Gaywallet (they/it)

I’m gay

  • 9 Posts
  • 15 Comments
Joined 3 years ago
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Cake day: January 28th, 2022

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  • I suppose to wrap up my whole message in one closing statement : people who deny systematic inequality are braindead and for whatever reason, they were on my mind while reading this article.

    In my mind, this is the whole purpose of regulation. A strong governing body can put in restrictions to ensure people follow the relevant standards. Environmental protection agencies, for example, help ensure that people who understand waste are involved in corporate production processes. Regulation around AI implementation and transparency could enforce that people think about these or that it at the very least goes through a proper review process. Think international review boards for academic studies, but applied to the implementation or design of AI.

    I’ll be curious what they find out about removing these biases, how do we even define a racist-less model? We have nothing to compare it to

    AI ethics is a field which very much exists- there are plenty of ways to measure and define how racist or biased a model is. The comparison groups are typically other demographics… such as in this article, where they compare AAE to standard English.




  • I do want to point out that social media use may be one of the first of these ‘evils’ to meet actual statistical significance on a large scale. I’ve seen meta-analyses which show an overall positive association with negative outcomes, as well as criticisms and no correlation found, but the sum of those (a meta-analyses of meta-analyses) shows a small positive association with “loneliness, self-esteem, life satisfaction, or self-reported depression, and somewhat stronger links to a thin body ideal and higher social capital.”

    I do think this is generally a public health reflection though, in the same way that TV and video games can be public health problems - moderation and healthy interaction/use of course being the important part here. If you spend all day playing video games, your physical health might suffer, but it can be offset by playing games which keep you active or can be offset by doing physical activity. I believe the same can be true of social media, but is a much more complex subject. Managing mental health is a combination of many factors - for some it may simply be about framing how they interact with the platform. For others it may be about limiting screen time. Some individuals may find spending more time with friends off the platform to be enriching.

    It’s a complicated subject, as all of the other ‘evils’ have always been, but it is an interesting one because it is one of the first I’ve personally seen where even kids are self-recognizing the harm social media has brought to them. Not only did they invent slang to create social pressures against being constantly online, but they have also started to self-organize and interact with government and local authority (school boards, etc.) to tackle the problem. This kind of self-awareness combined with action being taken at such a young age on this kind of scale is unique to social media - the kids who were watching a bunch of TV and playing video games didn’t start organizing about the harms of it, the harms were a narrative created solely by concerned parents.





  • Any information humanity has ever preserved in any format is worthless

    It’s like this person only just discovered science, lol. Has this person never realized that bias is a thing? There’s a reason we learn to cite our sources, because people need the context of what bias is being shown. Entire civilizations have been erased by people who conquered them, do you really think they didn’t re-write the history of who these people are? Has this person never followed scientific advancement, where people test and validate that results can be reproduced?

    Humans are absolutely gonna human. The author is right to realize that a single source holds a lot less factual accuracy than many sources, but it’s catastrophizing to call it worthless and it ignores how additional information can add to or detract from a particular claim- so long as we examine the biases present in the creation of said information resources.


  • This isn’t just about GPT, of note in the article, one example:

    The AI assistant conducted a Breast Imaging Reporting and Data System (BI-RADS) assessment on each scan. Researchers knew beforehand which mammograms had cancer but set up the AI to provide an incorrect answer for a subset of the scans. When the AI provided an incorrect result, researchers found inexperienced and moderately experienced radiologists dropped their cancer-detecting accuracy from around 80% to about 22%. Very experienced radiologists’ accuracy dropped from nearly 80% to 45%.

    In this case, researchers manually spoiled the results of a non-generative AI designed to highlight areas of interest. Being presented with incorrect information reduced the accuracy of the radiologist. This kind of bias/issue is important to highlight and is of critical importance when we talk about when and how to ethically introduce any form of computerized assistance in healthcare.





  • It’s removedING OBVIOUS

    What is obvious to you is not always obvious to others. There are already countless examples of AI being used to do things like sort through applicants for jobs, who gets audited for child protective services, and who can get a visa for a country.

    But it’s also more insidious than that, because the far reaching implications of this bias often cannot be predicted. For example, excluding all gender data from training ended up making sexism worse in this real world example of financial lending assisted by AI and the same was true for apple’s credit card and we even have full-blown articles showing how the removal of data can actually reinforce bias indicating that it’s not just what material is used to train the model but what data is not used or explicitly removed.

    This is so much more complicated than “this is obvious” and there’s a lot of signs pointing towards the need for regulation around AI and ML models being used in places it really matters, such as decision making, until we understand it a lot better.