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Race and gender should be inputs then. The female part is actually a bit more surprising. Its easy to imagine a dataset not skewed towards black people. ~15% of the population in North America, probably less in Europe, and way less in Asia. But female? Thats ~52% globally.
What about Africa?
It seems critical to have diverse, inclusive, and equitable data for model training. (I call this concept "DIET".)
Investors will throw money at startups claiming to make their own training data by consulting experts, finetuning as it is now will be obsolete, pre-ChatGPT internet scrapes will be worth their weight in gold. Once a block is hit on what we can do with data, the data itself is the next target.
This seems like a problem that should be worked on It also seems like we shouldn't let it prevent all AI deployment in the interim. It is better that we take the disease detection rate for part of the population up a few percent than we do not. Plus it's not like doctors or radiologists always diagnose at perfectly equal accuracy across all populations. Let's not let the perfect become the enemy of the good.
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In the mean time these problems should be surfaced and corrected.
Race and gender should be inputs then. The female part is actually a bit more surprising. Its easy to imagine a dataset not skewed towards black people. ~15% of the population in North America, probably less in Europe, and way less in Asia. But female? Thats ~52% globally.
Race and gender should be inputs then. The female part is actually a bit more surprising. Its easy to imagine a dataset not skewed towards black people. ~15% of the population in North America, probably less in Europe, and way less in Asia. But female? Thats ~52% globally.
> Its easy to imagine a dataset not skewed towards black people. ~15% of the population in North America, probably less in Europe, and way less in Asia. What about Africa?
Race and gender should be inputs then. The female part is actually a bit more surprising. Its easy to imagine a dataset not skewed towards black people. ~15% of the population in North America, probably less in Europe, and way less in Asia. But female? Thats ~52% globally.
> Its easy to imagine a dataset not skewed towards black people. ~15% of the population in North America, probably less in Europe, and way less in Asia. What about Africa?
What's so striking is how strongly race shows in X-rays. That's unexpected.
Race and gender should be inputs then. The female part is actually a bit more surprising. Its easy to imagine a dataset not skewed towards black people. ~15% of the population in North America, probably less in Europe, and way less in Asia. But female? Thats ~52% globally.
Surprising? That's not a new realisation. It's a well known fact that women are affected by this in medicine. You can do a cursory search for the gender gap in medicine and get an endless amount of reporting on that topic.
Race and gender should be inputs then. The female part is actually a bit more surprising. Its easy to imagine a dataset not skewed towards black people. ~15% of the population in North America, probably less in Europe, and way less in Asia. But female? Thats ~52% globally.
Race and sex should be inputs. Giving any medical prominence to gender identity will result in people receiving wrong and potentially harmful treatment, or lack of treatment.