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> The post you are replying to has three clear bullet points which indicate where the problem lies. No, it does not. All it does is point possible explanations

> The post you are replying to has three clear bullet points which indicate where the problem lies. No, it does not. All it does is point possible explanations of why reality is the way it is. Yet, reality is still reality. If models are expected to predict reality then their output will match what we observe in reality. If someone expects predictive models to not predict reality and instead output results that do not match reality and instead comply with someone's personal ideals then that's an entirely different problem: predicting someone's personal goals instead of reality.

ncallaway 7y ago
The third bullet point of the above question (which you still haven't addressed) directly addresses this. The problem is not that the model outputs reality. The problem is that the model _incorrectly_ produces not reality, based on facts it _did_ learn from reality. That is, there is a fact pattern in reality, but the model draws an illogical conclusion based on that fact pattern. So, I'll ask again. Could you directly address this point from the parent post: "A recruiting agency uses a predictive model to recruit computer programmers. Due to the way the model was trained, it excludes qualified women (not OK, a clear misapplication of an algorithm)"
- thereisnospork 7y ago
  > "A recruiting agency uses a predictive model to recruit computer programmers. Due to the way the model was trained, it excludes qualified women (not OK, a clear misapplication of an algorithm)" Philosophically wrong or not, perhaps excluding those hypothetical women from consideration is the quote unquote optimal approach to allocate recruiting resources. Why this is the case is irrelevant; the onus is on society to 'shape up' and remove the reasons the group 'women'[1] are disqualifying themselves from the fair model. Of course assuming good faith in model design, training, implementation, etc. [1] Ditto for men as kindergarten teachers - employment (e.g.) being a zero(ish) sum game, so long as more women are teachers, some other profession will contain an outsized amount of men (and vice versa). There are reasons[2] why more women are kindergarten teachers than men, and any fair algorithm and effective algorithm would be expected to make prediction on these reasons or proxies thereof. [2] Or there would be an even gender ratio.

ncallaway 7y ago

The third bullet point of the above question (which you still haven't addressed) directly addresses this. The problem is not that the model outputs reality. The problem is that the model _incorrectly_ produces not reality, based on facts it _did_ learn from reality. That is, there is a fact pattern in reality, but the model draws an illogical conclusion based on that fact pattern. So, I'll ask again. Could you directly address this point from the parent post: "A recruiting agency uses a predictive model to recruit computer programmers. Due to the way the model was trained, it excludes qualified women (not OK, a clear misapplication of an algorithm)"

thereisnospork 7y ago
> "A recruiting agency uses a predictive model to recruit computer programmers. Due to the way the model was trained, it excludes qualified women (not OK, a clear misapplication of an algorithm)" Philosophically wrong or not, perhaps excluding those hypothetical women from consideration is the quote unquote optimal approach to allocate recruiting resources. Why this is the case is irrelevant; the onus is on society to 'shape up' and remove the reasons the group 'women'[1] are disqualifying themselves from the fair model. Of course assuming good faith in model design, training, implementation, etc. [1] Ditto for men as kindergarten teachers - employment (e.g.) being a zero(ish) sum game, so long as more women are teachers, some other profession will contain an outsized amount of men (and vice versa). There are reasons[2] why more women are kindergarten teachers than men, and any fair algorithm and effective algorithm would be expected to make prediction on these reasons or proxies thereof. [2] Or there would be an even gender ratio.