Note — Mar 06, 2022

Molly Wright Steenson on Cybernetics, AI, Feedback Loops, and Data

A couple of weeks ago I published this interview with Bryan Boyer, now he’s the one doing the interviewing. Bryan spoke with Molly Wright Steenson of Carnegie Mellon University. I had nothing to do with this second one but they do fit together nicely, through feedback loops and data, and should be of interest for many Sentiers readers for Wright Steenson’s insights on the history of cybernetics and AI. The segment on how Paul Edwards defines the “closed world,” and the influence of the military on inflections in the direction of research is quite enlightening.

In 1948 the term “cybernetics” is coined, but right around the same year communication theory was coined, and with it, notions of systems, information, and feedback. The idea was that if you could describe what a system does in terms of its flows of information, then you could compare systems. So whether you are [talking about biology]; or if you’re talking about anthropology and you’re Margaret Mead or Gregory Bateson; or whether you're talking about psychology; or you're talking about politics like Stafford Beer; or maybe it’s business and operations research (which is still dominant and absolutely central to the curricula of some business schools today), then you’re talking about systems that can be compared with one another. […]

She has been looking at the idea of futures and foresight in history and pointing out that where you start from changes where you go. … if you start a history at Clemson University in the 1980s and a couple of programs there that brought Black African American students to computer science departments, then you have a history of computation that has Black African Americans at the core, and that changes the futures that are possible. […]

What happens if data is given? What happens if data is an architectural problem and a design problem? What happens if the question of data is human-centered? What happens when designers of all kinds understand that how you collect the data impacts what you even see the data as? What happens when designers always confront the fact that data has biases, because we all have biases?