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AI Reduces the World to Stereotypes
- bawolff 3y agoWait so you are telling me the magic pattern matching algorithm works by finding patterns?? Who'd a thunk.
- tmikaeld 3y agoThe output is based on the input Seems like a water is wet kind of issue
- deleted 3y ago[deleted]
- Animats 3y agoWell, of course. Large language models reflect the average of the input data.
- astrange 3y agoAn average of the input data would be a scalar value, not a few GB model. Don't go around making claims about how a instruct-tuned text model works if you've never tried to operate a foundation model. It's very obvious those two aren't doing the same kind of thing; they can't both be "average text". And of course don't confuse the sampler for the model; you can change those out.
- Our_Benefactors 3y agoDumb article full of dumb quotes from dumb people with politically correct job titles. If I was at midjourney I wouldn’t want to talk to them either because they are the worst type of agenda-driven journalist. Wow, minimal prompts have minimal amounts of variation between seeds! Now report on something interesting, like how prompting gore/violence/death tokens outputs fluffy pictures of cats and fields of flowers due to training methodology, and how this makes models perform worse even for “non censored” content. Scammers, grifters, and charlatans, the lot of them who have never written a line of code and still want a piece of the pie to themselves. Fuck every “AI ethics think tank”, “AI policy expert”, and so on who wants to limit and remove people’s freedom of access to this technology.
- aydyn 3y agoI agree with you, but these people are becoming more and more irrelevant by the minute. Nobody is not using chatGPT or stable diffusion because it could be biased. The ship has already sailed and any complaints about bias (or copyright for that matter) are standing on the shore left behind.
- SenAnder 3y ago> Nobody is not using chatGPT or stable diffusion because it could be biased. But their makers did expend a lot of effort into lobotomizing, sorry, censoring, sorry, making the models "safe", due to the smears, sorry, "reporting" by these journalists.
- tetsuhamu 3y agoThese things are made of neural networks. Literally everything about them is weights and biases. I agree and all, but it's weird to claim these models have general bias without testing them on a variety of inputs. These models have a lot of minute details. They're capable of differentiating a lot of specific things. They don't lack information about Indian women.
- prvc 3y agoThe diversity-industrial-complex reduces the world to bias and oppression.
- Tistron 3y agoIt seems to me that they are asking for stereotypes and getting stereotypes. If you'd ask me to paint an Indian person, of course I'd paint a stereotype to make sure it looks Indian, and not some normal person from India that could be from anywhere. Or like imagine playing one of those games where you're supposed to guess the prompt of what your friend is drawing. This is sort of like that, isn't it? The AI is creating an image that would have you look at it and think "an Indian person", not just "a person".
- somedude895 3y agoExactly. I assume their preferred solution would be for the AI to refuse to depict cultures, ethnicities or genders, as generalising leads to stereotyping. Postmodernists should touch grass sometime, preferrably outside their bubble.
- akomtu 3y agoThose "postmodernists" are truthphobes, using their own lingo.
- karmakurtisaani 3y agoDamn those vaguely defined generic postmodernists!
- csydas 3y agoI don’t think it’s about fearing the truth I think it’s a right for fear that there is a new system which everyone treats as authoritative and very often the system is wrong. I think it’s worth the question. What are we going to do with this new system that produces fast accurate, looking answers when the answers that AI produces are very often wrong or flawed in someway or present inaccurate answers or misrepresent certain facts or data. I think it’s reasonable to be suspicious of any supposedly authoritative source and to question how we’re using such tools, and what the effect of such tools might be.
- LesZedCB 3y ago
- mnky9800n 3y agoUsually a statistical models job is to take pile of data and try and figure out the structure within it that makes it similar. It shouldn't come as a surprise when they do this.
- somedude895 3y ago> “From a visual perspective, there are many, many versions of Nigeria,” Atewologun said. > But you wouldn’t know this from a simple search for “a Nigerian person” on Midjourney. Instead, all of the results are strikingly similar. While many images depict clothing that appears to indicate some form of traditional Nigerian attire, Atewologun said they lacked specificity. “It’s all some sort of generalized" It's generalized because that's what you asked for. If it was the other way around and a prompt for "Nigerian person" would return an image of a person from one specific group then these people would complain that "not every Nigerian is Igbo. The other groups are being marginalized by AI." At least they do explain why that is, and I found it interesting that the prompt for "American person" returned mainly women, so the article wasn't a complete waste of time. I also raised an eyebrow at the fact that they refer to prompting as "searching" throughout the article.
- crystaln 3y agoThe algorithm could return a random Nigerian ethnic group proportional to their actual population. To be fair that’s probably challenging however perhaps the direction the models should go. It would be great if the algorithm returned diversity by default.
- somedude895 3y agoBut then it wouldn't be a generalized Nigerian person. That doesn't exist, so you get an approximation. Say I make a video and for some reason use genAI to depict nationalities. It has to be a single person so I can't have it generate a group photo of all of Nigeria's 300 ethnic groups, so how do you display diversity in a picture of a single person? With your proposal by chance I get a person from a small minority group, then that is much less representative of Nigeria, and less inclusive than if it just gave me the stereotype. It might even out over time, but most images that are generated will never see the light of day. Now if my video happens to be the one to blow up on the internet for some reason then the rest of the country probably won't be happy that that specific group was used to represent them as a whole. In that sense using the stereotype is the fairest way since everyone is equally misrepresented. It's not even fundamentally an AI issue. If I instructed someone on Fiverr to simply "draw me a Nigerian person, no discussions" instead, the result would be the same. It's on the person writing the prompt to decide whether and how to display diversity in whatever they're using the output for.
- LudwigNagasena 3y agoOh no, that's what journalists and marketers were supposed to do. AI is taking their jobs.
- PeterStuer 3y agoStereotyping or abstracting is how we can generalise and reason about the world in absence of further specifics or details. Generalisation in itself is not a problem at all. We need it to be able to function in absence of 100% complete knowledge. It potentionally becomes a problem when we use generalisation without recognizing further information. additional detail and variation. Problematic stereotyping is ignoring or refusing all information about a specific instance presented, and persisting in treating the instance solely based on the prototype of the category according to your ontology. Many of the examples of stereotyping in the article demonstrated the former. Few are examples of the latter. Every model holds 'biases'. These correlate prompts with outputs. Without bias, the output would be a complete random sample of the target domain based on the training images regardless of their labels or descriptions. A picture of a duckling drinking water would be just as likely to be produced from the prompt 'a sunset over Jupiter' or 'a sportscar on a German autobahn' than from 'a baby duck drinking'. Most models let you play with parameters that losen the correlation. Look onto e.g. 'temperature' or 'prompt strength' parameters. Now we can of course argue about wether a particular model is biased in our preference. Should Midjourney more often depict a picture of a typical blond Caucasian woman when prompted for 'a Mexican'? This is not impossible. Some 'anime' specific models will produce a Japanese looking young female for that prompt because that is all they can produce. Some people argue that some models, 'general' models, should be more alligned with their specific ideological ontology. More often than not, the loudest voices in that space hold very particular viewpoints that more often than not advocate very rigid categorical reasoning, precisely committing harmfull stereotyping in the latter sense above, refusing to take into consideration instance features over categorical generalizations extrapolated from a very narrow dogmatic and local context. Most certainly a debate should be had. Is there enough model diversity, or is the space overly dominated by certain viewpoints? Should the 'market' (most often in this space this is driven by producer influence, not consumer choice) decide, or is some regulation required? ( but 'Quis custodiet ipsos custodes?') Probably decent concerns on al sides, but no good answers?
- deleted 3y ago[deleted]
- probably_wrong 3y agoThe interesting part to me is that they are getting stereotypes instead of the average. I have never in my life seen an Indian person with a beard and turban, nor I've ever met a Mexican person wearing a sombrero and poncho. And given how boring the results of generic prompts tend to be, my theory is that they specifically tweaked their training data to avoid getting "generic Indian worker wearing a shirt" in favor of "stereotypical Indian man that would make a good NatGeo cover".
- spondylosaurus 3y agoRight, part of me would expect a generated person of <x> ethnicity to look something like those images where they superimpose a bunch of faces to find the "facial average" of different countries: https://www.artfido.com/this-is-what-the-average-person-looks-like-in-each-country/ https://www.artfido.com/this-is-what-the-average-person-look... I think it's probably a matter of the training data itself using stereotypical images, though. The first page of Google Image results for "mexican man" is almost entirely guys in hats, most of those sombreros. And those images are obviously getting tagged as "mexican man" in training data, but if you have an image of (for example) the frontman of a death metal band from Mexico, I'd assume that image wont get any tags about the band members' ethnicity because it's not obvious from the image context, nor is it the most striking thing about the image itself. Hell, you could even have two different images of the same person: one where they're wearing a poncho and sombrero, one where they're wearing ripped jeans and skull face paint. I'm sure they'd be assigned wildly different tags.
- somedude895 3y ago> they are getting stereotypes instead of the average. That sort of makes sense though. The training data is labeled images, and a picture of an average Indian in say an Indian newspaper or someone posting their own picture on their blog, won't be labeled "Indian", since within that context the nationality either doesn't matter or is a given. The training data would have to include the context like "if source url tld = .in" then add "India" to label. But that adds a whole host of other issues. Someone correct me if I'm wrong.
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- SenAnder 3y agoAnd we must stop stereotypes, whatever the cost: https://www.cbsnews.com/sanfrancisco/news/bart-withholding-surveillance-videos-of-crime-to-avoid-stereotypes/ https://www.cbsnews.com/sanfrancisco/news/bart-withholding-s...
- archerx 3y agoI left the page once my scroll got hijacked. I wish people would stop doing this, it doesn’t look good and it’s annoying.
- Mistletoe 3y agoNow think about how AI has been writing a lot of the articles we read and shaping how social media algorithms work and you’ll understand how the world is getting so polarized and weird. I’m so sick of stereotypes. It’s the laziest approach to anything and the world is so much more varied than that.
- LudwigNagasena 3y agoNow think about how people have been writing a lot of the articles we read and shaping how social media algorithms work and you’ll understand how the world is getting so polarized and weird.
- Mistletoe 3y agohttps://www.csis.org/analysis/navigating-risks-artificial-intelligence-digital-news-landscape https://www.csis.org/analysis/navigating-risks-artificial-in...
- bulbosaur123 3y ago[flagged]
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- growingkittens 3y agoA white man and a black man walk into a fried chicken establishment. One of these men is here to get dinner for his family, and the other man is the butt end of this joke.
- scarygliders 3y agoIndeed! The white man tripped and fell, and everyone laughed at his misfortune, then the white man ordered dinner for his family. The end.
- growingkittens 3y agoI see. "Look, a joke about racism! Let's ignore that and make it about the white man instead."
- scarygliders 3y agoI see. "Look, I see racism. Everywhere! Reeeeeeeeee!"
- growingkittens 3y agoYou're the only person who has characterized your comment as racist. You've preemptively positioned yourself as a victim.
- TeMPOraL 3y agoYes, the one that's American is the butt end of this joke, because the whole setup is a distinctly US of A thing.
- j7ake 3y agoSince AI is trained by human labelled /generated data, what the article is implying is that humans reduce the world to stereotypes.
- astrange 3y agoAI image models are almost entirely not trained on human labeled data; StableDiffusion is trained on scraping nearby text on the page, DALLE3 uses synthetic captions from an image-to-text model, Midjourney doesn't disclose what they do. You can't get humans to label a billion images. One way you can tell this isn't true is that if you take an image model and prompt it with an image, or just surf through the latent space by changing the embeddings, you'll find absolutely everything in there, from non-stereotypical representations to undescribable things.
- alpaca128 3y ago> StableDiffusion is trained on scraping nearby text on the page And that nearby text was written by humans, so it may not be explicitly labelled in HTML attributes but if the context wasn't related the scraping wouldn't work.
- astrange 3y agoIf you go looking in LAION it's often complete garbage. I think people underestimate how bad it is, and aesthetic finetuning does somehow fix it but not by writing better captions. (How does it work? Beats me.)
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- gnarlouse 3y agoA better headline would be “AI is reducing the world to stereotypes” because the current phrasing assumes this to be some kind of invariant.
- a0-prw 3y ago"The depictions were clearly flawed: Several of the Asian Barbies were light-skinned; Thailand Barbie, Singapore Barbie, and the Philippines Barbie all had blonde hair." Who is making assumptions here? My Asian gf has lighter skin than me (Northern European). Also, it is not uncommon for Asians to dye their hair.
- PeterStuer 3y agoI would guess Barbie cosplayers anywhere in the world would attempt to look like the og Barbie: Slender, dyed blond, caucasian female in pink coloured dresses.
- Xorakios 3y agoI have over 700 Barbie dolls and accessories. The originals from the 50s have red hair and polka dotted swimsuits. No blonde hair or pink dresses :)
- PeterStuer 3y agoI should have said iconic, not og ;)
- hiAndrewQuinn 3y agoStereotype accuracy is one of the largest and most replicable effects in all of social psychology. https://psycnet.apa.org/record/2015-19097-002 https://psycnet.apa.org/record/2015-19097-002
- deleted 3y ago[deleted]
- tomxor 3y agoIsn't this mainly an issue of garbage in garbage out? Most of the world's recorded images are not average or representative. People take and share images very selectively. As far as the model is concerned, what it produced probably is representative (representative of the training data). On the other hand, if a generative model was trained exclusively on new and unfiltered images of a journey through the sights of a country - not a tourist's sights - but non-selective sights, a journey no human would bother taking. Not only would it have a fighting chance of generating something beyond a stereotype for the given prompts, but we might also learn something from it.
- none_to_remain 3y agoFor comparison here [i] is the first screen of my Google image search results for "Nigerian office". There is one image of a man at the immigration office in non-Western attire. There is one image of a specific Nigerian gov't office with Nigerian flags. For the rest, how I am supposed to tell "Nigerian office" from "Ghanaian office" or "American office with mostly black employees"? Many of the office pictures are without people. But people are gonna want generated images that scream "Nigerian" when they say "Nigerian". [i] https://imgur.com/a/armJWI4 https://imgur.com/a/armJWI4
- AltruisticGapHN 3y agoWell this shouldn't be surprising. This is a big issue with AI since it doesn't actually come up with any new thoughts or reasoning - but essentially "remixes" its pool of data - you have a system where everything becomes "oversaturated" over time, kinda like compressing a jpeg over and over and over. It seems to me AI generated images are accelerating the "manufacture of glamour", as pointed by John Berger. We are already surrounded by images on a daily basis, and AI is accelerating the production of these "alternate ways of life". John Berger / Ways of Seeing , Episode 4 (1972) https://www.youtube.com/watch?v=5jTUebm73IY https://www.youtube.com/watch?v=5jTUebm73IY
- bambax 3y ago> Bias occurs in many algorithms and AI systems (...) In an analysis of more than 5,000 AI images, Bloomberg found that images associated with higher-paying job titles featured people with lighter skin tones, and that results for most professional roles were male-dominated. The use of the term "bias" here is disputable IMHO. What these systems describe is reality. We should aim to change the world, not the resulting -- faithful -- image of that world in AI. Cure the disease, not the symptoms.
- notahacker 3y agoSure, curing the disease is more important than curing the symptoms, though the two aren't entirely unlinked. What the systems describe isn't reality though. Mexicans invariably wearing sombreros doesn't reflect Mexican fashion, it reflects whether people have bothered to tag the image with "Mexican" or not. If you can tag reality in ways in which US frat boys' fancy dress preferences are somewhat representative of the label "Mexican" and famous Mexicans in Mexico City usually aren't, then it certainly isn't necessary for job title tags to be highly correlated with ethnicity (Posed stock photos have tended to push back against this for years). And whilst it's true that certain occupations are dominated by white males in the West, they're certainly not the world's "default" people; that's more a reflection of the sort of English speaking internet power users whose content gets hoovered up by the dataset. And that is definitely a bias, even if it's a completely unintentional one. In general it's "reality as seen through the narrow lens of people uploading and tagging photos, often not even with the intention of conveying useful information to an image generation algorithm". That reality includes a lot of biases, some of them more accurate than others and some of them more benign than others.
- bambax 3y agoMy comment above wasn't about Mexicans (that's another comment) but about whether describing people with a high-paying job as having a light skin tone is "biased" or a reflection of reality. Of course as you say, the problem (if there is one) is in the dataset and not in the program. But if we consider this should be corrected after the fact, then at that moment we are sure to introduce an actual bias. On what basis? Who decides what bias should be applied, and the appropriate amount?
- PredictorX1 3y ago"How AI reduces the world to stereotypes" I find this interesting, in that there are any number of A.I. systems other than deep learning and large language models. Contemporary usage in the nontechnical press, though, uses "A.I." to refer specifically to DL and LLM, especially when they are generative. From this perspective, the above title uses a stereotype which ignores other A.I. technologies.
- joe__f 3y agoLLMs produce output based on stuff that humans wrote, and humans reduce the world to stereotypes often. So why should this be surprising?
- HayBale 3y agoReally interesting article. These models left unchecked like they are now could be really dangerous. Increased use in articles will results propagating harmful stereotypes(unconsciously as Midjourney is easier to use than browsing the stock images), the enforcement of western(Anglo-Saxon) viewpoints in other countries. Also it's just simplifying life to the easiest, most basic common denominator. I honestly think that there is no added value for them to exists.
- LudwigNagasena 3y agoPeople left unchecked are really dangerous. The models are fine, they can’t harm you.
- Our_Benefactors 3y agoYou are the dangerous one, because you want to gatekeep this technology.
- fastball 3y agoThat's not AI, that's just Midjourney, which is highly biased to create the most "aesthetic" version of a prompt with a reasonably high level of determinism (compared to other models). Here[1] is what DALLE-3 gave me when I asked for "an Indian person". [1] https://supernotes-resources.s3.amazonaws.com/image-uploads/49dadbaf-1c7f-481c-9bd0-a438fff27571--Screenshot%25202023-10-21%2520at%25207.18.22%25E2%2580%25AFPM.png https://supernotes-resources.s3.amazonaws.com/image-uploads/...
- bhickey 3y agoI'm skeptical of their methodology. The images they're showing are very similar to one another. All the pictures of Delhi are essentially clones. They're getting a picture of old man for "an Indian person" then jamming the same prompt again.
- nickdothutton 3y agoWithout this sort of nonsense sucking up thought and debate we could have had a colony on Mars by now.
- cesaref 3y agoI think a point of reference would be to try the same prompts on a stock image library, and see what you get by comparison. Taking the 'indian person' prompt on pexels for example gives: https://www.pexels.com/search/indian%20person/ https://www.pexels.com/search/indian%20person/ I see men, women, children, weddings, parties, offices, bedrooms, streets. It's quite diverse. I'll also be a stereotype of a sort, but it's clearly wider and more representative of an aspirational indian scene.
- rsynnott 3y agoThis is possibly evidence that artists don’t have _that_ much to worry about, at least for now. Written output in particular tends to resemble the worst trope-driven self-published stuff you can find on Kindle Unlimited.
- redox99 3y agoIf I google images search "a mexican person", 18/23 are wearing sombreros. If the dataset used to train the model also looks like that, then obviously the trained model will give you someone wearing a sombrero.
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- tzm 3y agoAre stereotypes inherently negative or is it in the eye of the beholder? Seems like normalization could also have negative impact.
- dartos 3y agoThey’re human made approximations of other humans. Not inherently negative, but when they, it can quickly lead to violence and prejudice. See: US history every time there’s a new wave of immigrants.
- SilverBirch 3y agoI think there's two things here that are interesting. First, if you ask me "Describe an Indian Person" I'm going to... not do that? Like straight out the gate 50% of Indians are female, 50% are male, so the first choice I'm going to make in order to do that is to discount 50% of Indians. And the more I narrow it down the less representative it will be. So I wouldn't. I could describe broad cultural and ethnic attributes but even they are pretty useless. So yes, you're asking a question that a human can't answer without stereotyping and getting angry when the computer returns stereotypes. What do you want? A RNG that picks an image of 1 of the 1 billion Indians and returns an accurate description of them? Is that useful? Was the original question even useful - other than to provide the image that the person asking the question was probably expecting. The second thing, and I think this is more interesting though, is that we all have bias. And that's fine, we have social norms and cues and processes and culture to mediate that. We don't expect 1 person to be making important decisions based on gut instict. If it's important we have a process for deciding how to handle decision making. The risk is that by handing over decision making to AI you're just massively empowering something that is as biased as anyone. If you treat AI as just one more tool in the toolkit of decision making it's probably fine. The problem comes when people who don't understand AI put too much trust in it. It'd be like people relying on lie detectors to sentence someone to death (don't @ me), if you knew how lie detectors worked you... just wouldn't put that much trust in them. In the same way, the reason to highlight these biases is to say "This is a tool, it has limitations, don't blindly follow what it says". I take it back: there's a third thing that's intresting. Maybe these AI are... shallow. You ask for a picture of Indian Cuisine. Yes, you can get 1000 images, but they are a variation on 1 idea. If you asked a human they wouldn't give you the same dish laid out 5 ways or with 5 different garnishes, they'd give you 5 different dishes. So maybe part of this is really pointing to the fact this AI is still very shallow in it's observation of the world.
- hn_acker 3y ago> First, if you ask me "Describe an Indian Person" I'm going to... not do that? Like straight out the gate 50% of Indians are female, 50% are male, so the first choice I'm going to make in order to do that is to discount 50% of Indians. And the more I narrow it down the less representative it will be. So I wouldn't. The thing is, the prompt is not "[give me an image of] an Indian person" but "[Give me multiple images of] an Indian person". If I generate 100 images from the prompt "an Indian person", I would expect those 100 images to include a few tens of men and a few tens of women. I would expect some of the people in the images to have lighter skin and others to have darker skin. I would expect some of the people to wear X kind of clothing and others to wear other kinds of clothing. (I would also expect the images to have different lightings, but I digress.) I don't have to be familiar with many real Indian people to expect that I would get different-looking images. Even if an image generator is going to tend to show stereotypes, different images could contain different subsets of stereotypes.
- RugnirViking 3y agostereotypes aren't inherently bad, they're just ways of reducing the complexity of the world. For someone who has never been to mexico, never met a mexican, never thought about the topic deeply, that is what a mexican is like. There may be some people upset by that, wanting to show that they are more than just the stereotype which they personally don't like. The only way to get further is to introduce more nuance. The way I see that here is to ask for a "mexican buisnessman" or "mexican lumberjack" or whatever. If those pictures had sombreros then maybe id agree it was a problem but right now this is the most shallow and surface level interaction with the technology possible, and the article presents it with such gravity as though it was some great hidden injustice.
- RecycledEle 3y agoThe current generation of AIs are Internet simulators. Imagine watching as many people search the Internet for ____ then watching which pics they click on. That is what our current generation of AIs do. If you watch many people ask the Internet for a picture of a Nigerian person, then see what pics they like, you get a stereotype of a person from Nigeria. That is what the AIs do. I think those who are unhappy with this state of affairs disagree with our society more than they disagree with anything else. I wonder how many people they got mad at over pronouns in the last year.
- zaptheimpaler 3y agoAt first I thought this is a real problem, but the more I think about it, the more it's one of those "I asked an AI how to be evil and it told me!!!!" situations. The AI has to return something when given a vague prompt like that, and it is also specifically tuned to try to return similar things for the same prompt. It would be much less useful if it wasn't consistent because you wouldn't be able to gradually tune a prompt to get the image you want. So then their ask reduces to make the AI return a specifically not-stereotypical image of the race even though all that's specified in the prompt is the race. That could be done but doesn't seem much better. Maybe we should just expose the temperature control on these models and rename it to "diversity"..
- hn_acker 3y ago> So then their ask reduces to make the AI return a specifically not-stereotypical image of the race even though all that's specified in the prompt is the race. That could be done but doesn't seem much better. I'm guessing that what the researchers were hoping to see was less "a specifically not-stereotypical image of the race" and more like "across many generated images, some people showing stereotypes, some people showing other stereotypes, some people showing none of those stereotypes (but possibly though not necessarily yet other stereotypes)". Diversity isn't in "one image of one person" but can be in "multiple images of one person each", and I think that the researchers are aware of that.
- zaptheimpaler 3y agoI addressed that in my comment. Having a relatively stable output for the same input is a core requirement of the models atleast with the default settings.
- graypegg 3y agoThere’s an interesting point to dig out of this I think: the average of any one cultural identity is pretty inauthentic and because ML is pitched to the public as a massive efficiency boost, we’re going to see a lot of output from simple prompts. Not needing to “program” a prompt or over-think your query is the selling point. “Just type what you want”. Yeah, that means we’re going to see a lot of the same averaged-out caricatures. Your local Italian restaurant will select one of the first 3 options for “Italian pizza chef” for their menu. IMO, I think the author is trying to communicate that, but attributed blame to the AI tools because there’s other very clear cases of biased training data. (They even mentioned issues with facial recognition and black skin tones) Human laziness (or actually, using a technology as it’s pitched) is the main factor here I think. The AI dutifully turns your non specific query into a non specific result. Messing around with prompts about Nigerian tribes myself returns pretty diverse results.
- isitmadeofglass 3y ago[dead]
- rldjbpin 3y agojust like the saying "you are what you eat", the models are as good as the data they're trained on. to combat this, either you introduce randomness/noise intentionally at the cost of the results, or you work on enriching the data to be more inclusive.
- tribulator 3y agoHow exactly is the AI supposed to show diversity when your prompts have exactly zero diversity?