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Search results for tag #bias

[?]seedsignal » 🌐
@seedsignal@mastodon.social

@device You have stomach left after you whittle that down? Talk to me about the and what you can do to get humans to understand that 50% of this, alongside and belong to them and they've been studiously pretending it doesn't exist and blaming us for in literature and life alike when reality is and frankly, alongside thinking. 🪔🖖📣

    AodeRelay boosted

    [?]GrumpyOldFart » 🌐
    @GrumpyOldFart@expressional.social

    @IndyRichard

    @uk_politics

    Lest we forget:

    As historian AJP Taylor wryly remarked, the BBC’s first Director General [Baron John] Reith had "managed to preserve the technical independence of the BBC by suppressing news which the government did not want published. This set a pattern for the future: the vaunted independence of the was secure so long as it was NOT exercised."

    Not so @BBC5Live, @BBCRadio4, @BBCNews

      AodeRelay boosted

      [?]Broadwaybabyto » 🌐
      @broadwaybabyto@zeroes.ca

      A 24 year old Indigenous Canadian woman named Heather Winterstein died of sepsis after ER staff dismissed her repeatedly and labeled her as a homeless addict.

      There’s an inquest into her death that shows her falling to the floor in the ER and still being ignored.

      Bias, bigotry, misogyny and racism can determine the care you receive in the hospital .

      I was Heather’s age when I experienced a life threatening complication after my hysterectomy

      Like her I was sent home from the ER multiple times

      Told I was exaggerating.

      Attention seeking

      Deemed a trouble maker

      On my fourth visit my then boyfriend had to carry me in because I couldn’t even sit up

      He had to raise his voice and cause a scene

      He said he was refusing to take me home to die, and he firmly believed that’s what would happen

      Triage called security who threatened to call police

      They would rather arrest him than treat me

      Thankfully a doctor heard him yelling and came to look in on me and instantly realized something was wrong

      Within hours I was being rushed to a larger hospital for emergency surgery

      I had been bleeding internally the entire time and developed a huge infected abscess

      Had they treated me earlier, my survival odds would have been much better

      Instead I very nearly lost my life and ended up spending a month in the hospital

      I was one of the lucky ones

      No one’s survival should ever be based on luck, race, money or privilege

      Yet more often than not those things determine who lives and who dies

      My heart is heavy for Heather and all who knew and loved her

      We must do better

      We must believe and listen to patients

      We must strive to treat everyone equally

      cbc.ca/news/canada/hamilton/li

        0 ★ 0 ↺

        [?]Anthony » 🌐
        @abucci@buc.ci

        Regarding the ideological nature of what's at play, it's well worth looking more into ecological rationality and its neighbors. There is a pretty significant body of evidence at this point that in a wide variety of cases of interest, simple small data methods demonstrably outperform complex big data ones. Benchmarking is a tricky subject, and there are specific (and well-chosen, I'd say) benchmarks on which models like LLMs perform better than alternatives. Nevertheless, "less is more" phenomena are well-documented, and conversations about when to apply simple/small methods and when to use complex/large ones are conspicuously absent. Also absent are conversations about what Leonard Savage--the guy who arguably ushered in the rise of Bayesian inference, which makes up the guts of a lot of modern AI--referred to as "small" versus "large" worlds, and how absurd it is to apply statistical techniques to large worlds. I'd argue that the vast majority of horrors we hear LLMs implicated in involve large worlds in Savage's sense, including applications to government or judicial decisionmaking and "companion" bots. "Self-driving" cars that are not car-skinned trains are another (the word "self" in that name is a tell). This means in particular that applying LLMs to large world problems directly contradicts the mathematical foundations on which their efficacy is (supposedly) grounded.

        Therefore, if we were having a technical conversation about large language models and their use, we'd be addressing these and related concerns. But I don't think that's what the conversation's been about, not in the public sphere nor in the technical sphere.

        All this goes beyond AI. Henry Brighton (I think?) coined the phrase "the bias bias" to refer to a tendency where, when applying a model to a problem, people respond to inadequate outcomes by adding complexity to the model. This goes for mathematical models as much as computational models. The rationale seems to be that the more "true to life" the model is, the more likely it is to succeed (whatever that may mean for them). People are often surprised to learn that this is not always the case: models can and sometimes do become less likely to succeed the more "true to life" they're made. The bias bias can lead to even worse outcomes in such cases, triggering the tendency again and resulting in a feedback loop. The end result can be enormously complex models and concomitant extreme surveillance to acquire data to feed data the models. I look at FORPLAN or ChatGPT, and this is what I see.