Counting the Countless | Will O'Pedia

tags: Datafication Algorithmic Discrimination

Notes

I replied, “Well, to be perfectly honest, I think data science is a profound threat to queer existences.”

NOTER_PAGE: (2 0.7646103896103896 . 0.0934873949579832)

saying that you think data science is a path to cultural genocide makes people doubletake.

NOTER_PAGE: (2 0.8814935064935066 . 0.7321428571428571)

lack of clear definition is important to trans existences and experiences, because trans lives are ultimately (to a certain degree) about autonomy: about the freedom to set one’s own path.

NOTER_PAGE: (3 0.4423701298701299 . 0.16596638655462184)

enables control and surveillance, because now, even aside from all the rigid gatekeeping, a load of people have a note somewhere in their official records that you’re trans.

NOTER_PAGE: (4 0.6582792207792209 . 0.26050420168067223)

So could we reform this? Spade would say no — the rigid maintenance of hierarchy and norms is really what the state is for.

NOTER_PAGE: (4 0.7564935064935066 . 0.1092436974789916)

We are attempting to negotiate with a system that is fundamentally out to constrain us.

NOTER_PAGE: (5 0.08603896103896104 . 0.11029411764705882)

data science can be seen as an extension of those administrative logics: It’s gussied-up statistics, after all — the “science of the state.”

NOTER_PAGE: (5 0.41964285714285715 . 0.19117647058823528)

What is data science, anyway? There are a lot of definitions, but the one I quite like is: The quantitative analysis of large amounts of data for the purpose of decision-making.

NOTER_PAGE: (5 0.6883116883116883 . 0.5609243697478992)

You are going to have to make judgment calls on where the similarities are and what is (and is not) equivalent.

NOTER_PAGE: (6 0.38474025974025977 . 0.5283613445378151)

Our data should be collected ubiquitously, it should be collected consistently and perpetually, and any variation that complicates the data collection should be eliminated.

NOTER_PAGE: (6 0.6810064935064936 . 0.21428571428571427)

we can remove that fallible, inconsistent “human factor” in how we make decisions, working more consistently and a million times faster. Which is, you know, fine, sort of. But by definition, a removal of humanity makes a system, well, inhumane!

NOTER_PAGE: (7 0.05844155844155845 . 0.29831932773109243)

more accurate definition of data science would be: The inhumane reduction of humanity down to what can be counted.

NOTER_PAGE: (7 0.19074675324675325 . 0.24474789915966386)

“data violence” refers to the perpetuation of violence through datalogical systems: everything from YouTube’s recommender algorithm to facial recognition to online advertising.

NOTER_PAGE: (7 0.3181818181818182 . 0.2867647058823529)

Ubiquitous data systems, routinely treated by business executives, governments, and war criminals as the future, can capture a lot more of your life than the DMV. This is not accidental; the entire appeal and point of data science is ubiquitous collection.

NOTER_PAGE: (7 0.5308441558441559 . 0.25315126050420167)

you out yourself and mark yourself forever.

NOTER_PAGE: (8 0.05925324675324676 . 0.2226890756302521)

administrative transition may be a boon to you, but it’s also a boon to the vast number of systems dedicated to tracking the course of your life, for their own not necessarily benevolent purposes.

NOTER_PAGE: (8 0.2767857142857143 . 0.11449579831932773)

The people caught in this trap are disproportionately likely to be already marginalized, already marked

NOTER_PAGE: (8 0.8969155844155845 . 0.11239495798319327)

system’s integration with things like apps for discounts and coupons renders people without smartphones invisible. Faced with such critiques, those running such programs are likely to respond, You’re right! We should make sure there’s a sensor in the bodega too!

NOTER_PAGE: (9 0.06087662337662338 . 0.11239495798319327)

it responds to critique only by expanding the degree to which it surveils us.

NOTER_PAGE: (9 0.24756493506493507 . 0.6901260504201681)

“reform” often benefits only the least marginalized while legitimizing the system and giving cover for it to continue its violence.

NOTER_PAGE: (9 0.5600649350649352 . 0.28256302521008403)

gender-recognition systems are fundamentally controlling and dangerous to trans people and simply cannot be reformed to not be violent against us. Normalizing, reductive views of gender are what these systems are for.

NOTER_PAGE: (10 0.06087662337662338 . 0.4989495798319328)

incorporating more people into facial-recognition systems isn’t really a good thing even for them. As Zoé Samudzi notes, facial-recognition systems are designed for control, primarily by law enforcement.

NOTER_PAGE: (10 0.2280844155844156 . 0.10399159663865545)

For me, my ethics of care says that we should be working for a radical data science: a data science that is not controlling, eliminationist, assimilatory. A data science premised on enabling autonomous control of data, on enabling plural ways of being. A data science that preserves context and does not punish those who do not participate in the system.

NOTER_PAGE: (11 0.36769480519480524 . 0.1092436974789916)

Notes

I replied, “Well, to be perfectly honest, I think data science is a profound threat to queer existences.”

saying that you think data science is a path to cultural genocide makes people doubletake.

lack of clear definition is important to trans existences and experiences, because trans lives are ultimately (to a certain degree) about autonomy: about the freedom to set one’s own path.

enables control and surveillance, because now, even aside from all the rigid gatekeeping, a load of people have a note somewhere in their official records that you’re trans.

So could we reform this? Spade would say no — the rigid maintenance of hierarchy and norms is really what the state is for.

We are attempting to negotiate with a system that is fundamentally out to constrain us.

data science can be seen as an extension of those administrative logics: It’s gussied-up statistics, after all — the “science of the state.”

What is data science, anyway? There are a lot of definitions, but the one I quite like is: The quantitative analysis of large amounts of data for the purpose of decision-making.

You are going to have to make judgment calls on where the similarities are and what is (and is not) equivalent.

Our data should be collected ubiquitously, it should be collected consistently and perpetually, and any variation that complicates the data collection should be eliminated.

we can remove that fallible, inconsistent “human factor” in how we make decisions, working more consistently and a million times faster. Which is, you know, fine, sort of. But by definition, a removal of humanity makes a system, well, inhumane!

more accurate definition of data science would be: The inhumane reduction of humanity down to what can be counted.

“data violence” refers to the perpetuation of violence through datalogical systems: everything from YouTube’s recommender algorithm to facial recognition to online advertising.

Ubiquitous data systems, routinely treated by business executives, governments, and war criminals as the future, can capture a lot more of your life than the DMV. This is not accidental; the entire appeal and point of data science is ubiquitous collection.

you out yourself and mark yourself forever.

administrative transition may be a boon to you, but it’s also a boon to the vast number of systems dedicated to tracking the course of your life, for their own not necessarily benevolent purposes.

The people caught in this trap are disproportionately likely to be already marginalized, already marked

system’s integration with things like apps for discounts and coupons renders people without smartphones invisible. Faced with such critiques, those running such programs are likely to respond, You’re right! We should make sure there’s a sensor in the bodega too!

it responds to critique only by expanding the degree to which it surveils us.

“reform” often benefits only the least marginalized while legitimizing the system and giving cover for it to continue its violence.

gender-recognition systems are fundamentally controlling and dangerous to trans people and simply cannot be reformed to not be violent against us. Normalizing, reductive views of gender are what these systems are for.

incorporating more people into facial-recognition systems isn’t really a good thing even for them. As Zoé Samudzi notes, facial-recognition systems are designed for control, primarily by law enforcement.

Notes that link to this note