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Thank you for adding perspective to this issue with police and drawing it out of exclusively-race territory. I would suggest that perhaps, part of what's happe

Thank you for adding perspective to this issue with police and drawing it out of exclusively-race territory. I would suggest that perhaps, part of what's happening is that the police are optimizing for a particular metric. We can hope that it's crime reduction, or some number or numbers very close to that. When you optimize for one metric, other metrics fall. The police, like most anyone, make errors based on superficial attributes. Even if statistically, arresting tall hobo beard man taking pictures of trees late at night is the best course of action for the police's metrics, you perceive it as a series of stupid errors. This leads to a perception of injustice and harassment. I'm the kind of person who doesn't mind security check ups and even being stopped by police as long as they are cordial and professional, and, crucially, I understand their reasoning. I recall listening to one black man, I think he was talking to Sam Harris, relating how police would stop black men and grab them. Unprofessional treatment creates a perception of an uncivilized society, and that's unjust.

yosito 8y ago
> When you optimize for one metric, other metrics fall. This is very true. People often assume that things like machine learning or AI won't have racial biases, but then we find out time and time again that our algorithms are in fact biased. It's not that the bias is intentional (even human bias is rarely intentional), it's that by focusing on metrics, the metric of racial equality often suffers. I'm not sure why you were down voted for pointing this out. This is a key concept to understand if we care about creating a more equal society in an age of big data and machine learning. Google did some research in this area: https://research.google.com/bigpicture/attacking-discrimination-in-ml/ https://research.google.com/bigpicture/attacking-discriminat... The TL;DR version is that in order to eliminate bias, you have to be aware of biases as metrics and optimize to eliminate them.
- naasking 8y ago
  Interesting way to frame it. If I could restate in slightly more colloquial terms, the police's goal is typically something like "to protect and serve", where "serve" is serving the public by upholding public ideals or principles. Racial profiling might satisfy the "protect" clause, but it fails the "serve" clause, because it fails the portion of the public that is profiled.

yosito 8y ago

> When you optimize for one metric, other metrics fall. This is very true. People often assume that things like machine learning or AI won't have racial biases, but then we find out time and time again that our algorithms are in fact biased. It's not that the bias is intentional (even human bias is rarely intentional), it's that by focusing on metrics, the metric of racial equality often suffers. I'm not sure why you were down voted for pointing this out. This is a key concept to understand if we care about creating a more equal society in an age of big data and machine learning. Google did some research in this area: https://research.google.com/bigpicture/attacking-discrimination-in-ml/ https://research.google.com/bigpicture/attacking-discriminat... The TL;DR version is that in order to eliminate bias, you have to be aware of biases as metrics and optimize to eliminate them.

naasking 8y ago
Interesting way to frame it. If I could restate in slightly more colloquial terms, the police's goal is typically something like "to protect and serve", where "serve" is serving the public by upholding public ideals or principles. Racial profiling might satisfy the "protect" clause, but it fails the "serve" clause, because it fails the portion of the public that is profiled.