on blacknezz and data


from slavery to political campaigns, black lives have always been reduced to numbers in the form of commodities, revenue streams, statistical deviations, and vectors of risk. how do we make our escape?

During my five months as a field organizer tasked with registering people to vote, I sat behind a computer screen compulsively reviewing spreadsheets for hours at a time. Each spreadsheet organized thousands of people into neat columns of name, age, telephone number, and address, as well as special codes, percentages, and an assortment of arbitrary numerical values only decipherable by me and my colleagues. I did this day in and day out, until it became so habitual I would dream about it.

I’d often daydream in code or have nightmares about error messages. I would obsess over algorithms and became so preoccupied with data’s many exploits that whenever I met a person, I would imagine them as a row on a spreadsheet based on their ability, age, and gender. Such mental processing would go on to warp my interpretation of real events and genuine human connection even weeks after Election Day. I was no longer interested in interfacing with human bodies, just their data.

One of my duties as a field organizer was to register a shit ton of people to vote – several hundred in the county where I worked, and hundreds of thousands more across the entire state. The fastest way to register lots of people is to walk from house to house and knock on doors. I would cover between one and two hundred houses per day.

Voter registration, by its very nature, tends to exclude many poor black and brown people for being “on papers,” a euphemism for being on probation or parole, not having an address, or not being a citizen. Older black and un(der)educated people who can’t read or write are also often excluded. They are not impossible to register, but many choose to save themselves the embarrassment by declining politely. I knew this going in, but I wouldn’t pass someone up based on potentially false data or inaccurate assumptions. It wasn’t uncommon that ten out of every forty or fifty people I talked to confessed to prior criminality, being undocumented, or shielded my questions by shaming or making fun of me. Sometimes they got angry. Occasionally they confronted me with physical violence. Once, I was threatened with a garden hose nozzle and, another time by a man holding a (very docile) bull dog. While registering a group of homeless men and women at the train station, an older man in a white Jeep stepped out to ask if I “wanted to take a ride” down the block. He said he’d pay for my lunch if in return I’d trade sex. I declined his proposition and pressed on with the rest of my ten-hour day.

There were many reasons I was met with hostility. To many poor and undocumented people of color, I represented someone commonly referred to as “the law,” an epithet saved for any type of law enforcement agent (e.g. ICE, DEA, local police) who might seek to apprehend a suspect or violently trespass upon them. On one occasion, in the wake of a Category 1 hurricane, I was door-knocking two doors behind a team of FEMA agents in a Housing Authority that was severely affected by flooding and was consistently confused as an agent. People treated me like some type of an insurgent in their community, or as someone who might’ve been sent into the neighborhood to serve warrants or aid in a capture. With my voter registration forms in tow, people assumed that I was there to track their movement, since I was asking for a “current address,” or to bring trouble and possibly harm to those who were undocumented, since I was implicitly asking about their citizenship status as well. However, with every form I collected from an unregistered voter, I felt the thrall of instant gratification and – although I’m not white – a sense of white savior superiority. With every form I collected, I inched closer to fulfilling my delusional heroic-activist goal of meeting metrics set forth by a team of “data nerds,” as I called them, stationed in air conditioned offices in Brooklyn, NY, over 800 miles north of here. I viewed registering a voter the same as offering the disenfranchised a seat at the table.

There’s nothing like being yelled at by a woman old enough to be my grandmother first thing in the morning, and it didn’t take long for this idyllic vision of what I was doing to fade. I slowly realised that I hadn’t a seat at the table to give. Despite my good intentions when door-knocking in low-income neighborhoods, I could not move past the inherent power dynamics at play between me, a representative of electoral politics, and the person who came to answer the door.

Perhaps they understood something I would only realize later: that campaigns often hire wide-eyed young people who lack an understanding of neocolonialism to organize in predominately black and low income communities to trick them into feeling that politicians are on their side. At every door, I was vulnerable to rejection, insults, or violence – and for good reason. I had been hired to extract from the community, not provide a seat or access to resources. I was there to collect data that would be used to make decisions serving a tier of whiteness bent on the criminalization and imprisonment of black people.

Data has many uses, of course, and not all of them are bad. Many newspapers and other publications tell complex stories alongside data tables to humanize the numbers or contextualize their arguments. It’s true that data can aid in distinguishing facts from “alternative facts,” and the more data we have, the more informed and transparent our decision making tends to be. Take, for example, the Human Rights Campaign reporting the murder of 21 transgender people in 2015 and another 26 deaths in 2016, which alerts us to the fact that transgender and gender nonconforming people’s lives are subjected to dangers not typically faced by their cisgender counterparts. Or, the National Coalition of Anti-Violence Programs’ 2015 report, which found that 62 percent of violent homicides of LGBTQ and HIV-affected people were people of color, and 50 percent of reported victims were black. The problem is that obsessive circulation of stats has a life of its own, and often produce the opposite of our intentions – especially when they’re directed by people who live far away from black and brown communities, who never interact with queer and trans people, but who nevertheless determine the fate of these communities through their economic and legislative power. If the extent of their knowledge is reduced to statistics of crime, violence, disease, and impoverishment, it’s perhaps to be expected that the data is cited to enact policies that marginalize people further. As Shaka McGlotten describes in “Black Data,” data collection functions as both an episteme (or science) and techné (or practice) of racism. Since black people are cast as subjects having very little autonomy in the process, it should come as no surprise that over time, racial resentment and disdain for data collection would erupt from black communities.

Campaigns and nonprofits aren’t the only ones reducing black lives to various forms of accounting or politicking for profit. As McGlotten notes, this trend is caught up in a history of anti-black violence and massacre. Consider, for example, the slave ship Zong, as Christina Sharpe does in In the Wake: On Blackness and Being. Zong sailed from the coast of Africa en route to Jamaica in September 1781. On board the ship was a small crew of less than 20 carrying an estimated 470 enslaved Africans of various genders, sexualities, occupations, ages, religions, and political influence. The human lives aboard the Zong were sold by African slave catchers to white men who inspected the captured Africans’ health and ability. If selected, meaning that they met a series of profit-driven criteria, the captured Africans were detained aboard the Zong for transport. If denied, they were most likely killed on the spot. As the Zong sailed to Jamaica, the overcrowding of the vessel began to pose a substantial risk to all of its inhabitants. Many of the captured Africans, as well as members of the ship’s crew, began to die from disease and malnutrition. As anxiety set in, the ship’s captain, Luke Collingwood, gave orders to unload “cargo” from the ship’s hold into the frigid Atlantic water. By “cargo,” of course, he meant the Africans onboard. So, in November of 1781, Collingwood threw overboard a total of 133 slaves within the course of a week. Afterwards, James Gregson filed an insurance claim for 133 “units of property” lost at sea.

The Zong massacre took place in a system that did not possess the capacity to mourn the drowned Africans, but reinterpret their deaths as collateral damage in British pounds. McGlotten invites us to consider how similarly racist regimes have continued to reduce black people to numbers to justify the barbarities of slavery, the prison-industrial complex, and the ousting of black people from historically black cities and neighborhoods. “[W]e appear as commodities, revenue streams, statistical deviations, or vectors of risk,” writes McGlotten when considering the myriad of ways that big data institutions, such as insurance companies, electoral campaigns, social media sites, and so on enact this violence on black people to this day. To take up the mantle and continue this work in support of “big data” is to disregard this violence.

Reporting on the deaths of LGBTQ people opens the door, yes, to awareness, but silence, apathy, and delay as well. McGlotten proposes a new heuristic, “black data,” as one way to resist big data’s call to “reduce our lives to mere numbers” or other “forms of accounting” that conspire to bring about the harm or premature death of black people. Black data, which is inspired by black queer practices such as reading and throwing shade, can take many forms, such as “defacement, opacity, and encryption.” Black data are practices that might enable us to begin charting escape routes away from the hold of big data.

Escaping big data can seem impossible. But looking back at my time as a field organizer, the evasive practices of black data were all around me. People who reacted to my appearance, candor and optimism, with either shade, opacity, or any other technique to circumvent my door-to-door interrogation, reveal that black data forms have always underlied interactions that happen between insider/outsider, anarchist/authority, queer/non-queer, and serve to radically safeguard vulnerable communities and populations from harm or intrusion. These forms push back against or “call out” big data’s manipulation and mismanagement of black people to numbers, such as in metric-based campaigning, which places an undue burden upon black and of color communities to vote for Democratic nominees without considering how these candidates often contradict the livelihoods of black and nonblack people of color. It was only after I saw a tweet mentioning Ashton Crawley’s essay, “Otherwise Ferguson,” that I could even begin to comprehend that the frustration and antipathy I felt being aimed toward me was actually part of a black radical tradition of a nuanced set of behaviors, mores, and codes which Crawley terms “the black non-sequitur.” McGlotten reads these responses of the black radical tradition as queer praxis or “black data” practices. Both terms suggests a will to contradict or respond to authoritarian regimes with misinformation, deflection, and wit.

Whether we are considering racial profiling by police, health epidemics, or scarcity, describing the vulnerability of blackness or “black suffering” with data will limit our activism rather than drive it. Our time and efforts might be better spent plotting new and profound escape routes that collectively shade, deface, or otherwise read big data into obscurity once and for all.

source: http://www.maskmagazine.com/the-escape-issue/struggle/black-data


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