My inadvisable camera setup

Fri, 12 Jun 2026 00:00:00 +0000

People have asked what I use for video, and I’ve always been reluctant to list everything I’ve got. A lot of it is redundant, involves lots of compromises that normal people wouldn’t want to make, or is wildly over-budget for normal people looking to get a good video for zoom or even video recordings. But I also realize sometimes people are just curious. So in the interest of satisfying that curiosity, I’ll list what I happen to have & use, and then I’ll work through a list of things I think I would probably recommend someone to buy if they wanted to have a decent video setup that requires as little adjustment as possible and gives you a lot of flexibility between video chats, recordings, or whatever else.

Here’s what I use:

bmpcc 6k gen1
sigma 35mm f/1.4 ART
some cheap adjustable ND filter
cheap tripod legs + iFootage fluid head
Elgato 4k USB capture device
some cheap HDMI cable
Shure Beta 58A Microphone
Zoom F3 (when I’m just doing an audio call)
XLR cables
Nanlite FS-150/300B monolights
some C-stands and clamps

Have I mentioned that you should not get that? I’m not even linking to them - that’s how much I want you not to buy this stuff.

I don’t use the full extent of the features of most of these things, and if I needed to rebuild my entire setup with $2000-3000 budget, I would skip most of this stuff. I have some of this gear because of a past life in photography that I just decided to reuse (like the sigma lens). Other stuff I bought because I was drawn in by influencers; other stuff was probably the best thing I could get at the time, but you could get something much better for much cheaper today.

So with that… here’s what I think I would get, if I was determined to have a dedicated light, video, and audio setup without wanting to improve my setup for at least several years (if ever).

video stuff

camera body: sony zv-e10 ii (on B&H) ($950)
lens: any one of the following:
- sigma 17-40 f/1.8 (on B&H) ($900)
- sigma 16mm f/1.4 (on B&H) ($500)
- sigma 30mm f/1.4 (on B&H) ($300)

The 17-40mm f/1.8 is very versatile, but if you want to set it up in one place and not adjust things, the versatility might be $500 spent on versatility you don’t care about. I still think it’s a good option, especially if you don’t have a strong intuition about how far you want the camera, how tight a frame you want on your face, etc… but the lens is going to eclipse the cost of almost everything else, so if you think you’re pretty satisfied with a 30mm or even 16mm lens, then you can save about $300-400.

Optical quality on a prime is also generally better (for the same price) than on a zoom lens, so while you could find a zoom lens around 17-40mm for $300-$500 (similar to the prime lenses), odds are the optical quality won’t be as good as either prime. But you might not care, especially if you’re on video chat; on zoom or jitsi or google meet, nobody will be able to tell the difference between a $300 zoom and a $900 zoom.

If you think you just want to replicate the view I have, I would say go with the 30mm. If you don’t have a ton of room, you might find 16mm better for your setting.

optional: adjustable ND filter:
- k&f 82mm filter (on B&H) ($32)
- step up rings (on B&H) ($9)

I’m listing an adjustable ND filter because the goal with lighting (in the next section) might involve trying to overpower ambient light that fluctuates or doesn’t provide the look you want. That often means a lot of light, so you’ll sort of have to balance it back out by knocking down the exposure with an ND filter. This isn’t an amazing ND filter, but I’ve been happy with it, and I think it’ll do the job. I’m also suggesting an oversized filter and step-up rings because it’s just cheaper to buy one giant filter and then stepping up to that size, rather than getting multiple filter sizes that may not be compatible with whatever lens you’re using down the road.

lighting

light: either:
- 1x amaran halo 60x (on B&H) ($120)
- 1x amaran halo 60x (on B&H) ($120) + 1x amaran halo 100x (B&H) ($170) ($290 total)
1x bowens reflector (on B&H) ($10)
light stands:
- if you have space: neewer light stands (on B&H) ($35)
- if you don’t have space: some kind of desk clamp thing like this (on B&H) ($33)

Lighting can be annoying. I think you’ll find it easiest to use a dark room and then add lights to it. I would suggest some kind of modified “3-point lighting” setup, but instead of a fill light, use something to reflect the key light back to your face. The image below doesn’t show it, but imagine the camera between the key light and the fill light pointing at the object.

(credit to wikimedia)

I’ve always found the back light to be kind of marginal to the final image. It’s nice if you can illuminate the background if you have books or something back there, but if you don’t want to spend $120 on that, don’t worry about it. If you have christmas lights, a lamp, or anything else that you can turn on in the background, that would help add some depth and make the video look nice.

It’s relatively important to use high-quality lights for the key light and the fill light; if the color rendering index (CRI) of those lights is very poor, your skin might look really off in a way that’s hard or impossible to fix. Those are where low-quality light will hurt the image (or conversely, where high-quality lights really reward you).

In any case, I would consider pointing the key light (whichever one that is) either at the ceiling or at the wall that you’re facing. That’ll reflect the light back in a broad, diffuse pattern, without you having to spend $100+ on a softbox (or find space for a softbox, which can be enormous). This is what I do with my key light (point it at a white wall and let that illuminate my face).

Unfortunately, this is really something you’ll just have to experiment with and come up with something that looks creatively nice and acceptable to you. On the plus side, you get to experiment.

I will offer that video compression “crushes” shadows (and so does YouTube, for that matter) so that you can’t see as much detail in there, so don’t get too fancy with dramatic lighting. Flat, even lighting is better than really moody lighting with highlights and deep shadows that get messed up by video compression.

audio stuff

microphone: either:
- if you can have the microphone in the video frame: shure sm58 (on B&H) ($100)
- if you cannot tolerate the microphone being visible in the video frame: se electronics se7 (not on B&H as far as I can tell) ($120)
field recorder: zoom h1 xlr (on B&H) ($150)

I have my microphone on a little arm that I can adjust and point just under my mouth toward my chest. There’s a bit of technique to make sure you’re not breathing into the microphone or making “mouth sounds” that can be quite upsetting to people listening on their headphones, but either the SM58 or the SE7 will be reliable tools to work on mic technique.

In an ideal world, you would try out a handful of microphones and listen to yourself on headphones to see what pairs with your voice most naturally. In reality, we don’t all have access to a plethora of microphones, and we just kind of have to hope or trust that someone on YouTube has a similar voice as us, and their reviews are representative of how our voice will sound.

The field recorder may be excessive and it is possible to connect your microphone to your camera for less than $150, but a field recorder is legitimately useful for other reasons that you may have need for later (audio-only productions, recording interviews with people offline, etc…). This is a small premium that potentially solves a handful of other problems later on.

audio treatment

Doing something to “knock down” reverberation in your room will help a lot, but I don’t think there’s anything online that I would strongly recommend. Everything is either very expensive or very bulky and impractical to ship (often both). If you have blankets or other soft, dense material that you can put up around you when you’re recording, that’ll help absorb your voice before it bounces off the hard walls and returns to the microphone. You’d be surprised how much everyday things like couches, beds, bookcases, etc… can help, and how bad a room can sound when you’re recording in a bare room.

I would not recommend buying those 12” square foam panels that you see in packs of 50 on Amazon. I’ve bought them, they were utterly useless for what I needed, and probably objectively useless for anyone’s needs. They’re a miniaturized simulacrum of real sound panels that cost $400+ and require mounting hardware and stuff because they’re just too substantial to apply to a wall with double sided sticky tape. The only “hack” is to buy a certain brand of insulation from a hardware store, wrap it in fabric, and mount that to a wall; the cost for that is closer to $100-200, not the $30-50 of those amazon products.

some other audio things

These are the less exciting peripherals and cables that you almost definitely need if you’re getting the above stuff:

microphone clamp: elgato microphone arm (on B&H) ($100)
usb cable: almost any usb cable will do fine (not sure what I would recommend on B&H) ($16)
xlr cable: 2x 10ft any cable with neutrik connectors (on B&H) ($22)
3.5mm cable: any (on B&H) ($2)

misc/peripherals

necessary

usb-c cable: almost any will do for the field recorder, but you need a 5gbps cable for the camera ($16)
tripod: neewer video tripod with fluid head (similar option on B&H) ($100)
dummy battery: neewer dummy battery (on B&H) ($35)
gray card: any gray card (on B&H) ($15)
fan for camera: smallrig fan (on B&H) ($40)

optional

hdmi adapter: any micro to full-size hdmi adapter (on B&H) ($10)
hdmi cable: monoprice hdmi cable (on B&H) ($20)
hdmi capture: elgato 4k capture device (on B&H) ($100)
sd card: lexar 64gb (on B&H) ($100)

Subtotal is, I think, $2,523.23 (or thereabout) with the 16mm lens. The 30mm lens will be a little less, and the 17-40 zoom lens will be about $400-500 more.

new vs used gear

Generally, video gear is safe to buy used, because it’s built to be used in very rough conditions. I would especially recommend finding the camera, lens, and microphone on the used market if you can. Stands and other grip gear is also fine to buy used, but people don’t sell clamps and stands all that often; you kinda buy it once and you’re set for life.

Lights are complicated; LEDs deteriorate with heat over time, so if they spent 5 years turned on for like 8 hours a day they’ll be in worse condition than new LEDs (which might look like a nasty color shift). All of which is to say I would buy lights new or very lightly used.

[BH se electronics se7]:

[BH usb cable]:

Weeds tend not to grow where they can't take root

Thu, 21 May 2026 00:00:00 +0000

I’ve been participating in organizing around stopping an AI project in my community (namely a data center collaboration between the University of Michigan and Los Alamos National Labs), and I’ve been thinking a lot about how merely stopping these initiatives seems insufficient. I’ve been thinking about what would be sufficient, and I’ve come to appreciate, through organizing, that as much as “destroying AI” means uprooting AI initiatives, it means building counter-structures that render AI interventions totally incomprehensible to us.

I think I wrote in my techno-optimism piece that AI seems to thrive opportunistically, not totally unlike a weed in a garden. Maybe I’ve been thinking about this because I’ve been doing some gardening lately, but stay with me; weeds take root where the environment has resources to extract, and where other things are not yet growing. Where too many other things are thriving, weeds tend to have much more trouble taking root. You can spend all day uprooting weeds, but frustratingly the best thing you can do is cultivate a healthy environment and support the life that you want to thrive; healthy plants will deny weeds access passively, mostly on their own.

We can organize to stop data center proposals; we can demand our cities cancel surveillance contracts with Flock; we can shout down the commencement speakers who try to browbeat us into submission under the idea that “AI is inevitable”. I do some of those things when I can, and we must do all of those things across the board. But that doesn’t make our community any less hospitable an environment for “AI solutionism” to take root and thrive as soon as the winds change and we falter. You could blow up Flock HQ and in 5 years there’ll still be surveillance companies offering their services to police, because there is a hospitable environment for such a business and we give them space to take root, like weeds.

After reading Health Communism by Artie Vierkant and Beatrice Adler-Bolton, I spent a lot of time thinking about the premise they laid out for the reader - a society that mobilized the bulk of its resources to care for the sick, that didn’t make that care conditional, that moved mountains for the vulnerable rather than leveraging people’s vulnerability to stoke desperation and anxiety. What would a society look like that denies the use of threats of homelessness and destitution to coerce people? What would our lives be like if we structured our society to mobilize care for all of us?

I believe that we will not be able to stop Flock just by canceling contracts across the country. I believe that we will not be able to destroy AI just by stopping data center proposals. I think it’s important to make the costs of pursuing AI exceptionally high, but it’s also important to render the potential benefits of AI enterprises as close to nil as possible. There has to be no room for these weeds to take root and proliferate, or we’ll constantly be chasing data center proposals, browbeating city counselors for signing surveillance contracts, etc… and as soon as we’re exhausted, they’ll overrun us.

Destroying AI must include building counter-structures and nurturing a healthy, thriving social landscape that denies AI projects access to us in the first place. AI solutions like therapy & medical chatbots find space to thrive because of all the gaps in medical care we’ve normalized; we must make these interventions totally inscrutable in a future where care is always available, and people’s needs are not constantly being means-tested and scrutinized. Surveillance interventions and police collaborations like Flock thrive because we subscribe to this police state in which we live, where surveillance and violence are natural extensions of the logic motivating state violence; we must build a future where the impulse for the state to commit violence or to intrude on our privacy registers as bewildering and alienating.

Uprooting these AI projects is what we do to address a past mistake in leaving space for these things to grow in the first place. We must both uproot these projects and we must work to build a future where these kinds of proposals simply make no sense to us, because we have reoriented everything we have to take care of one another earnestly and patiently.

I don’t have a blueprint for this; it’s just a thought that has been lingering at the periphery for a while that I’ve wanted to get out of my head. I worry that the University of Michigan is not motivated by exceptional forces in their exceptional project to build this billion-dollar military facility with the lab that built the nuclear bomb. I don’t believe healthcare, education, and a myriad of other industries are reacting to exceptional influences when they embrace AI; I think they’re being overrun by conditions that are conducive to the invasive weediness of AI, and I think in every setting we need to think about what counter-structures we need to nurture and sustain to make these interventions make no more sense than coupons for things that are already free, or a way to fast-forward through leisure time. I don’t know if we can destroy AI just by chasing around the sprouting invasive plants; I think we need to grow and foster other things that make weeds struggle.

On techno-optimism

Mon, 04 May 2026 00:00:00 +0000

I’ve got a longer draft in the works, but I don’t think that’s going to become a blog post, so I figure it might be useful to publish something here and solicit feedback, or just plant a flag in the ground that this was a thing I did. ~~Last week~~ Several weeks ago I gave a talk at Wayne State University titled “the uncanny pessimism of techno-optimism”. In this post I’m going to breeze through some of the stuff I talked about and give a sense of what’ll be coming. Eventually.

The gist is that techno-optimism, especially what we see in this era of AI’s ascent, is neither particularly “techno” nor “optimistic”, but something else entirely. In the talk I pieced apart some issues with talking about “AI” as a purely technological project (stuff I’ve written about elsewhere), and then I talked about how techno-optimism, taken on its own terms, seems to foreclose on our self-determination - that is, our ability to shape our own futures and make decisions about what our future looks like and is constituted of.

We might argue about whether “techno-something” makes sense, and I don’t particularly want to die on that hill, but the something is definitely not “optimism”; I think it’s at least arguably the case that what we’re looking at is techno-fatalism.

This sense of fatalism is both ubiquitous and conspicuous, making it kind of overstimulating to exist in the world today and to be aware of discourses about AI and billionaires’ and tech companies’ plans for our collective future. But I found some clarity and relief in comments Dr. Tressie McMillan Cottom made at the Urban Consulate back in November, when she posited that her audacious vision of the future is one with refusal.

So in my talk I asked my audience at Wayne State University to imagine (referencing Ruha Benjamin’s work) what it would require to refuse something, and more interestingly what kinds of futures refusal unlocks. You could think of this akin to what goes into that project, and what comes out of it. I touched on a bunch of examples of groups pushing back against this AI project in a bunch of different ways - with lawsuits, with protests, with moratoriums on data center infrastructure, with sabotage, etc…

I also got to talk a little about the Stop the Data Center group that I’m connected to via the AI Skeptics Reading Group, and both some of the conspicuously fucked up parts of the UM-LANL data center (as if it’s not enough that the University of Michigan is angling to build a billion-dollar data center with the folks that made the atomic bomb).

I ended my talk by observing that pushing back against this data center proposal doesn’t defeat this overarching project involving fatalism and proto-fascistic qualities (as Dan McQuillan has written about in Resisting AI, and other people have observed over the years since that book came onto the scene). We’re not looking to destroy a Flock camera and go home - we’re not even looking to destroy Flock the company and go home. Even if we burn Flock headquarters to the ground, the value proposition of selling surveillance services to cops means we’ll never be done with entrepreneurs looking to do essentially what Flock has done. Maybe they’ll crowdsource the capital investment the way Amazon has done with Ring; maybe they’ll deploy their technology on roving surveillance systems like Waymo has done with cars that can be queried en masse based on where they were at any given moment.

We need to change the conditions that make it profitable to do this stuff. If we stop this UM-LANL military facility, we’ll still have AI companies looking to pick us off by offering palliative solutions to make up for austerity cuts, defunded resources, and shuttered programs.

It seems like we see AI flourishing in certain industries where resources are being hollowed out and where people’s needs and refusal can be discarded - see Palestine, of course; but you can also see it every day in schools, in healthcare, in prisons & policing. It feels as if AI solutions are opportunistic infections or predatory creatures, in that they thrive where human rights and civil society have been weakened.

All this was to say, if we’re going to “destroy AI” in a sense that matters, we need to build counter-institutions that sustain us and make us stronger; we need to support one another, meaningfully, so that exploitative palliative non-solutions like chatbot counselors are incomprehensible to us because they don’t prey on our desperation and our vulnerability, because we have made ourselves collectively less desperate, less vulnerable to these predators.

Scolding people for using chatbots is easy; it’s less easy to put myself into the shoes of someone who has so utterly given up on getting help from anyone that they turn to a chatbot for that help, and put myself through the steps of what it’s going to take to restore their trust in asking for help from people, to say nothing of the work we have to do for that help to actually be available to be mobilized.

That sounds awfully hard, and maybe it is. At the end in my notes I admit that yes this is hard as hell, but if we’re going to work for anything, let’s make it be a future defined in terms centered around people, around our liberation. A future of a free Palestine; a future where trans people are not persecuted by transphobic, mold-huffing billionaires; a future where the sick get real care. I’d rather struggle for our liberation and die than live a lifetime of abject surrender.

Anyway, still a lot of revising and annotating that needs to happen before I can share the talk notes, so I figured it’d be worth sharing these condensed notes somewhere.

Reading Group!

Wed, 18 Dec 2024 00:00:00 +0000

Hi all, it sounds like there’s enthusiasm for a reading group. That’s exciting and scary!

I’m going to make a few unilateral decisions:

We’re going to start with Ruha Benjamin’s Imagination: A Manifesto.
I’ll draw up a reading calendar, so if you were pulling for another book, stay tuned.

Now some overwrought thoughts. I want to try to give as complete an experience as I can to everyone, in whatever format they’re able to participate, without anyone feeling like they’re missing out on anything.

On Mondays, I’ll post a couple of quotations, or provocations, or something to get a few conversations going. I’ll draw from the reading for that week.

You can quote/reply/engage however you want on bluesky, mastodon, or twitter. you can also write your own blog posts or tweets. If you @ me, I’ll keep an eye on, and engage with, conversations all week.

On Fridays, I’ll send summaries of some of the thoughts across the web to a Buttondown mailing list. These will include some of my own reflections directly, but mostly a quick rundown of some of the conversations I saw.

On Saturdays, I’ll host a jitsi or zoom call or something. if you want to unpack some thoughts, chat with people about something, or just express something that you didn’t want to say during the week, you can do that in the call. In some ways it’ll resemble a typical reading group, but the ground will be seeded with conversations from the previous 5 days.

You don’t have to participate in the social network stuff; nor do you have to really participate in the zoom stuff, to be honest. You can do one, or both, or neither; the best way to be involved will be by signing up for the mailing list, so you can keep apprised of what conversations people are having from week to week.

If you’re interested in joining, please be sure to buy Imagination: A Manifesto as soon as you can. Then subscribe to this buttondown mailing list.

I’ll send an email in a few weeks giving people a schedule of upcoming calls, a firm calendar of what to read (and when), and some other organizing details. Hope to see you soon.

edit notes: fixed a link that went to a 404 error.

Reading Group?

Sun, 08 Dec 2024 00:00:00 +0000

I’ve been hinting for a little while on social media like Mona Lisa Vito hinting about her biological clock in My Cousin Vinny

but I would be very open to participating in a reading group for certain works. I would be so amenable, in fact, that I might be open to starting one if there’s interest.

If anyone would like to read any of the following books, please let me know.

Ideally, @ me with whatever books you would be especially interested in reading. If you have a book in mind that you think is a really close fit within the constellation of the books I suggested, feel free to write in those options. If you’re looking for some options, I’m drawing mostly from the list of books on Workshops 4 Gaza, so consider browsing that catalog if you’re looking.

Additionally, if you have thoughts about what format you’d like to participate in, that might be thought-provoking. Be creative.

Let’s say, for the sake of organizing, that we would start in early January.

Defining AI

Fri, 06 Dec 2024 00:00:00 +0000

The main issue I have with a lot of work that tries to define AI is that the criteria they use to draw boundaries often turn out to be functionally useless for my needs; these definitions lead us to weird places, letting scholars fixate on strange, unworkable frameworks. Those pedantic fixations don’t really benefit the organizers, activists, regular people who are getting crushed by the systems they’re trying to work against. So I’m going to try to unpack how I think about AI; how I trace the boundaries of the term in a way that’s as useful as possible for me and my needs; and how I would encourage you to scope or define ideas that are important to your work.

Let’s look at how AI Snake Oil, by Naranayan and Kapoor, sets out to define AI. The authors pose 3 questions to loosely infer whether something is AI:

Does the task require creative effort or training for a human to perform? [page 12]

Was the behavior of the system directly specified in code by the developer, or did it indirectly emerge, say by learning from examples or searching through a database? [page 13]

… whether the system makes decisions more or less autonomously and possesses some degree of flexibility and adaptability to the environment. [page 13]

I found these questions a little nebulous and indeterminate. I think you could spend some time litigating what constitutes creative effort, what reflects an “indirectly emergent behavior”, what satisfies “some degree of flexibility”, and so on.

Fortunately, they offer some examples. Examples can be a great way to disambiguate something like a definition, or a framework for thinking about a problem. A salient example can help affirm or problematize the motivating idea - if a framework should have something to say about an issue, I like to try to work through it and see what it offers. It’s a good sign if the framework suggests something that makes sense - even better if that framework brings something into focus that wasn’t obvious otherwise. A good definition can help sensitize us to critical details that we might otherwise overlook.

Okay, I’ll digress from talking about the virtues of good writing and framework-building like this. Let’s look at an example they provide that offers contrasts between a clear case of AI and a clear case of not-AI:

… an insurance pricing formula, for example, might be considered AI if it was developed by having the computer analyze past claims data, but not if it was a direct result of an expert’s knowledge, even if the actual rule was identical in both cases. [page 13]

I was pretty stumped by this example. My instinct was to ask in what sense this distinction matters, and especially why it’s being held so prominently. If anything, it struck me as an illustrative example of the abject failure of an approach to defining AI that’s contingent on the inner workings of the system - because anybody who has experienced health insurance claims systems can tell you that you don’t get a lot of insight into how the system works. You certainly don’t get to know whether you’re in the former system or the latter without a lot of litigation and paperwork.

This distinction grated at me for some time, because as far as I was concerned it didn’t really matter if you were dealing with two identical systems - identical decisions, identical opacity, identical absence of accountability - whether one is AI and the other not. Only they, the computer scientists argue, can affirmatively identify one system as AI while another, identical system, is not.

It frustrated me for a while; the implication that computer scientists and other technical experts should effectively chaperone the collective efforts of activists and organizers against these systems is deeply offensive to me. But at least it brought into focus another critique they offered later:

Civil rights advocates have often lumped together facial recognition [technology (FRT)] with other error-prone technologies used in the criminal justice system, like those that predict the risk of crime—despite the fact that the two technologies have nothing in common and the fact that error rates differ by many orders of magnitude. [page 16]

Again, I found this interpretation of a coalition of civil rights advocates quite strange at first. Perhaps this is the difference between an anthropologist-trained computer scientist and a pair of computer scientists trained through-and-through as such, but my first instinct was to think about what civil rights advocates understand about the ecology of policing technology that gives them the perspective to recognize this constellation of technologies as neighbors of one another. What do they know that I, as a relative outsider to their collective effort, obviously don’t?

The Stop LAPD Spying Coalition produced a whole documentary explaining their rationale, where they talk about some of the work they did to figure out what connecting through-lines bind them together. I don’t think it takes a particularly sharp mind to understand that the coalitions against FRT, against predictive policing, against prisons, etc… are working against a constellation of politically closely-related politics that happen to be implemented on a range of disparate technologies.

One might inspect handguns and stun guns, come to the conclusion that these are two totally different technologies because they don’t operate on the same technical principles at all, and embarrassingly overlook that they’re obviously both tools police use to maim and kill people. You don’t need to deconstruct a handgun and a stun gun to understand their adjacent, often overlapping, roles in policing. Fixating on the firing mechanisms can apparently desensitize someone to the political neighborhood these technologies share, and make a person a very unreliable ally in an ongoing collective struggle to abolishing policing.

I think we should shed the idea that AI is a technological artifact with political features and recognize it as a political artifact through and through. AI is an ideological project to shift authority and autonomy away from individuals, towards centralized structures of power. Projects that claim to “democratize” AI routinely conflate “democratization” with “commodification”. Even open-source AI projects often borrow from libertarian ideologies to help manufacture little fiefdoms.

This way of thinking about AI (as a political project that happens to be implemented technologically in myriad ways that are inconsequential to identifying the overarching project as “AI”) brings the discipline - reaching at least as far back as the 1950s and 60s, drenched in blood from military funding - into focus as part of the same continuous tradition.

Defining AI along political and ideological language allows us to think about things we experience and recognize productively as AI, without needing the self-serving supervision of computer scientists to allow or direct our collective work. We can recognize, based on our own knowledge and experience as people who deal with these systems, what’s part of this overarching project of disempowerment by the way that it renders autonomy farther away from us, by the way that it alienates our authority on the subjects of our own expertise.

This framework sensitizes us to “small” systems that cause tremendous harm because of the settings in which they’re placed and the authority people place upon them; and it inoculates us against fixations on things like regulating systems just because they happened to use 10^26 floating point calculations in training - an arbitrary threshold, denoting nothing in particular, beneath which actors could (and do) cause monumental harms already, today.

Okay, that was a bit of a post. Whether you subscribe to this way of defining AI or you totally reject it, I hope I’ve made it more salient to you that you can judge frameworks entirely according to how well it helps you navigate a space you’re trying to navigate. You can reject a definition that isn’t helping you, and I would encourage you to reject mine just as readily as I rejected AI Snake Oil’s if it’s not serving your purposes.

I’ll wrap it up here; if you’re benefiting from some particular way of drawing a boundary around and thinking about AI, I’d really like to hear about it.

Eight Stages of White Settler-Colonial Denial

Sat, 24 Aug 2024 00:00:00 +0000

I’ve been seeing this image floating around and I mentioned intending to provide alt text, which led me down a hole for a little while, including sifting through links that don’t work anymore, tweets that don’t exist anymore, twitter users who have deleted their accounts, etc… so I wanted to compile information in one place in case more of the web becomes impossible to search and archive effectively… But first, the original image:

… and now, the text (verbatim)

Eight Stages of White Settler-Colonial Denial

“They didn’t exist” (terra nullius)

Complete denial of Indigenous presence in a given area (country, province, etc). Includes denial of Indigeneity, e.g. “Indigenous Peoples are Settlers too”.
“If they did, they weren’t here” (terra nullius)

Denial that Indigenous People inhabit/travel/harvest/exist in a specific area. Often based on euro-centric definitions of evidence of occupation.
“If they were, they didn’t use the land” (doctrine of discovery)

Denial that Indigenous People have a connection with the Land. Often based on euro-centric worldviews of the land as something to be owned and extracted.
“If they did, they didn’t deserve it” (great chain of being)

Denial that Indigenous People have rights to their Lands. Often based on euro-centric value judgements of “primitive vs. civilized”, “nomadic vs. sedentary”.
“If they did, they lost it” (right of conquest)

Denial that Indigenous People retain their rights to their Lands. Often based on colonially imposed European systems of law / “might makes right” worldviews.
“If they didn’t, it doesn’t matter anymore” (Westphalian sovereignty)

Denial that Indigenous Rights are still binding and take precedence. Often based on false claims of supremacy of colonial legal institutions and systems.
“If it does, we need to move on”. (liberalism)

Denial that violations of Indigenous Rights requires redress. Often based on claims redress is “disruptive/unfair/reverse racism” & false calls for “equality”.
“If we can’t, we are you” (self-indigenization)

Denial of separateness of Indigenous Peoples and Rights. Often based on attempts to reduce Indigenous Rights to Human Rights, claim Indigeneity, etc.

CC SA 4.0 “Eight Stages of Settler Denial”. Revision I

Background

As far as I can tell, it’s often attributed to Dr. Kim TallBear, who has since deleted her Twitter account, but she fortunately also published a paper where she pointed out that she didn’t create the image and instead saw it on Twitter. She apparently saw it posted by someone who went by @pazuzumycete (now deleted), who may have created the graphic or may not have.

… and since I was working with the text a lot, I ended up putting it in a latex file. If you want that text, it’s here; it produces a PDF much like this:

If you’d like to download a ridiculously high-resolution version (~10k by 13k), you can access it here. I can’t imagine why you would want that, but there you go.

Destroy AI

Mon, 24 Jun 2024 00:00:00 +0000

I’ve been struggling to articulate this idea, and maybe the problem is that it’s actually kind of simple once you put it out there, and there’s really no good reason to unpack a whole case for it once you put the thought on paper.

I’m gravitating away from the discourse of measuring and fixing unfair algorithmic systems, or making them more transparent, or accountable. Instead, I’m finding myself fixated on articulating the moral case for sabotaging, circumventing, and destroying “AI”, machine learning systems, and their surrounding political projects as valid responses to harm.

In other words, I want us to internalize and develop a more rigorous appreciation of those who fuck up AI and its supporting systems.

With hegemonic algorithmic systems (namely large language models and similar machine learning systems), and the overwhelming power of capital pushing these technologies on us, I’ve come to feel like human-centered design (HCD) and the overarching project of HCI has reached a state of abject failure. Maybe it’s been there for a while, but I think the field’s inability to rise forcefully to the ascent of large language models and the pervasive use of chatbots as panaceas to every conceivable problem is uncharitably illustrative of its current state.

CHI and FAccT have failed to meaningfully respond to the deskilling of creative labor; or to the environmental or humanitarian crises these systems cause or exacerbate around the world; or even to the co-opting of our spaces by grifters and con artists making up “probabilities of doom”. Indeed, in some ways, these spaces have avoided the difficult conversations and welcomed the nonsense in to try to avoid the anguish of facing a genocide in which we are collectively implicated, or the disillusionment of confronting our own roles as agents of the corporate states that invade, surveil, displace, and kill people.

I’m no longer interested in encouraging the design of more human-centered versions of these murderous technologies, or to inform the more humane administration of complex algorithmic systems that separate families, bomb schools and neighborhoods, that force people out of their homes or onto the streets, or that deny medical care at the moment people need it most. These systems exist to facilitate violence, and HCI researchers who have committed their careers to curl back that violence at the margins have considerably more of something in them than I have. I hope it’s patience and determination, and not self-interested greed.

Regardless, there’s no way to make the administrative and bureaucratic systems of apartheid and violence more humane for the people subjugated by that system.

I think we must forcefully put on the table the possibility that we will destroy systems that fail to make a compelling affirmative case for their existence. That threat must be credible. We should actively undermine and sabotage systems, and recognize that labor as a moral project that we engage in, the way luddites sabotaged machinery that tore people apart.

This isn’t really a post or a conversation for the HCD or HCI community. I’ve grown weary (and wary, I suppose) of the design community because they ultimately seem committed to … designing systems - an ideological project antithetical to this one.

If you think of yourself as a member of that community most people call “design”, I would ask you to pose a few challenging questions to yourself. Start with these:

Do you work with systems, or people? Which would you follow, if the two paths diverge? What if if they’re in conflict and you can only follow or defend one? If you see a system dismantling a human being’s life, do you think that the system must be fixed, or that the system must be destroyed?

I wanted to end with a few positive notes: I like projects like Glaze at the University of Chicago, and while I’ve been trying to write a piece about this I came across a manifesto on mastodon that I thought was very cool and uncannily relevant. I’ve also seen some really rad indigenous art, and I know that there are other works that have explored the general space of destroying, sabotaging, and poisoning datasets and whatnot.

I’d be surprised if nobody has ever thought to put this constellation of ideas together in the same blog post - please hit me up if you’ve seen additional thoughts like this. I’d love to hear from other people who are thinking about stuff like this.

If you’ve been thinking along these lines, or if you’re one of the people I’ve linked to, then please take this as encouragement and an invitation to be in dialog with the other stuff I’ve written about and pointed to, because I think resistance is necessary, and mustn’t be captured by the design-brained people.

edit notes: Someone kindly pointed out that the “Manifesto on Algorithmic Sabotage” link was broken, and found the correct page for me. I updated it accordingly.

AI Kremlinology

Fri, 29 Mar 2024 00:00:00 +0000

I’ve been trying to dig myself out of a pretty deep depression for some time now, and while I still don’t feel like I’m out of it, I have at least found some sense of where some of this depression is coming from. I mean… in addition to all the other stuff.

A while back I wrote this paper where I tried to describe the kinds of violence that I was observing in complex algorithmic systems (let’s call them “AI” or “machine learning systems”, but this will all probably apply more broadly than either of those terms, whatever they actually mean). It seemed like people were experiencing a lot of violence that wasn’t mapping easily to a naive conceptualization of harms that was popular to talk about at the time, so I was looking for something that could kind of… bring into focus what felt like something sharp cutting obliquely - not along the dimensions that we were used to talking about.

I ultimately wrote about bureaucratic theory and administrative systems because the idea of a state - and all the history, theory, and insights about state violence - being brought to bear on seemingly new systems really appealed to me, because it made sense of a lot of the kinds of harm that otherwise seemed hard to parse for someone who had spent most of the preceding 5 years in a PhD program working among relatively ahistorical computer scientists.

It also appealed to a sense I had, that these systems were new in a strict sense… but didn’t feel new in the sense that we were seeing black people denied bail, and disabled people rejected from jobs, and trans people run out of groups and spaces for talking about gender identity… all stuff that was decidedly pretty un-novel. Whatever you wanted to say about how these systems work in new or remarkable ways, it sorta seemed like it was great at replicating unremarkable, old clichés of violence.

Historically minoritized, and particularly vulnerable people, arguably know how bureaucratic systems work, certainly better than any political scientist without direct experience does. To reference a recent episode from the 5-4 podcast, it’s not totally surprising that black rappers predicted the incomprehensible rationale of the supreme court ruling that drug-sniffing dogs don’t infringe on your constitutional protections against unreasonable searches; Jay-Z is a black man, and in that way he knows the law. Which is to say, he knows law as a function of power and oppression, in a way that any of the half-dozen constitutional law scholars who unrelatedly got quoted in the New York Times a while ago all in the midst of various existential crises over the Supreme Court’s more transparent politicization didn’t really know the law.

Constitutional law scholars, generally, don’t have an experience with the law as quotidian, as visceral, as life-or-death as black people in the US often do. Maybe it’s all to say that law professors need to know their subject in a professional way; black people, trans people, disabled people, people who are routinely harassed and targeted by cops all need to know the same subject in an existential way.

I digress; the point is that people with real experience pressed up against police, bureaucratic violence, etc… know administrative violence in a lot of the same ways that minoritized people know AI systems and their harms. Which is to say that there’s a community of technical people who know about AI professionally, and then there are people who actually know these systems because not knowing could have existential consequences.

At the end of my paper, I pointed out that two things make this algorithmic harm really possible:

the capture of people within those systems, often against their will; and
the lack of consequences for the system making the “wrong call”

I think I didn’t unpack my condemnation for systems that both capture people in these domains and also have no consequences for doing terrible things to those people, but I felt like I was painting a sufficiently clear picture of a kind of incoherent, senseless, bureaucratic dystopia - a utopia for the systems themselves rather than for us - and I hoped that people would instinctively pull back from that cliff upon seeing the precipitous fall.

Instead, it sort of seems like people embraced that incoherent, senseless, bureaucratic dystopia… and dove right in.

In the time since then, the ascent of generative AI has turned a lot of human processes and activities like learning; art; basic correspondence; as well as job, rental, and school applications; and myriad other things into a mess of noise and garbage. And it doesn’t seem like that’s getting any better, to be honest.

For some of that time, I was director of the Center for Applied Data Ethics, where I had some space to think about and try to engage with this stuff. But wave after wave of AI systems, whether for generative or discriminatory & classification purposes, made everything seem increasingly absurd and senseless. Because these systems weren’t constructing worlds that made sense according to any intrinsic reason; they were creating rules entirely extrinsically, without any reason or rationale behind any of them, purely informed by the patterns of observations from datasets - datasets not designed for these applications, to be clear.

Not being able to make things make sense made me depressed, which made me less able to make things make sense, which made me more depressed, which made me less able to make things make sense, which made me- You see where this is going.

None of this is the insight. Welcome to where my head was at, like… 2 years ago.

Over time, I felt haunted by a sense that these AIs were like a Kafkaesque nightmare. There was nothing that made sense about why these systems made any decisions that they did - it was just arbitrary, stochastic garbage. They just optimized to replicate patterns that they observed. And again, rather than pull back from the meaningless, senseless, dystopian crap that I felt was pretty clearly awful… A lot of people, and a lot of tech companies, and a lot of governments all seemed to be lured in by the tantalizing prospect of a bullshit generator of their own, finally doing the work for them.

I’d like to tell you a story; would that be okay? If not, you can skip to the next horizontal line.

In 2009 or 2010, I remember it seemingly wasn’t as common to file tax returns online as it is now. Maybe it’s more accurate to say it was something I wasn’t aware I could do. I remember filling out my tax information, printing out some documents, and driving my car over to the nearest regional processing facility - this huge shipping facility in San Jose - where you could drop off the mail.

This bit requires some explanation. Post offices and mailboxes all indicate a “last pickup” time. Sometimes it’s as early as 1pm, sometimes as late as 7pm. The idea is that if you drop off mail in a mailbox before its “last pickup” time, then the mail will be stamped and dated on that date, and if the recipient cares about that sort of thing, they’ll cut you some slack if your envelope arrives late.

This was one of those things. The IRS wouldn’t penalize you as long as your tax returns were postmarked April 15 or earlier. So if you blew past 1pm or 4pm or 7pm, you might think you were out of luck… but regional processing facilities tended to be open later, and on Tax Day this one would push their “last pickup” time to 11:59pm.

(It was a bit of ridiculous fiction, but this is the fiction we all chose and continue to live in. We just have the internet now, so you can file your returns without embarrassing yourself the way I did.)

The San Jose Processing Facility was a bit far, maybe 30 or 40 minutes away if there wasn’t traffic, or about an hour if there was. So it was worth filling out forms until 10 or 11pm and driving to the processing center in the industrial part of San Jose, near the airport.

This wasn’t a building that regular people were necessarily supposed to access all the time. When I say “USPS regional processing facility”, whatever industrial-looking picture you’re thinking of in your head is probably not industrial enough. It was a shipping yard nearby for a while, sweeping up the sprawling parking lot, and it was basically that.

I cannot put into words the chaos that you would have seen if you visited that post office at 11:30pm on April 15th in any given year. People stopped their cars in the middle of the street, and ran up to the wrong side of the building where all the trailers were, their tax returns clenched in their hands, held above their heads, as if desperately looking for a relay partner to hand a baton off to.

I think my depression came from an intuition that documenting and describing the peculiarity of AIs that cause harm seems… too peculiar to me. I know and respect a lot of the people who are leading the way on AI auditing work, but writing papers auditing algorithmic systems is a prospect that I could best describe as a kind of “AI Kremlinology”. The idiosyncratic, outsider observations of what little clues the AI systems dropped, and a lot of the writing about how and why these systems did what they did… felt a little too speculative and uncertain.

Worse, it seemed like anything I wrote about how these systems worked (or didn’t) could be modified and rendered irrelevant in the span of a few hours. And not because anyone at Google or Anthropic or any other company had any idea what they were doing, but merely to paper over the observations that I and others like me might have made. You would end up with some clunky system full of papered-over potholes, generating images of nazis who are exclusively people of color. Again, just incoherent, nonsensical garbage.

Maybe I also feel apprehensive, or maybe just frustrated, about the overarching idea of auditing in the sense of trying to construct knowledge about these arbitrary, senseless, harmful systems. The entire idea of “rationalizing” a senseless bureaucracy seems like a kind of desperate ploy to reinstate “our kind of knowledge” after being kicked out for having built these shitty systems in the first place. I don’t know if people need the language or the frameworks of computer scientists to formalize, rationalize, or legitimize the knowledge that oppressed people already have of the ways these systems suck and cause harm.

I think black rappers predicted the reality of their rights better than a constitutional law scholar would have at the time because they fundamentally needed to know how the system worked, without any of the high-minded nonsense that the ivory tower sometimes coddles and nurtures. I think incoming law students have a better sense of the reality of constitutional law and the Supreme Court because they’re more tethered to the world than law professors are. They have to be; law students don’t have tenure.

I don’t really want to write about the strange and exceptional traffic patterns outside the post office distribution center in San Jose on April 15th. I’m not saying it wasn’t chaotic, or dangerous; I’m certainly not saying it wasn’t important. But even as I write this out, it feels like I’m a strange old man telling you about how people used to have ice delivered every week for their fridges. Like, okay, that’s wild, but I don’t know what to do with that story anymore because fridges haven’t worked that way for like a hundred years. It’s such a dated and strange anecdote, and it lost relevance rapidly as electric refrigerators replaced them.

By far, the more important and enduring story would be one that sees through the ice delivery truck and sees the rearrangement of natural resources away from indigenous communities for the benefit and comfort of white people who are, it should be said, living on stolen land in the first place. And that analysis is actually pretty indifferent to the rise or fall of the ice delivery truck. More importantly, the details of that analysis are not so anachronistic that you get distracted by it.

I could write about how ChatGPT or Claude or Gemini or whatever else can’t give you an accurate count of the countries in Africa that have a “K” in their names. But then, within… an hour? Someone at one of these companies could push an update that renders my entire analysis irrelevant. And it would be almost categorically worthless, because describing how the system works or doesn’t work is the currency that I’m offering. If I don’t have the facts, or if I’m describing a system that doesn’t exist and never existed long enough for people to even remember it, then it’s perhaps even worse than the story about post office tax return shenanigans, or a refrigerator and the ice block delivery man. At least some people remember that shit.

All this is to say:

I feel like AIs are themselves a a kind of senseless, Kafkaesque nightmare where rules exist for no intrinsic reason except perhaps to justify the system itself and the power we give to that system; and it is that power that makes the rules have any bearing on reality through oppressive and coercive force, not adherence by those rules to the lived reality of … anyone.

And also: AIs, or more accurately the people and companies putting AI into everything… are making the world an increasingly senseless, nightmarish, frustrating place. They’re not the only thing making the world senseless and nightmarish, but they’re pulling their weight.

And finally: the idea of settling into a career where I document and speculate about little particular details of these senseless, arbitrary, unhinged-from-reality systems… just feels hopelessly depressing. Like… I don’t want a career in “AI Kremlinology”.

…

I have no idea where or how this piece was supposed to end. I actually do have some thoughts that I’d love to share, depending on how pitches and proposals go. One of them is… I wouldn’t say optimistic, because look at me. But I would say that it has a target on the horizon that I advocate working towards, rather than a spirit looming over me, haunting and hectoring me to keep running away.

To Live In Their Utopia (and loose threads)

Fri, 15 Jan 2021 00:00:00 +0000

I’m not sure exactly what I’m waiting for (I mean, I know why I’m waiting, but that’s another post), but in the meantime between now and when I make the paper more publicly available, I’d like to unpack a few things that I didn’t find time to talk about in the paper itself. This is a weird experience for me to have a lot that I wished I could have talked about so soon after wrapping up a paper (usually I’m so wiped out that I just need a break), but I’ll take it as a good, or at least generative, sign.

To Live In Their Utopia

If you’ve read my post about “digital forests”, then you have a sense of some part of the paper that’ll be coming out. I unpacked the idea that James Scott introduces in Seeing Like a State, trying to find useful language to talk about how and why algorithmic systems cause harms in the ways they do toward the specific people they do.

If you’ve read my street-level algorithms paper, then you might be thinking “you talked about this, didn’t you?” and the answer there is basically “no”; my feeling about the street-level algorithms paper was that it explains why it makes the wrong calls once it’s in that situation, but I didn’t have a good answer for why AIs end up in those situations in the first place.

The basic idea that I came away with is that it might be most useful to think about AIs as constructing a world from the data that we give them that represents the world. People construct their worlds, and to some extent I want to elicit this feeling that the world in any meaningful sense is constructed by AIs as well, but I didn’t go very far with that thought because the way we talk about what and how an algorithm “understands” (see, that word is already problematic) is a little clumsy - or rather, I feel clumsy trying to navigate this area. There’s a whole bunch of scholarship to work through what we mean by “responsibility” (there are different meanings and they’re not as obviously distinct when we talk about people as when we talk about machines; again, another digression), but I didn’t (and still don’t) want to get into that. Talking about algorithms having a tacit understanding of something, being “responsible” for something, etc… sometimes descends into madness depending on whether your definitions of those words come from a social context or a psychological one (which is sort of a theme, isn’t it?).

All this is to say that AIs harm people - specifically marginalized groups - because the algorithms that construct models of the world construct these worlds represented by those models where all the things they observe are explained by other things in the data that went into the model. But Bowker & Star and tons of others have written about how shallow and incomplete the data we use to describe the world is when compared to our actual, rich experiences of things.

That oversimplification is okay when it’s just me looking at step counts and recognizing that there was a 2-week chunk of time that I wasn’t running because I had recently moved and needed to self-quarantine. There’s all this surrounding information about what’s going on in the world, what the particular details of the pandemic are, what the public health regimes are for the places I was living and am now living, etc… that step_count can’t possibly express. But as a person I can look at that metric and (not quite, but almost) disregard the period of time that I know there’s more to the story represented in the data.

Algorithmic systems don’t necessarily have that information. Or rather, computational models are always missing something. What they’re missing is never obvious until you look at it later, and even then you may not realize it until much later, and even then you may not realize it ever if you’ve never lived that experience.

John Jackson wrote about “thin description” in 2013 talking about African Hebrew Israelites to try to get across that even people don’t really understand everything; the idea of doing a “thick description” in the sense that Geertz wrote is hubristic and folly. Ruha Benjamin pulls that point further to talk about technology in Race After Technology, which is also an important part of my paper (it doesn’t take up as many lines in the paper, but I consider it central in the same way that Seeing Like a State and Utopia of Rules are central).

I’d hate myself if I didn’t underscore the conceit of the paper’s title: “To Live In Their Utopia” is the beckoning call that AIs make of people whose lives meaningfully don’t fit into the boxes that trained the AI. AIs might construct a world where race isn’t a thing of its own, where white supremacy and slavery aren’t features of the training data. And so the world that the AI constructs is this delusional utopia where these things aren’t factors that explain why someone has $8 to their name when their neighbors have $250,000. It’s something about the ZIP code or something else; the direct answer - “because their ancestors were slaves” just isn’t something a computational model can wrap itself around.

Back in June 2020 I was listening to an online conversation with Frank Wilderson III, who wrote Afropessimism, who said (quoting from a frantic hand-written transcription):

The brown person embodies the demand of the return of the southwestern US; the red person embodies the demand of the return of the midwestern US; but when the Black person enters, here is the embodiment of the demand for which words can never express.

How can computational systems express these things, if people struggle to express the depths of this injustice to one another? What hope do neural networks have? What good are they if they’re this far from expressing, let alone understanding, let alone perceiving, the depths of the underlying reasons behind all of these observations we feed into the systems we use?

I’m getting away from the reason that I wanted to write this post, which was to talk about some of the things I decided not to talk about because I just didn’t have enough space to really unpack it, or time or expertise to do it justice.

Loose threads

There were two or maybe three major threads I wished I could have pulled on.

How do people work around these increasingly ubiquitous regimes?

Forrest Stuart wrote about drillers in The Ballad of the Bullet, mostly focusing on the aspirations drillers express and pursue to get out of Chicago and to get rich and famous. But he briefly talks about how drillers learned about YouTube analytics, figured out what kinds of behaviors trigger YouTube’s promotion systems (like naming other drillers, doing collaborations, posting at certain times and certain frequencies, etc…) and I think this would have been a really interesting area to dig into.

Brooke Erin Duffy has written about cosmetics/fashion YouTubers struggling to make careers out of the videos they post, and I’ve been seeing more and more of instagram influencers talk with each other about what “the algorithm” does (and how it’s changed, when they think it’s been updated in a decisive way). I think the closest people that come to mind are Motahhare Eslami (who’s done a lot of work on perceptions of news feeds and algorithmic new sfeed curation), maybe Megan French (who’s written about folk theories of algorithmic systems), Sophie Bishop (who’s actually written about discussions about how YouTube’s algorithms work - “algorithmic gossip”), and Kelly Cotter (who’s been thinking about Instagram influencers trying to be visible, which I just mentioned).

All this is to say that there’s definitely a ton of work in this area, coming from various places (a lot of Comm, some Anthro, some STS, some HCI), and I think if we’re going to think about this stuff as consequential to the design of systems (in vaguely the kind of way that people need to study and talk to homeless people to understand homelessness before designing programs to try to help people who experience homelessness), then I think there’s room for someone to find a way to bring all of this work together, or at least a paper to try to do that. I think framing this implicitly as something that we need to understand in the terms of the people experiencing these systems (that is, meeting people where they are), and framing us as designers as akin to people who nominally serve the public, would be a different framing than the way we tend to talk about designing algorithmic systems (where we talk about things in terms that trace benefits back to the interests of the company running the system).

I’m going to breeze through the other topics because this post is getting long and I didn’t want to spend this long on it. I’m sure there’s work on this stuff, but it doesn’t immediately come to mind, so feel free to point it out by contacting me

How are rules applied or felt differently?

I’ve been talking about this a bit on Twitter recently, but the whole discourse around whether it violates the president’s first amendment right to speech if you suspend his Twitter account is so stupid it’s baffling. He has other platforms and Twitter doesn’t have an obligation to preserve anyone’s right to speech; it’s their platform. We should definitely talk about how messed up it is that one platform’s decision is so consequential, but Twitter being too big for its own good isn’t a good reason to say that the president is above the rules.

It doesn’t make sense to say that the people who are influential, who have power, who have voice, should necessarily get a pass to be more problematic on your platform, and yet that’s the tact Twitter has taken. Is the logic that because he already has all this mass swirling around him, we have to feed that with more mass? That’s ridiculous. If he’s newsworthy, let him go publish a book or write missives on White House stationary.

If anything, my concerns are less with the president than with anyone else. A year ago none of these platforms were wringing their hands about deplatforming sex workers for whom the little slivers of information about potential clients (which they got from forums talking to one another) meant the difference between life and death. Sex workers have died since these platforms gave up the fight against FOSTA/SESTA.

Platforms talk about applying rules equitably, but they know that for the people with resources, any kind of bureaucratic obstacles are trivial, whereas for the people whose lives hang by a few threads, the bureaucratic hurdles people have to jump over to get access to their money (PayPal) or to their audiences (Patreon, Instagram, Twitter, Tiktok, etc…) are tremendous. Applying the rules evenly without regard for how consequential this enforcement is going to be on the person’s life is sadistic, and I think we could draw from scholarship in the social sciences to learn about how things like ID/drivers’ licenses and other administrative requirements get disproportionately felt by people working hourly, who are already at the margin, and now have to answer this bureaucratic demand.

People talk about how a parking ticket could wipe out a Black woman but hundreds of parking tickets could mean nothing to Jeff Bezos; we should talk about how Twitter temporarily suspending a Black woman on twitter trying to use that platform as part of her career is disproportionately more disruptive to her life than permanently suspending Donald Trump is disruptive of his life, because at the end of the day he’ll be fine (if he can stop having a tantrum).

I’d be remiss if I didn’t note that Uber tracks your phone’s battery level and has gone to the trouble if verifying that you’re willing to pay more if your battery is going to die. They insist that they don’t use this information to set prices; I think I speak for everyone when I say I categorically don’t believe them. But besides that, what am I supposed to do with the knowledge that Uber is very aware that they’re quoting a rate to someone who’s more desperate than usual to get a ride home, when they quote a surge rate that’s 3x or 4x? More to the point: how likely do you think you would be to get away with this kind of behavior in a small-scale app you made where you sold something like rides home to people who may be vulnerable, like drunk or worried about their safety?

How do people with power buy or flex their way out of algorithmic absurdity?

This kind of dovetails with the previous question; I’ve heard rumors of people having inside contacts at YouTube to talk to when their videos get demonetized; it seems like the kind of thing that only people with enormous followings get to have, while people with a few hundred thousand subscribers (or even a million or more?) may not get to benefit from; instead they have to deal with the algorithmic appeals processes.

The same sort of stuff happens with Apple’s app review process; some developers (Facebook, Uber, etc…) not only seem to get special access to Apple when something breaks for them, but when the apps these companies produce are found to be doing egregiously inappropriate things, they get to negotiate their ways out of it in ways that I understand smaller devs don’t. After it came to light that Facebook has been paying teens to install spyware on their phones, and after Apple suspended Facebook’s developer credentials, Facebook was able to get back in the good graces of Apple; does anyone believe Marco Arment would’ve gotten that kind of accommodation if Overcast (arguably the best podcast player on iOS) was found doing shady stuff like that? And Marco is fairly prominent, with a large audience of tech enthusiasts (and undoubtedly a number of people within Apple). How do you feel about your chances of keeping your developer account if you pulled that kind of stunt?

Who are these systems games for?

Again, touching on some of the earlier ideas: Graeber wrote about the appeal of games and play in Utopia of Rules, talking about how these are constructed rules that people can walk away from whenever they want. I think this might actually be a really effective way to frame the difference between algorithmic systems as they’re felt by vulnerable groups versus how they’re felt by people with power. I think this shines a light on the disparate experiences of these systems on people, and how people struggle with those decisions.

For people with power that they can draw on (or return to), the rules and algorithmic decisions of any single place or system don’t really matter that much. But for someone whose life and voice and power are so strongly mediated by this one platform, suspension isn’t a game; it’s a serious threat. People who don’t invest in their identities on Twitter and create new sockpuppet accounts on a whim don’t care about the rules or about the arbitrariness of Twitter’s appeals policy.

I think this ties into a bunch of the questions I just outlined, but I hope that this conveys it more as a way to arrive at a generalizable theory than as a series of ethnographic accounts or vignettes.

Anyway, that’s where my head’s been at recently; a lot of these are very generally in the same kind of space, asking how this kind of framing changes what kinds of questions we’re asking, and how. I’m thinking about how i would pursue a few of these thoughts, but for now I’m thinking about the paper as it exists and as it tries to express the framing, rather than all the new ways I could think about how algorithmic systems manifest and express values and legibility.