zlacker

[return to "Facial Recognition Leads To False Arrest Of Black Man In Detroit"]
1. danso+02[view] [source] 2020-06-24 14:55:32
>>vermon+(OP)
This story is really alarming because as described, the police ran a face recognition tool based on a frame of grainy security footage and got a positive hit. Does this tool give any indication of a confidence value? Does it return a list (sorted by confidence) of possible suspects, or any other kind of feedback that would indicate even to a layperson how much uncertainty there is?

The issue of face recognition algorithms performing worse on dark faces is a major problem. But the other side of it is: would police be more hesitant to act on such fuzzy evidence if the top match appeared to be a middle-class Caucasian (i.e. someone who is more likely to take legal recourse)?

◧◩
2. throwa+ed[view] [source] 2020-06-24 15:40:18
>>danso+02
> But the other side of it is: would police be more hesitant to act on such fuzzy evidence if the top match appeared to be a middle-class Caucasian (i.e. someone who is more likely to take legal recourse)?

Honest question: does race predict legal recourse when decoupled from socioeconomic status, or is this an assumption?

◧◩◪
3. advise+Ie[view] [source] 2020-06-24 15:46:36
>>throwa+ed
Race and socioeconomic status are deeply intertwined. Or to be more blunt - US society has kept black people poorer. To treat them as independent variables is to ignore the whole history of race in the US.
◧◩◪◨
4. throwa+dq[view] [source] 2020-06-24 16:28:25
>>advise+Ie
> To treat them as independent variables is to ignore the whole history of race in the US.

Presumably the coupling of the variables is not binary (dependent or independent) but variable (degrees of coupling). Presumably these variables were more tightly coupled in the past than in the present. Presumably it's useful to understand precisely how coupled these variables are today because it would drive our approach to addressing these disparities. E.g., if the variables are loosely coupled then bias-reducing programs would have a marginal impact on the disparities and the better investment would be social welfare programs (and the inverse is true if the variables are tightly coupled).

[go to top]