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 not disabled, and I’ve had the same problems with HMO healthcare.
Those organizations drive decisions based on statistics, not the individual. I’ve seen my doctors working to find ways describe/categorize my problems so they could justify the treatment they felt was most appropriate (only after working through numerous doctors in the organization - one actually said “Well, I guess you’re just going to have to learn to live with the pain”).
Walking into an independent doctor office is completely different - they’re quick to work toward a solution, and move to a different approach when they see things aren’t improving. Because they don’t have to justify their actions to a risk/cost-management board.
Interestingly, the HMOs don’t hesitate to do surgeries. Never had any pushback there, even for things with moderate risk, but relatively low need.
I understand this is partially because I have the mindset of the programmer they’re referring to, but this sounds really interesting
Rather than looking to big data for solutions to hegemonically defined problems, what if we used it to find the catalysts of inequality themselves
…
What are the conditions in which the outlier is culled? What if we used AI to identify the pruning mechanism and dismantle it?
Using more in depth analysis of what gets pruned to understand why it’s being pruned is a very interesting concept to find marginalized groups
I don’t know how to fix those underlying problems, but identifying them and showing that data to leaders seems like a really good endeavor
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
Edit: I see from the comments this is about insurance carriers… in that case it’s not tinfoil hat at all. The wording I quoted sucks though because it’s not the AI doing it any more than it’s the hammer that drives a nail sideways.
Where did you get insurance carriers from?
No idea what your post, before or after edit, is trying to say. But the subject of your quoted sentence is “proponents of AI” not “AI”, and the sentence is about what is enabled by AI systems. Your attempt at pedantry makes no sense.
If you’re suggesting that it is possible to build an AI with none of the biases embedded in the world it learns from, you might want to read that article again because the (obvious) rebuttal is right there.
The systems didn’t do anything they weren’t told to do. You’re correct that it says proponents, but they knew what it was doing and kept doing it because it was giving them the answers they wanted regardless of reality. The AI is still like the hammer.
The systems didn’t do anything they weren’t told to do.
You’re thinking of the kinds of algorithms written by human beings. AI is a black box. No one knows how these models obtain their answers.
That’s not how programming works.
It’s how LLMs work.
Sure thing bud.