Wednesday, July 24, 2013

Discrimination = Overgeneralization


Why is it that we get slightly nervous when we see a man with a long beard clothed in a dress sitting next to us in the plane? We overgeneralize. We associate terrorism with Islam and Islam with bearded people in dresses. But clearly, the number of peaceful, well willing muslims far exceeds the number of muslim terrorists.

If tomorrow a horrific terrorist attack from white Caucasians would take place, we would not suddenly get nervous with every white Caucasian sitting next to us in our plane. Perhaps the somewhat obvious reason could be that while we tend to ignore all the peaceful muslims in our society, we do have one important counter example to the hypothesis that all Caucasians are terrorists: ourself. Any viable hypothesis should not implicate ourself, so you start looking for more subtle attributes: does the person look like a slob? Or perhaps do they grow long hair etc.?

Our tendency to overgeneralize directly causes us to discriminate. Studies reveal that we (white people) are more afraid to be mugged by black people than by white people, even though we think we do not think so ourselves! In machine learning we call this "underfitting". Our theories about the world are too simple, our prediction about the world are poor and we draw conclusions too fast.

What can we do about this? Firstly, being aware of these inborn tendencies can help, at least at a conscious level. But it's not enough. My recommendation: build appreciation of other cultures by organizing multi-cultural parties in your community. Experience many positive counter examples to counteract your prejudices. Don't let those unfounded hypotheses based on a single negative example cloud your judgement.

2 comments:

  1. It seems to me that whether or not you're overgeneralizing when you stereotype depends on what features you have available. If you are trying to hire someone based on their CV, you have a lot of relevant features besides race and gender, so if you only use the latter you are indeed underfitting.

    But if you are walking down a dark street trying to decide whether the person coming the other way poses a threat, you have only superficial features to go on. And if you conclude that they are "shady", the features that made you do so, e.g., visible tattoos, probably are correlated with the threat level.

    I think the problem with stereotyping is not that it necessarily overgeneralizes, because that's only sometimes the case. The problem is that even if the stereotype is correct, it is unfair to person being stereotyped. People with tattoos should be just as innocent until proven guilty as anyone else, regardless of the correlation between tattoos and criminal behavior.

    I once saw a talk by an engineer doing fraud detection for Apple. He said their machine learning algorithm wanted to flag all orders from a zip code in south central LA as suspicious. The problem is not that this is a bad rule (on the contrary, it's probably a great rule). The problem is that it's a deeply unfair rule.

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  2. I think things like this are really interesting. I absolutely love to find unique places like this. It really looks super creepy though!! Best Machine Learning institute in velachery | python machine learning course in velachery | Machine Learning course in chennai

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