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Mole or cancer? The algorithm that gets one in three melanomas wrong and erases patients with dark skin

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  • The Basque Country is implementing Quantus Skin in its health clinics after an investment of 1.6 million euros. Specialists criticise the artificial intelligence developed by the Asisa subsidiary due to its "poor” and “dangerous" results. The algorithm has been trained only with data from white patients.

  • The Basque Country is implementing Quantus Skin in its health clinics after an investment of 1.6 million euros. Specialists criticise the artificial intelligence developed by the Asisa subsidiary due to its "poor” and “dangerous" results. The algorithm has been trained only with data from white patients.

    Though I get the point, I would caution against calling "racism!" on AI not being able to detect molea or cancers well on people with darker skin; its harder to see darker areas on darker skins. That is physics, not racism

  • Though I get the point, I would caution against calling "racism!" on AI not being able to detect molea or cancers well on people with darker skin; its harder to see darker areas on darker skins. That is physics, not racism

    The racism is in training on white patients only, not in the abilities of the AI in this case.

  • The Basque Country is implementing Quantus Skin in its health clinics after an investment of 1.6 million euros. Specialists criticise the artificial intelligence developed by the Asisa subsidiary due to its "poor” and “dangerous" results. The algorithm has been trained only with data from white patients.

    If someone with dark skin gets a real doctor to look at them, because it's known that this thing doesn't work at all in their case, then they are better off, really.

  • The racism is in training on white patients only, not in the abilities of the AI in this case.

    It's still not racism. The article itself says there is a lack of diversity in the training data. Training data will consist of 100% "obvious" pictures of skin cancers which is most books and online images I've looked into seems to be majority fair skinned individuals.

    "...such algorithms perform worse on black people, which is not due to technical problems, but to a lack of diversity in the training data..."

    Calling out things as racist really works to mask what a useful tool this could be to help screen for skin cancers.

  • Though I get the point, I would caution against calling "racism!" on AI not being able to detect molea or cancers well on people with darker skin; its harder to see darker areas on darker skins. That is physics, not racism

    Think more about the intended audience.

    This isn't about melanoma. The media has been pushing yellow journalism like this regarding AI since it became big.

    It's similar to how right wing media would push headlines about immigrant invasions. Hating on AI is the left's version of illegal immigrants.

  • It's still not racism. The article itself says there is a lack of diversity in the training data. Training data will consist of 100% "obvious" pictures of skin cancers which is most books and online images I've looked into seems to be majority fair skinned individuals.

    "...such algorithms perform worse on black people, which is not due to technical problems, but to a lack of diversity in the training data..."

    Calling out things as racist really works to mask what a useful tool this could be to help screen for skin cancers.

    Why is there a lack of robust training data across skin colors? Could it be that people with darker skin colors have less access to cutting edge medical care and research studies? Would be pretty racist.

    There is a similar bias in medical literature for genders. Many studies only consider males. That is sexist.

  • Think more about the intended audience.

    This isn't about melanoma. The media has been pushing yellow journalism like this regarding AI since it became big.

    It's similar to how right wing media would push headlines about immigrant invasions. Hating on AI is the left's version of illegal immigrants.

    Reading the article, it seems like badly regulated procurement processes with a company that did not meet the criteria to begin with.

    Poor results on people with darker skin colour are a known issue. However, the article says its training data containes ONLY white patients. The issue is not hate against AI, it's about what the tools can do with obviously problematic data.

    Unless the article is lying, these are valid concerns that have nothing to do with hating on AI, it has all to do with the minimal requirements for health AI tools.

  • Why is there a lack of robust training data across skin colors? Could it be that people with darker skin colors have less access to cutting edge medical care and research studies? Would be pretty racist.

    There is a similar bias in medical literature for genders. Many studies only consider males. That is sexist.

    I never said that the data gathered over decades wasn't biased in some way towards racial prejudice, discrimination, or social/cultural norms over history. I am quite aware of those things.

    But if a majority of the data you have at your disposal is from fair skinned people, and that's all you have...using it is not racist.

    Would you prefer that no data was used, or that we wait until the spectrum of people are fully represented in sufficient quantities, or that they make up stuff?

    This is what they have. Calling them racist for trying to help and create something to speed up diagnosis helps ALL people.

    The creators of this AI screening tool do not have any power over how the data was collected. They're not racist and it's quite ignorant to reason that they are.

  • Why is there a lack of robust training data across skin colors? Could it be that people with darker skin colors have less access to cutting edge medical care and research studies? Would be pretty racist.

    There is a similar bias in medical literature for genders. Many studies only consider males. That is sexist.

    Yeah, it does make it racist, but which party is performing the racist act? The AI, the AI trainer, the data collector, or the system that prioritises white patients? That's the important distinction that simply calling it racist fails to address.

  • Why is there a lack of robust training data across skin colors? Could it be that people with darker skin colors have less access to cutting edge medical care and research studies? Would be pretty racist.

    There is a similar bias in medical literature for genders. Many studies only consider males. That is sexist.

    My only real counter to this is who created the dataset and did the people that were creating the app have any power to affect that? To me, to say something is racist implies intent, where this situation could be that, but it could also be a case where it's just not racially diverse, which doesn't necessarily imply racism.

    There's a plethora of reasons that the dataset may be mostly fair skinned. To prattle off a couple that come to mind (all of this may be known, idk, these are ignorant possibilities on my side) perhaps more fair skinned people are susceptible so there's more data, like you mentioned that dark skinned individuals may have less options to get medical help, or maybe the dataset came from a region with not many dark skinned patients. Again, all ignorant speculation on my part, but I would say that none of those options inherently make the model racist, just not a good model. Maybe racist actions led to a bad dataset, but if that's out of the devs control, then I wouldn't personally put that negative on the model.

    Also, my interpretation of what racist means may differ, so there's that too. Or it could have all been done intentionally in which case, yea racist 100%

    Edit: I actually read the article. It sounds like they used public datasets that did have mostly Caucasian people. They also acknowledged that fair skinned people are significantly more likely to get melanoma, which does give some credence to the unbalanced dataset. It's still not ideal, but I would also say that maybe nobody should put all of their eggs in an AI screening tool, especially for something like cancer.

  • Reading the article, it seems like badly regulated procurement processes with a company that did not meet the criteria to begin with.

    Poor results on people with darker skin colour are a known issue. However, the article says its training data containes ONLY white patients. The issue is not hate against AI, it's about what the tools can do with obviously problematic data.

    Unless the article is lying, these are valid concerns that have nothing to do with hating on AI, it has all to do with the minimal requirements for health AI tools.

    Do you think any of these articles are lying or that these are not intended to generate certain sentiments towards immigrants?

    Are they valid concerns to be aware of?

    The reason I'm asking is because could you not say the same about any of these articles even though we all know exactly what the NY Post is doing?

    Compare it to posts on Lemmy with AI topics. They're the same.

  • My only real counter to this is who created the dataset and did the people that were creating the app have any power to affect that? To me, to say something is racist implies intent, where this situation could be that, but it could also be a case where it's just not racially diverse, which doesn't necessarily imply racism.

    There's a plethora of reasons that the dataset may be mostly fair skinned. To prattle off a couple that come to mind (all of this may be known, idk, these are ignorant possibilities on my side) perhaps more fair skinned people are susceptible so there's more data, like you mentioned that dark skinned individuals may have less options to get medical help, or maybe the dataset came from a region with not many dark skinned patients. Again, all ignorant speculation on my part, but I would say that none of those options inherently make the model racist, just not a good model. Maybe racist actions led to a bad dataset, but if that's out of the devs control, then I wouldn't personally put that negative on the model.

    Also, my interpretation of what racist means may differ, so there's that too. Or it could have all been done intentionally in which case, yea racist 100%

    Edit: I actually read the article. It sounds like they used public datasets that did have mostly Caucasian people. They also acknowledged that fair skinned people are significantly more likely to get melanoma, which does give some credence to the unbalanced dataset. It's still not ideal, but I would also say that maybe nobody should put all of their eggs in an AI screening tool, especially for something like cancer.

    My only real counter to this is who created the dataset and did the people that were creating the app have any power to affect that?

    A lot of AI research in general was first done by largely Caucasian students, so the datasets they used skewed that way, and other projects very often started from those initial datasets. The historical reason there are more students of that skin tone is because they have in general the most money to finance the schooling, and that's because past racism held African-American families back from accumulating wealth and accessing education, and that still affects their finances and chances today, assuming there is no racism still going on in scholarships and accepting students these days.

    Not saying this is specifically happening for this project, just a lot of AI projects in general. It causes issues with facial recognition in lots of apps for example.

  • My only real counter to this is who created the dataset and did the people that were creating the app have any power to affect that? To me, to say something is racist implies intent, where this situation could be that, but it could also be a case where it's just not racially diverse, which doesn't necessarily imply racism.

    There's a plethora of reasons that the dataset may be mostly fair skinned. To prattle off a couple that come to mind (all of this may be known, idk, these are ignorant possibilities on my side) perhaps more fair skinned people are susceptible so there's more data, like you mentioned that dark skinned individuals may have less options to get medical help, or maybe the dataset came from a region with not many dark skinned patients. Again, all ignorant speculation on my part, but I would say that none of those options inherently make the model racist, just not a good model. Maybe racist actions led to a bad dataset, but if that's out of the devs control, then I wouldn't personally put that negative on the model.

    Also, my interpretation of what racist means may differ, so there's that too. Or it could have all been done intentionally in which case, yea racist 100%

    Edit: I actually read the article. It sounds like they used public datasets that did have mostly Caucasian people. They also acknowledged that fair skinned people are significantly more likely to get melanoma, which does give some credence to the unbalanced dataset. It's still not ideal, but I would also say that maybe nobody should put all of their eggs in an AI screening tool, especially for something like cancer.

    Seems more like a byproduct of racism than racist in and of itself.

  • My only real counter to this is who created the dataset and did the people that were creating the app have any power to affect that?

    A lot of AI research in general was first done by largely Caucasian students, so the datasets they used skewed that way, and other projects very often started from those initial datasets. The historical reason there are more students of that skin tone is because they have in general the most money to finance the schooling, and that's because past racism held African-American families back from accumulating wealth and accessing education, and that still affects their finances and chances today, assuming there is no racism still going on in scholarships and accepting students these days.

    Not saying this is specifically happening for this project, just a lot of AI projects in general. It causes issues with facial recognition in lots of apps for example.

    They did touch on the facial recognition aspect as well. My main thing is, does that make the model racist if the source data is diverse? I'd argue that it's not, although racist decisions may have lead to a poor dataset.

  • Seems more like a byproduct of racism than racist in and of itself.

    I think that's a very possible likely hood, but as with most things, there are other factors that could affect the dataset as well.

  • It's still not racism. The article itself says there is a lack of diversity in the training data. Training data will consist of 100% "obvious" pictures of skin cancers which is most books and online images I've looked into seems to be majority fair skinned individuals.

    "...such algorithms perform worse on black people, which is not due to technical problems, but to a lack of diversity in the training data..."

    Calling out things as racist really works to mask what a useful tool this could be to help screen for skin cancers.

    Training data will consist of 100% "obvious" pictures of skin cancers

    Only if you're using shitty training data

  • Seems more like a byproduct of racism than racist in and of itself.

    Yes, we call that "structural racism".

  • The Basque Country is implementing Quantus Skin in its health clinics after an investment of 1.6 million euros. Specialists criticise the artificial intelligence developed by the Asisa subsidiary due to its "poor” and “dangerous" results. The algorithm has been trained only with data from white patients.

    It's, going to erase me?

  • My only real counter to this is who created the dataset and did the people that were creating the app have any power to affect that? To me, to say something is racist implies intent, where this situation could be that, but it could also be a case where it's just not racially diverse, which doesn't necessarily imply racism.

    There's a plethora of reasons that the dataset may be mostly fair skinned. To prattle off a couple that come to mind (all of this may be known, idk, these are ignorant possibilities on my side) perhaps more fair skinned people are susceptible so there's more data, like you mentioned that dark skinned individuals may have less options to get medical help, or maybe the dataset came from a region with not many dark skinned patients. Again, all ignorant speculation on my part, but I would say that none of those options inherently make the model racist, just not a good model. Maybe racist actions led to a bad dataset, but if that's out of the devs control, then I wouldn't personally put that negative on the model.

    Also, my interpretation of what racist means may differ, so there's that too. Or it could have all been done intentionally in which case, yea racist 100%

    Edit: I actually read the article. It sounds like they used public datasets that did have mostly Caucasian people. They also acknowledged that fair skinned people are significantly more likely to get melanoma, which does give some credence to the unbalanced dataset. It's still not ideal, but I would also say that maybe nobody should put all of their eggs in an AI screening tool, especially for something like cancer.

    There is a more specific word for it: Institutional racism.

    Institutional racism, also known as systemic racism, is a form of institutional discrimination based on race or ethnic group and can include policies and practices that exist throughout a whole society or organization that result in and support a continued unfair advantage to some people and unfair or harmful treatment of others. It manifests as discrimination in areas such as criminal justice, employment, housing, healthcare, education and political representation.[1]

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    It should be taught at schools that there is no such thing as human race, it's a fucking disgracing non-scientific term. Skin color is just that - a skin color.
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    Yeah, I agree. It's a great starting place. Recently I needed a piece of information that I couldn't find anywhere through a regular search. ChatGPT, Claude and Gemini all gave a similar answers, but it was only confirmed when I contacted the company directly which took about 3 business days to reply.
  • The Decline of Usability: Revisited | datagubbe.se

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    Just saw this article linked in a ThePrimeagen video. I didn't watch the video, but I did read the article, and all of this article is exactly what I'm always saying when I'm complaining about current UI trends and why I'm so picky about the software I use and also the tools I use to write software. I shouldn't have to be picky, but it seems like developers (professional and hobbyist alike) don't care anymore and users don't have standards.
  • No JS, No CSS, No HTML: online "clubs" celebrate plainer websites

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    Gemini is just a web replacement protocol. With basic things we remember from olden days Web, but with everything non-essential removed, for a client to be doable in a couple of days. I have my own Gemini viewer, LOL. This for me seems a completely different application from torrents. I was dreaming for a thing similar to torrent trackers for aggregating storage and computation and indexing and search, with search and aggregation and other services' responses being structured and standardized, and cryptographic identities, and some kind of market services to sell and buy storage and computation in unified and pooled, but transparent way (scripted by buyer\seller), similar to MMORPG markets, with the representation (what is a siloed service in modern web) being on the client native application, and those services allowing to build any kind of client-server huge system on them, that being global. But that's more of a global Facebook\Usenet\whatever, a killer of platforms. Their infrastructure is internal, while their representation is public on the Internet. I want to make infrastructure public on the Internet, and representation client-side, sharing it for many kinds of applications. Adding another layer to the OSI model, so to say, between transport and application layer. For this application: I think you could have some kind of Kademlia-based p2p with groups voluntarily joined (involving very huge groups) where nodes store replicas of partitions of group common data based on their pseudo-random identifiers and/or some kind of ring built from those identifiers, to balance storage and resilience. If a group has a creator, then you can have replication factor propagated signed by them, and membership too signed by them. But if having a creator (even with cryptographically delegated decisions) and propagating changes by them is not ok, then maybe just using whole data hash, or it's bittorrent-like info tree hash, as namespace with peers freely joining it can do. Then it may be better to partition not by parts of the whole piece, but by info tree? I guess making it exactly bittorrent-like is not a good idea, rather some kind of block tree, like for a filesystem, and a separate piece of information to lookup which file is in which blocks. If we are doing directory structure. Then, with freely joining it, there's no need in any owners or replication factors, I guess just pseudorandom distribution of hashes will do, and each node storing first partitions closest to its hash. Now thinking about it, such a system would be not that different from bittorrent and can even be interoperable with it. There's the issue of updates, yes, hence I've started with groups having hierarchy of creators, who can make or accept those updates. Having that and the ability to gradually store one group's data to another group, it should be possible to do forks of a certain state. But that line of thought makes reusing bittorrent only possible for part of the system. The whole database is guaranteed to be more than a normal HDD (1 TB? I dunno). Absolutely guaranteed, no doubt at all. 1 TB (for example) would be someone's collection of favorite stuff, and not too rich one.
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    I just hear that they move to LibreOffice but not to Linux, ateast not right now.
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    you don’t need to worry about trying to enforce it ( By the simple expedient of there being essentially nothing you can enforce.
  • Pocket shutting down

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    Can anyone recommend a good alternative? I still use it to bookmark most wanted sites.