Malcolm Harris on Glitch Capitalism and AI Logic
In the stories of algorithms gone haywire, the glitches prompt programmers to reassess what they really want from their programs, and how to get it. What we can learn from the errors of machine learning is that we do not have to live according to a set of rules that produces obviously unfair and undesirable outcomes like a bloated one percent, apartheid prisons, and the single worst person in the country as president. There are American political traditions that saw these problems coming and envisioned relationships between our algorithms, our state, and ourselves better than the one we have now. For instance, the final clause of the tenth point of the Black Panther Party’s 1972 Ten-Point Program was “people’s community control over modern technology” — that sounds like a good idea, especially compared to walking on your face.
But until we reassert control over our societal machine learning, we’re stuck face-planting. I remember the scholar Cornel West telling a joke about success as a narrow goal: “Success is easy!” he said. Then, mimicking a mugger, “Gimme your wallet.” America looks like a glitchy computer, and it’s because capitalism is a machine language, reducible to numbers. America exists to create wealth, and the system isn’t broken, it’s just obeying the rules to disaster; as a country, we’re more ourselves than ever. Donald Trump, who seems to be speedrunning American democracy, is like a living, breathing cheat code, proceeding through life by shortcuts alone. But if Trump represents a terminal failure of this system, it’s because he is a solution, and the easiest one in our current environment. He reminds me of another one of Shane’s examples: A program that, told to sort a list of numbers, simply deleted them. Nothing left to sort.