I think the article can make a clearer point of what it warns for. For starters, it starts by talking about AI and algorithms and combines them as one. I think the narrative would be better served...
I think the article can make a clearer point of what it warns for.
For starters, it starts by talking about AI and algorithms and combines them as one. I think the narrative would be better served when separating them.
As far as I see it:
“Automation bias” is the human tendency to reduce vigilance and oversight when working with machines.
This is an issue we are already facing with algorithms used in a variety of places. Some of which can and have wreaked havoc because of this reduced vigilance and lack of oversight.
The current wave of LLM based "AI" also have a lot of the same issues as “simpler” algorithms, like biased input (training data). Not even in theory, in practice this has already been seen.
Many people will not even be aware of the limitations and faults of LLMs.
Despite that many applications are pushed on the market, often poorly tested and with very little auditing and insight.
All of this combined makes the issue of “automation bias” increasingly more commonplace.
Most of these stem from the "I'm not biased therefore my dataset is not biased" fallacy. We see the world as coherent when our minds are at peace and chaotic when we have something that is...
Most of these stem from the "I'm not biased therefore my dataset is not biased" fallacy. We see the world as coherent when our minds are at peace and chaotic when we have something that is troubling us--but the world in aggregate doesn't change nearly as quickly as our individual circumstances do.
We really aught to leave statistical analysis and interpretation to the trained professionals. Issues of diagnosis for organizational and operational decision-making often does more financial damage than healthcare malpractice. Of course, lives and livelihood are not equivalent but there should be more negative consequences for misapplication of these technologies (and mismanagement in general) to discourage overuse.
I think the article can make a clearer point of what it warns for.
For starters, it starts by talking about AI and algorithms and combines them as one. I think the narrative would be better served when separating them.
As far as I see it:
Most of these stem from the "I'm not biased therefore my dataset is not biased" fallacy. We see the world as coherent when our minds are at peace and chaotic when we have something that is troubling us--but the world in aggregate doesn't change nearly as quickly as our individual circumstances do.
We really aught to leave statistical analysis and interpretation to the trained professionals. Issues of diagnosis for organizational and operational decision-making often does more financial damage than healthcare malpractice. Of course, lives and livelihood are not equivalent but there should be more negative consequences for misapplication of these technologies (and mismanagement in general) to discourage overuse.