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Corrected headline: "How safe would humans say this street looks?" Not the same question at all. While the research into computer vision and machine learning

by jleader 12y ago

Corrected headline: "How safe would humans say this street looks?" Not the same question at all. While the research into computer vision and machine learning is interesting, as a human I'd like to know when my intuition about safety is wrong, not just have a machine that can mimic my intuition.

HillRat 12y ago
Unfortunately, once you place the imprimatur of machine learning upon it, people will tend to conflate "perceived safety" with "actual safety." At the extreme, it becomes a way to create an apparent but false confirmation of the prejudices of the crowd. This is one of those rare cases in computer science research where there is an actual ethical component to the work being done; while machine scoring landscapes according to their relative attractiveness is fairly harmless, scoring them according to "perceived safety" without mentioning the obvious limitations thereof is heading towards trouble. As I recall, there was a crowdsourced app last year that supposedly would inform its users if they were in "bad" neighborhoods (using the opinions of other users to tag areas), and it received a wave of criticism for essentially similar reasons.
- duderific 12y ago
  If it was crowdsourced, why would you need an app to know if you were in a bad neighborhood? Your perception should be roughly similar to those who had contributed their opinions as crowd source data. Kind of like needing an app to tell you it's raining when you're standing in a downpour.
  - notahacker 12y ago
    The act of using the app can increase that perception though; if you get mugged whilst checking your smartphone in the middle of the street it's a pretty good indicator the area isn't safe :-)
notahacker 12y ago
"Would humans say" is also subtly different from "do humans actually think". The Williamsburg bridge in NY has a particularly odd pattern where pictures showing steelwork have particularly "safe" ratings and pictures showing river panoramas have extremely low ratings. Maybe HNers other than me have an idea of why that might be, but I find it hard to believe the steelwork is incredibly reassuring and the view of Manhattan behind utterly terrifying as the polarized figures would have you believe
PeterisP 12y ago
If you can automatically get data "how safe it looks" for all addresses, then it's a way to actually measure the [anti-]correlations with actual crime happening there. We had the actual crime data already, and now we could compare it with the perception to try and identify the reasons for mismatches. Not that identifying reasons is simple, though - with machine learning it's far simpler to get a system that makes good decisions than to get an understanding on why it makes those particular decisions.

by jleader 12y ago

HillRat 12y ago

Unfortunately, once you place the imprimatur of machine learning upon it, people will tend to conflate "perceived safety" with "actual safety." At the extreme, it becomes a way to create an apparent but false confirmation of the prejudices of the crowd. This is one of those rare cases in computer science research where there is an actual ethical component to the work being done; while machine scoring landscapes according to their relative attractiveness is fairly harmless, scoring them according to "perceived safety" without mentioning the obvious limitations thereof is heading towards trouble. As I recall, there was a crowdsourced app last year that supposedly would inform its users if they were in "bad" neighborhoods (using the opinions of other users to tag areas), and it received a wave of criticism for essentially similar reasons.

duderific 12y ago
If it was crowdsourced, why would you need an app to know if you were in a bad neighborhood? Your perception should be roughly similar to those who had contributed their opinions as crowd source data. Kind of like needing an app to tell you it's raining when you're standing in a downpour.
- notahacker 12y ago
  The act of using the app can increase that perception though; if you get mugged whilst checking your smartphone in the middle of the street it's a pretty good indicator the area isn't safe :-)

notahacker 12y ago

"Would humans say" is also subtly different from "do humans actually think". The Williamsburg bridge in NY has a particularly odd pattern where pictures showing steelwork have particularly "safe" ratings and pictures showing river panoramas have extremely low ratings. Maybe HNers other than me have an idea of why that might be, but I find it hard to believe the steelwork is incredibly reassuring and the view of Manhattan behind utterly terrifying as the polarized figures would have you believe

PeterisP 12y ago

If you can automatically get data "how safe it looks" for all addresses, then it's a way to actually measure the [anti-]correlations with actual crime happening there. We had the actual crime data already, and now we could compare it with the perception to try and identify the reasons for mismatches. Not that identifying reasons is simple, though - with machine learning it's far simpler to get a system that makes good decisions than to get an understanding on why it makes those particular decisions.