151–160 of 180 posts
Garbage research.
..”In this modelling study, we defined race as a social, political, and legal construct that relates to the interaction between external perceptions (ie, “how do others see me?”) and self-identification, and specifically make use of self-reported race of patients in all of our experiments.” Garbage research.
Earlier quoted context omitted.
I could be entirely wrong here, so if you've got more context in this area by all means correct me. Consider an "AI" that rates the probability of recidivism for prisoners nearing their parole date. That score would then be presented to the parole board, and taken into consideration in determining whether or not to grant parole. If this AI were accidentally/incidentally accurately determining the race of the prisoner…
At the risk of discussing sensitive topics on a platform ill-suited: If, in your hypothetical recidivism case, an AI "accurately" determined that a pattern of higher recidivism-related features was correlated to race, and was able to determine "accurately" that the specific subset of recidivism-related features predicted race, why would it be wrong to make parole decisions using those recidivism-related features?
The interpretation part hit home: "The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging.…
I suspect this is a "tank vs sky" problem. The article says that the bright areas of bone are not the most important for predicting race. What if it's some features of different hospitals and x-ray setups? Also did they release their code and anonymized data? If not, it's impossible to tell if this is a bug. If I got this result in my work, I would check it 10k times over because it defies belief. Even allowing subtl…
Apparently this is a known and persistent affect across a variety of other medical images, tests, and scans. Not just for a "race" but for ethnic groups in general, as well as biological sex. So this might actually just be an "AI hit piece" that otherwise confirms an unpalatable but persistent and strong effect in the literature. The causes seem to be badly understudied, in part due of the obvious need for delicacy and respect around such topics.
This result is tremendously implausible to me, but I am finding quite a few articles documenting similar phenomena across things like retina scans and brain MRIs.
Earlier quoted context omitted.
Not to get into a flame war, but I want to present an alternate option to yours. Because in the US some people have a hard time understanding that all races and genders deserve to be treated equally as humans with the same access to goods and services. Further, that there are disparities in care based on race/ethnicity[1][2] and gender[3][4] because of that racism/sexism present in the systems. This then leads to req…
It sometimes makes sense to scrub race/ethnicity/gender information from certain types of data, typically when a human is going to be making individual decisions. For example, not having race data on resumes is generally productive, because that categorization can't provide a meaningful input to the decision associated with an individual person. Even if it were to be the case that there was some correlation between r…
The submitted title ("AI identifies race from xray, researchers don't know how") broke the site guidelines by editorializing. Submitters: please don't do that - it eventually causes your account to lose submission privileges. From the guidelines ( https://news.ycombinator.com/newsguidelines.html ): " Please use the original title, unless it is misleading or linkbait; don't editorialize. "
Earlier quoted context omitted.
At the risk of discussing sensitive topics on a platform ill-suited: If, in your hypothetical recidivism case, an AI "accurately" determined that a pattern of higher recidivism-related features was correlated to race, and was able to determine "accurately" that the specific subset of recidivism-related features predicted race, why would it be wrong to make parole decisions using those recidivism-related features?
If you decided on race, in this instance, you would be making people much more deterministic as a result of the power of race. Race is too broad a concept to reliably say that all white people are at X chance of recidivism. Instead we want to know if Marlowe is at risk of high recidivism based on her character.
The question I was posing is different, though, because this was discussing an AI system that looked at the underlying [in this case, recidivism] data which had race and race-adjacent information removed, and the AI has effectively rediscovered the concept of "race" by connecting it to some set of attributes of the actual [in this case, recidivism-predicting] features. If the AI were to determine such a link, that doesn't make its results biased, it just makes them uncomfortable. It's not clear to me that in such a case that would mean that we should remove those [recidivism-predicting] features from the dataset just because they ended up being correlated to race.
I read once that a radiologist can't always explain what they see in an image that leads them to one diagnosis or another, they say that after seeing many of them they just know.
So I suspect the same could be done for race. This would be a super interesting thing to try with some college students - pay them to train for a few days on images and see how they do.
The submitted title ("AI identifies race from xray, researchers don't know how") broke the site guidelines by editorializing. Submitters: please don't do that - it eventually causes your account to lose submission privileges. From the guidelines ( https://news.ycombinator.com/newsguidelines.html ): " Please use the original title, unless it is misleading or linkbait; don't editorialize. "
It's the title of the Vice article about the same topic. https://www.vice.com/en/article/wx5ypb/ai-can-guess-your-rac... (It was posted last year.) (No idea why the OP used one title and another URL.) (The title of Vice is a bad title anyway.)
..”In this modelling study, we defined race as a social, political, and legal construct that relates to the interaction between external perceptions (ie, “how do others see me?”) and self-identification, and specifically make use of self-reported race of patients in all of our experiments.” Garbage research.
Perfect example of citations-driven research. The authors aren’t motivated by a genuinely interesting scientific question (“are anatomical differences between genetically distinct groups of people visible in X-rays?”). Instead, the authors know that training a classifier to predict race will generate controversial headlines and tweets. All publicity, positive or negative, leads to more citations.
Is race a genetically distinct marker though? I guess if you limit the sample enough it is, but I've always thought of race as more of a continuous quality than a distinct one.