5 ms·

Perhaps Google don't want to be in the news for identifying dark-skinned people as monkeys again?

jjeaff 5y ago
I can't remember which company it was that launched a camera with face identification features, but that didn't recognize any face that wasn't lilly white like every single engineer that worked at that company. They could have probably benefited from a diversity and inclusion review. Heck, employing a single brown engineer or even QA engineer probably would have been enough to notice that before launch.
- alasdair_ 5y ago
  Microsoft Kinect also had this issue. I know the one black engineer (that had little to do with the project) that repeatedly got pulled in to see if the test system worked with non-white people. He left soon after.
  - shadowgovt 5y ago
    I wonder if the company failed to give him credit, responsibility, or compensation commensurate with his value to the project?
- saltminer 5y ago
  > I can't remember which company it was that launched a camera with face identification features, but that didn't recognize any face that wasn't lilly white like every single engineer that worked at that company It was HP https://www.youtube.com/watch?v=t4DT3tQqgRM https://www.youtube.com/watch?v=t4DT3tQqgRM
- NaturalPhallacy 5y ago
  People jump to the racism conclusion here, but it's really just a contrast issue. Detecting eyes, for example is simply easier with lighter skin. Light skinned black people read just fine, and super tanned white people are harder to read. It's literally contrast (light) detection, not racism. But because the media keeps everybody primed for racism to stave off the necessary class power rebalancing, everyone jumps to racism.
  - alasdair_ 5y ago
    It’s only a “contrast issue” if the people building the system failed to have roughly half of humanity represented in any meaningful way on their dev team.
    - erik_seaberg 5y ago
      They need to test on a realistic sample of users. Testing on the dev team is just lazy; they probably have unusually new and expensive hardware in well-lit offices.
    - throwaway45644 5y ago
      There is really no reason why the dev team should include any particular demographic: how are you supposed to have 90 years old people in the team to make sure they are recognized correctly? This is a requirements issue which directly impacts validation/test data collection. If their user base has 50% black people any reasonable protocol will include enough black faces int he test data to detect the problem early on. Ml based systems will always make errors, which errors matter will be defined by market/legal/mission requirements. It may very well be that faces of black people are harder to detect (especially in backlit situations). Should you hold the product because it may not work for everybody? It’s a complex decision. Maybe you can just have a good “face detection failed” flow to handle all the errors (think not only black people but also, tattooed people, etc.). Arguing that having quotas of that or the other in the dev team will make them more sensitive to diversity issues in general is also unnecessary because everybody is part of some minority in some situation, hence a minimum of education will make anybody understand first hand the value of inclusiveness and diversity. Btw, the team is using only their faces to test the system they won’t go far.. (think about lighting condition / different environments).
      - kelseyfrog 5y ago
        > Ml based systems will always make errors Sure, but error should be randomly distributed. This is stats 101. Any decent ML practitioner will check for this before releasing a model.

I can't remember which company it was that launched a camera with face identification features, but that didn't recognize any face that wasn't lilly white like every single engineer that worked at that company. They could have probably benefited from a diversity and inclusion review. Heck, employing a single brown engineer or even QA engineer probably would have been enough to notice that before launch.

alasdair_ 5y ago
Microsoft Kinect also had this issue. I know the one black engineer (that had little to do with the project) that repeatedly got pulled in to see if the test system worked with non-white people. He left soon after.
- shadowgovt 5y ago
  I wonder if the company failed to give him credit, responsibility, or compensation commensurate with his value to the project?
saltminer 5y ago
> I can't remember which company it was that launched a camera with face identification features, but that didn't recognize any face that wasn't lilly white like every single engineer that worked at that company It was HP https://www.youtube.com/watch?v=t4DT3tQqgRM https://www.youtube.com/watch?v=t4DT3tQqgRM
NaturalPhallacy 5y ago
People jump to the racism conclusion here, but it's really just a contrast issue. Detecting eyes, for example is simply easier with lighter skin. Light skinned black people read just fine, and super tanned white people are harder to read. It's literally contrast (light) detection, not racism. But because the media keeps everybody primed for racism to stave off the necessary class power rebalancing, everyone jumps to racism.
- alasdair_ 5y ago
  It’s only a “contrast issue” if the people building the system failed to have roughly half of humanity represented in any meaningful way on their dev team.
  - erik_seaberg 5y ago
    They need to test on a realistic sample of users. Testing on the dev team is just lazy; they probably have unusually new and expensive hardware in well-lit offices.
  - throwaway45644 5y ago
    There is really no reason why the dev team should include any particular demographic: how are you supposed to have 90 years old people in the team to make sure they are recognized correctly? This is a requirements issue which directly impacts validation/test data collection. If their user base has 50% black people any reasonable protocol will include enough black faces int he test data to detect the problem early on. Ml based systems will always make errors, which errors matter will be defined by market/legal/mission requirements. It may very well be that faces of black people are harder to detect (especially in backlit situations). Should you hold the product because it may not work for everybody? It’s a complex decision. Maybe you can just have a good “face detection failed” flow to handle all the errors (think not only black people but also, tattooed people, etc.). Arguing that having quotas of that or the other in the dev team will make them more sensitive to diversity issues in general is also unnecessary because everybody is part of some minority in some situation, hence a minimum of education will make anybody understand first hand the value of inclusiveness and diversity. Btw, the team is using only their faces to test the system they won’t go far.. (think about lighting condition / different environments).
    - kelseyfrog 5y ago
      > Ml based systems will always make errors Sure, but error should be randomly distributed. This is stats 101. Any decent ML practitioner will check for this before releasing a model.