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Cake day: June 19th, 2023

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  • andallthat@lemmy.worldtoTechnology@lemmy.world*Permanently Deleted*
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    1 month ago

    I’m not sure we, as a society, are ready to trust ML models to do things that might affect lives. This is true for self-driving cars and I expect it to be even more true for medicine. In particular, we can’t accept ML failures, even when they get to a point where they are statistically less likely than human errors.

    I don’t know if this is currently true or not, so please don’t shoot me for this specific example, but IF we were to have reliable stats that everything else being equal, self-driving cars cause less accidents than humans, a machine error will always be weird and alien and harder for us to justify than a human one.

    “He was drinking too much because his partner left him”, “she was suffering from a health condition and had an episode while driving”… we have the illusion that we understand humans and (to an extent) that this understanding helps us predict who we can trust not to drive us to our death or not to misdiagnose some STI and have our genitals wither. But machines? Even if they were 20% more reliable than humans, how would we know which ones we can trust?







  • About 20 new cases of gender violence arrive every day, each requiring investigation. Providing police protection for every victim would be impossible given staff sizes and budgets.

    I think machine-learning is not the key part, the quote above is. All these 20 people a day come to the police for protection, a very small minority of them might be just paranoid, but I’m sure that most of them had some bad shit done to them by their partner already and (in an ideal world) would all deserve some protection. The algorithm’s “success” in defined in the article as reducing probability of repeat attacks, especially the ones eventually leading to death.

    The police are trying to focus on the ones who are deemed to be the most at risk. A well-trained algorithm can help reduce the risk vs the judgement of the possibly overworked or inexperienced human handling the complaint? I’ll take that. But people are going to die anyway. Just, hopefully, a bit less of them and I don’t think it’s fair to say that it’s the machine’s fault when they do.







  • Just wanted to point out that the Pinterest examples are conflating two distinct issues: low-quality results polluting our searches (in that they are visibly AI-generated) and images that are not “true” but very convincing,

    The first one (search results quality) should theoretically be Google’s main job, except that they’ve never been great at it with images. Better quality results should get closer to the top as the algorithm and some manual editing do their job; crappy images (including bad AI ones) should move towards the bottom.

    The latter issue (“reality” of the result) is the one I find more concerning. As AI-generated results get better and harder to tell from reality, how would we know that the search results for anything isn’t a convincing spoof just coughed up by an AI? But I’m not sure this is a search-engine or even an Internet-specific issue. The internet is clearly more efficient in spreading information quickly, but any video seen on TV or image quoted in a scientific article has to be viewed much more skeptically now.