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>"How do machine learning and statistical analysis tools distinguish between a causal relationship and mere correlation?" They have no reason to make such a di
by Banthum 10y ago
>"How do machine learning and statistical analysis tools distinguish between a causal relationship and mere correlation?"
They have no reason to make such a distinction.
If you're trying to look for subjects of with a particular property X, but X is hidden, but you know property Y correlates with X, the correct action is to choose subjects with property Y. The causative relationship (or lack thereof) between X and Y is irrelevant.
As a simple example, if you're hiring people to carry 80-pound bags of grain between trucks, you want the property of physical strength. But, if you only have resumes to go on, you can't observe physical strength directly. But, gender correlates with physical strength, and names correlate with gender, so you'd be rational to choose the resumes with recognizably male names.
The reason we avoid discriminating between various protected groups isn't because groups don't have characteristics. If that was the case, such discrimination would be pointless and people would not bother doing it because it would have a cost but no benefit. Discrimination based on hair color is like this. Nobody does it and we don't need campaigns to stop it.
In reality, groups of people do have characteristics - physical, psychological, emotional, intellectual, cultural. However, we avoid discriminating between protected groups because we believe it's wrong to subject an individual to that kind of discrimination, even if such discrimination would be rational for the discriminator. Such discrimination violates our western ideals of individual rights and equal individual opportunity.
- Silhouette 10y agoHowever, we avoid discriminating between protected groups because we believe it's wrong to subject an individual to that kind of discrimination, even if such discrimination would be rational for the discriminator. I don't think most of us would have a problem with discriminating on an inherent property of a group that is relevant to the decision being made. Indeed, often neither do anti-discrimination laws. Such discrimination can be objectively justified. For example, consider a job where a certain level of fitness is a functional requirement, say a firefighter whose role includes being able to carry someone out of a burning building. Setting a relatively high bar for physical strength is going to discriminate against female job applicants and those with various physical disabilities as their respective groups, but it's not discriminating against someone because they're female or in a wheelchair, it's discriminating against them because they can't carry someone out of a burning building and so that person is going to die. I don't think most of us would have a problem with this kind of rule. The only thing unfair here is life not making us all equal, and there isn't much we can do about that. The problems usually start when either you discriminate against a whole group based on some property that is somewhat correlated with membership of the group but not inherent, or you discriminate against a whole group based on a property that is inherent but isn't actually relevant to the decision being made. For example, if a job involves sitting at a desk and using a computer all day, a woman or someone in a wheelchair can presumably do that just as well as our hypothetical strong male. In this case, discrimination on the basis of gender or physical strength simply isn't relevant to the decision being made but sadly does sometimes happen because of individual prejudice, and thus we have laws to protect those in more vulnerable situations. Unfortunately, the kinds of tools and mathematical analyses we're talking about here don't necessarily see those situations any differently. They may learn the wrong lesson if there are coincidental, misleading correlations in whatever data is used to train them, which brings us back to where we came in.