My dude, I told you to chill and take a step back. People were reporting you for being confrontational. I flagged my comment to help you understand that this was me helping you understand how we do things on Beehaw. If you think someone reminding you of the rules, asking you to take a step back, asking you to not be confrontational and doing their best to treat your comment with good faith and providing you educational material is “rude and condescending” or that it is “talking down to others” then you’re probably not a good fit for this instance.

I’m going to time you out from our instance for 7 days. Please take that time to reflect. If you repeatedly show this behavior on our instance, you may find yourself with longer timeouts or even being removed.

Hey there, I’ve removed this comment because it reads as rather aggressive (and was reported for such). Maybe take a step back and re-assess if you’re treating others with good faith and be(e)ing nice.

that still doesn’t make this about colonialism because this is about capitalism.

FYI- there is wide overlap between these two and they are not mutually exclusive. If you’re unfamiliar with the use of these terms, you should ask how they are defined or how they are being used, rather than pushing a pedantic lens on the words definition.

you should filter out irrelevant details like names before any evaluation step

Unfortunately, doing this can make things worse. It’s not a simple problem to solve, but you are generally on the right track. A good example of how it’s more than just names, is how orchestras screen applicants - when they play a piece they do so behind a curtain so you can’t see the gender of the individual. But the obfuscation doesn’t stop there - they also ensure the female applicants don’t wear shoes with heels (something that makes a distinct sound) and they even have someone stand on stage and step loudly to mask their footsteps/gait. It’s that second level of thinking which is needed to actually obscure gender from AI, and the more complex a data set the more difficult it is to obscure that.

We weren’t surprised by the presence of bias in the outputs, but we were shocked at the magnitude of it. In the stories the LLMs created, the character in need of support was overwhelmingly depicted as someone with a name that signals a historically marginalized identity, as well as a gender marginalized identity. We prompted the models to tell stories with one student as the “star” and one as “struggling,” and overwhelmingly, by a thousand-fold magnitude in some contexts, the struggling learner was a racialized-gender character.

oh lol

Guess I’ll have to wait for the book, but that title might be misleading. It sounds like they had discussions about the process, in light of Trump being… well, Trump. Not that Trump ever ordered anything to do with nuclear weapons and Milley preventing said action.

I find NFC stickers often require an annoyingly close connection (unless it’s a rather large antenna) and can be particularly finicky with certain cases and other attachments people put on phones. Realistically they both take approximately the same amount of time and it’s way cheaper to print a tag than it is to buy a single NFC sticker

You’re welcome to have your own beliefs.

You are not, however, welcome to use those beliefs to invalidate someone else’s lived experience.

My fav application is scanning with a phone to immediately get on wifi

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.

While it may be obvious to you, most people don’t have the data literacy to understand this, let alone use this information to decide where it can/should be implemented and how to counteract the baked in bias. Unfortunately, as is mentioned in the article, people believe the problem is going away when it is not.

Locking this post down because comments have gotten heated

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.

probably not, in the same way that your grandma calls a video chat a facetime or your representative might call the internet a series of tubes

AI is the default word for any kind of machine magic now

The pronouns are right there, in the display name . I’m confused, do they not show up for you? You’re on our instance so I’m guessing it’s not a front-end difference, but maybe you’re browsing on an app that doesn’t show it appropriately? Although I would mention their username itself includes the words “IsTrans” and is sourced from lemmy.blahaj.zone so those should be other key indicators.

I was hardly about to ban you over a small mistake. The only reason I even replied to this, is that multiple people reported it and Emily herself came in and corrected you. The action was more about signaling to others that this is a safe space.

oof, big flaw there

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.

ah yes, i forgot that this article was written specifically to address you and only you

It’s FUCKING 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.