> (Is there some kind of universal law that you're citing, that claims that it's impossible to build a useful, unbiased system?)
Usually it'd be a trade-off, where you'd give up some quality at doing the main job to gain whatever the secondary objective would be, e.g. producing results according to whatever desired distribution.
That said, the bigger issue would be defining what "unbiased" means. There're some mutually-exclusive notions of what it'd take to be unbiased, so if "unbiased" requires not being biased according to any perspective, then, yeah, it wouldn't generally be possible.
For a quick example, say that we want to be unbiased in our representation of men-vs.-women in a profession. Then, is the proper ratio: (1) 50% men and 50% women; (2) population-weighted ratios (because men and women aren't generally equally represented in the population); (3) profession-weighted ratios (because men and women aren't generally equally represented in a profession); (4) observer-subjective ratio (this is, a realistic ratio for how often a viewer might actually see men-vs.-women of that profession); (5) something else? What about intersexed people -- should they be represented, and if so, at what ratio, and how should men/women be adjusted to account for them?
Then, what if the photo would be served by having a person of a certain height, to best fill the space while not covering up more -- then, should the algorithm be biased toward a man or a woman based on typical heights, to accurately represent sex-specific height-distributions? Or, should it deviate from that, and present men and women as having the same height-distribution? And if we do deviate from that to pretend that men and women have the same height-distribution, but that height-distribution more closely matches the actual height-distribution for one sex than the other, then is that itself a form of bias?
Then, same as above, but for weight. Then for body size.
Then, what if men and women have different fashions in the relevant culture, and one fashion would better fit the scene -- can that be a factor? And if so, what should be the logic for picking the relevant culture?
Then, how should interactions be handled? For example, if we're generating pictures of doctors who're also mothers, then should men be included in that, or is it okay to only have female doctors in that context? Or what if we're generating pictures of doctors at a conference for female doctors in specific but has some male attendees -- then what ratio would be correct?
Point being that, for someone designing algorithms that would be "unbiased", they'd presumably want to know what folks would accept as a valid solution to that objective. Without a clear definition of what's desired, it'd seem like any particular solution would be open to criticism from other perspectives.
Usually it'd be a trade-off, where you'd give up some quality at doing the main job to gain whatever the secondary objective would be, e.g. producing results according to whatever desired distribution.
That said, the bigger issue would be defining what "unbiased" means. There're some mutually-exclusive notions of what it'd take to be unbiased, so if "unbiased" requires not being biased according to any perspective, then, yeah, it wouldn't generally be possible.
For a quick example, say that we want to be unbiased in our representation of men-vs.-women in a profession. Then, is the proper ratio: (1) 50% men and 50% women; (2) population-weighted ratios (because men and women aren't generally equally represented in the population); (3) profession-weighted ratios (because men and women aren't generally equally represented in a profession); (4) observer-subjective ratio (this is, a realistic ratio for how often a viewer might actually see men-vs.-women of that profession); (5) something else? What about intersexed people -- should they be represented, and if so, at what ratio, and how should men/women be adjusted to account for them?
Then, what if the photo would be served by having a person of a certain height, to best fill the space while not covering up more -- then, should the algorithm be biased toward a man or a woman based on typical heights, to accurately represent sex-specific height-distributions? Or, should it deviate from that, and present men and women as having the same height-distribution? And if we do deviate from that to pretend that men and women have the same height-distribution, but that height-distribution more closely matches the actual height-distribution for one sex than the other, then is that itself a form of bias?
Then, same as above, but for weight. Then for body size.
Then, what if men and women have different fashions in the relevant culture, and one fashion would better fit the scene -- can that be a factor? And if so, what should be the logic for picking the relevant culture?
Then, how should interactions be handled? For example, if we're generating pictures of doctors who're also mothers, then should men be included in that, or is it okay to only have female doctors in that context? Or what if we're generating pictures of doctors at a conference for female doctors in specific but has some male attendees -- then what ratio would be correct?
Point being that, for someone designing algorithms that would be "unbiased", they'd presumably want to know what folks would accept as a valid solution to that objective. Without a clear definition of what's desired, it'd seem like any particular solution would be open to criticism from other perspectives.