To be fair, the Facebook ads "for body armor, gun holsters, and other military equipment next to content promoting election misinformation and news about the attempted coup at the US Capitol" were probably not necessarily targeted at "coup plotting" per se.

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More from Cory Doctorow #BLM

There are lots of problems with ad-tech:

* being spied on all the time means that the people of the 21st century are less able to be their authentic selves;

* any data that is collected and retained will eventually breach, creating untold harms;

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* data-collection enables for discriminatory business practices ("digital redlining");

* the huge, tangled hairball of adtech companies siphons lots (maybe even most) of the money that should go creators and media orgs; and

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* anti-adblock demands browsers and devices that thwart their owners' wishes, a capability that can be exploited for even more nefarious purposes;

That's all terrible, but it's also IRONIC, since it appears that, in addition to everything else, ad-tech is a fraud, a bezzle.

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Bezzle was John Kenneth Galbraith's term for "the magic interval when a confidence trickster knows he has the money he has appropriated but the victim does not yet understand that he has lost it." That is, a rotten log that has yet to be turned over.

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Bezzles unwind slowly, then all at once. We've had some important peeks under ad-tech's rotten log, and they're increasing in both intensity and velocity. If you follow @Chronotope, you've had a front-row seat to the

More from Machine learning

Really enjoyed digging into recent innovations in the football analytics industry.

>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇‍♂️ https://t.co/9YOSrl8TdN


For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.

This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method
https://t.co/Hx8XTUMpJ5


Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.

Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN


So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?

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