Proponents of AI and other optimists are often ready to acknowledge the numerous problems, threats, dangers, and downright murders enabled by these systems to date. But they also dismiss critique and assuage skepticism with the promise that these casualties are themselves outliers — exceptions, flukes — or, if not, they are imminently fixable with the right methodological tweaks.
Common practices of technology development can produce this kind of naivete. Alberto Toscano calls this a “Culture of Abstraction.” He argues that logical abstraction, core to computer science and other scientific analysis, influences how we perceive real-world phenomena. This abstraction away from the particular and toward idealized representations produces and sustains apolitical conceits in science and technology. We are led to believe that if we can just “de-bias” the data and build in logical controls for “non-discrimination,” the techno-utopia will arrive, and the returns will come pouring in. The argument here is that these adverse consequences are unintended. The assumption is that the intention of algorithmic inference systems is always good — beneficial, benevolent, innovative, progressive.
Stafford Beer gave us an effective analytical tool to evaluate a system without getting sidetracked arguments about intent rather than its real impact. This tool is called POSIWID and it stands for “The Purpose of a System Is What It Does.” This analytical frame provides “a better starting point for understanding a system than a focus on designers’ or users’ intention or expectations.”
I'm not really sure what the author is trying to do here. The way he plays with the meaning of words, like "culling the outlier" is literary interesting. But it is also actively harmful to understanding or bettering the issues raised.
"AI" is interpreted as "algorithmic inferences." This paves over any of the technical distinctions between statistics, ML, AI, and neural nets. In the current hype, the term AI is often narrowed down to mean neural nets but the author widens the meaning. In the text, "AI" includes any kind of bureaucratic or rule-based decision-making.
The effect is to transfer responsibility away from decision-makers, organizations, and even society, at large, to a vaguely understood new technology.
I can see that this could be welcome to these decision-makers and organizations. And so it has the potential to attract funding from them. Perhaps that is the point.
She (or, if you're not sure, they).
Human-written rules are often flawed, and for similar reasons (the sole human thought process that 'AI' is very good at reproducing is system justification). But human-written rules can be written down and they can be interrogated. But Apple landed itself in court because it had no clue how its credit algorithm worked and could not conceive how it could possibly be sexist if the machine didn't get any gender data to analyse.
That is, indeed, the point.
I think you misunderstand. She is shifting responsibility.
This appears to be wrong.