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It depends on whether you'd like the model to learn casual or correlative relationships. If you want the model to understand what a "nurse" actually is, then i
by karpierz 4y ago
It depends on whether you'd like the model to learn casual or correlative relationships.
If you want the model to understand what a "nurse" actually is, then it shouldn't be associated with female.
If you want the model to understand how the word "nurse" is usually used, without regard for what a "nurse" actually is, then associating it with female is fine.
The issue with a correlative model is that it can easily be self-reinforcing.
- jdashg 4y agoAdditionally, if you optimize for most-likely-as-best, you will end up with the stereotypical result 100% of the time, instead of in proportional frequency to the statistics. Put another way, when we ask for an output optimized for "nursiness", is that not a request for some ur stereotypical nurse?
- jvalencia 4y agoYou could simply encode a score for how well the output matches the input. If 25% of trees in summer are brown, perhaps the output should also have 25% brown. The model scores itself on frequencies as well as correctness.
- spywaregorilla 4y agoSuppose 10% of people have green skin. And 90% of those people have broccoli hair. White people don't have broccoli hair. What percent of people should be rendered as white people with broccoli hair? What if you request green people. Or broccoli haired people. Or white broccoli haired people? Or broccoli haired nazis? It gets hard with these conditional probabilities
- astrange 4y agoThe only reason these models work is that we don’t interfere with them like that. Your description is closer to how the open source CLIP+GAN models did it - if you ask for “tree” it starts growing the picture towards treeness until it’s all averagely tree-y rather than being “a picture of a single tree”. It would be nice if asking for N samples got a diversity of traits you didn’t explicitly ask for. OpenAI seems to solve this by not letting you see it generate humans at all…
- ar_lan 4y agoYou could stipulate that it roll a die based on percentage results - if 70% of Americans are "white", then 70% of the time show a white person - 13% of the time the result should be black, etc. That's excessively simplified but wouldn't this drop the stereotype and better reflect reality?
- ghayes 4y agoIs this going to be hand-rolled? Do you change the prompt you pass to the network to reflect the desired outcomes?
- SnowHill9902 4y agoNo, because a user will see a particular image not the statistically ensemble. It will at times show an Eskimo without a hand because they do statistically exist. But the user definitely does not want that.
- LudwigNagasena 4y ago> If you want the model to understand how the word "nurse" is usually used, without regard for what a "nurse" actually is, then associating it with female is fine. That’s a distinction without a difference. Meaning is use.
- tines 4y agoNot really; the gender of a nurse is accidental, other properties are essential.
- codethief 4y agoWhile not essential, I wouldn't exactly call the gender "accidental": > We investigated sex differences in 473,260 adolescents’ aspirations to work in things-oriented (e.g., mechanic), people-oriented (e.g., nurse), and STEM (e.g., mathematician) careers across 80 countries and economic regions using the 2018 Programme for International Student Assessment (PISA). We analyzed student career aspirations in combination with student achievement in mathematics, reading, and science, as well as parental occupations and family wealth. In each country and region, more boys than girls aspired to a things-oriented or STEM occupation and more girls than boys to a people-oriented occupation. These sex differences were larger in countries with a higher level of women's empowerment. We explain this counter-intuitive finding through the indirect effect of wealth. Women's empowerment is associated with relatively high levels of national wealth and this wealth allows more students to aspire to occupations they are intrinsically interested in. Source: https://psyarxiv.com/zhvre/ https://psyarxiv.com/zhvre/ (HN discussion: https://news.ycombinator.com/item?id=29040132 https://news.ycombinator.com/item?id=29040132)
- bufbupa 4y agoAt the end of a day, if you ask for a nurse, should the model output a male or female by default? If the input text lacks context/nuance, then the model must have some bias to infer the user's intent. This holds true for any image it generates; not just the politically sensitive ones. For example, if I ask for a picture of a person, and don't get one with pink hair, is that a shortcoming of the model? I'd say that bias is only an issue if it's unable to respond to additional nuance in the input text. For example, if I ask for a "male nurse" it should be able to generate the less likely combination. Same with other races, hair colors, etc... Trying to generate a model that's "free of correlative relationships" is impossible because the model would never have the infinitely pedantic input text to describe the exact output image.
- karpierz 4y ago> At the end of a day, if you ask for a nurse, should the model output a male or female by default? Randomly pick one. > Trying to generate a model that's "free of correlative relationships" is impossible because the model would never have the infinitely pedantic input text to describe the exact output image. Sure, and you can never make a medical procedure 100% safe. Doesn't mean that you don't try to make them safer. You can trim the obvious low hanging fruit though.
- calvinmorrison 4y agowhat if I asked the model to show me a sunday school photograph of baptists in the National Baptist Convention?
- rvnx 4y agoThe pictures I got from a similar model when asking for a "sunday school photograph of baptists in the National Baptist Convention": https://ibb.co/sHGZwh7 https://ibb.co/sHGZwh7
- calvinmorrison 4y agoand how do we _feel_ about that outcome?
- sinenomine 4y ago> It depends on whether you'd like the model to learn casual or correlative relationships. I expect that in the practical limit of scale achievable, the regularization pressure inherent to the process of training these models converges to https://en.wikipedia.org/wiki/Minimum_description_length https://en.wikipedia.org/wiki/Minimum_description_length and the correlative relationships become optimized away, leaving mostly true causal relationships inherent to data-generating process.
- drdeca 4y agoThe meaning of the word "nurse" is determined by how the word "nurse" is used and understood. Perhaps what "nurse" means isn't what "nurse" should mean, but what people mean when they say "nurse" is what "nurse" means.