Bias In Facial Recognition Varies By Country, NIST Report Shows
By Zach Segal, Published Jul 15, 2020, 12:17pm EDT
(Info+)
While many argue that face recognition is inherently racist, results from one of the most extensive studies done on demographic bias in AI, the Facial Recognition Vendor Test (FRVT) Part 3 by the National Institute of Standards and Technolgy (NIST) analyzing over 100 algorithms, has shown that bias varied across the country of development.
In particular, they observed that several algorithms developed by China groups performed better on East Asian faces than Eastern European faces while the vast majority of algorithms performed Eastern European faces than East Asians.
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While the NIST report was from 7 months ago, IPVM, with Zach, is ramping up more coverage on facial recognition technology. Any suggestions for topics to cover, please share.
As your article points out the NIST report did not look at training data. As the FR industry increasingly depends on machine learning, what is being learnt would seem to be more and more critical. Perhaps a deeper dive into training sets and perhaps a look at what is out there as open source sets. What will, in my opinion, be an interesting trend to follow will be not only the extent to which open source acquisition and matching code becomes a key building block in products but also the extent to which open source training sets. One interesting glitch in this scenario are the privacy considerations around amassing a large data set of people's faces. This leads to the question of, to what extent could a synthethic training set be created that would address the challenges of performance and bias.
This leads to the question of, to what extent could a synthetic training set be created that would address the challenges of performance and bias.
Along these lines, I can see a universal type of accreditation for all artificial intelligence. This would not only apply to FR but all AI so that the general public and private entities can depend on ethical application of the algorithms being used.
It would be interesting if all algorithms had a "made from blank dataset" label and easily accessible information about that data.
And, Salvatore, synthetic data is an interesting idea, but it could also lead to increased homogeneity in datasets because a machine learning algorithm will invariably make the synthetic data based on real data. However, it could definitely solve privacy issues.
Hi Zach, great reference. I know the work, its very useful. There is a lot going on there and around the US and world. My guess is there will be a lot more to come on this topic. Solving bias is critical to ethical AI, and from a human perspective let's hope this comes to the fore. There is some interesting related work on de-identification, and other privacy related aspects.
Thank you and I agree. It is a very important topic and only growing more relevant.
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Comments (6)
John Honovich
Zach, good job!
While the NIST report was from 7 months ago, IPVM, with Zach, is ramping up more coverage on facial recognition technology. Any suggestions for topics to cover, please share.
Create New Topic
Salvatore D'Agostino
As your article points out the NIST report did not look at training data. As the FR industry increasingly depends on machine learning, what is being learnt would seem to be more and more critical. Perhaps a deeper dive into training sets and perhaps a look at what is out there as open source sets. What will, in my opinion, be an interesting trend to follow will be not only the extent to which open source acquisition and matching code becomes a key building block in products but also the extent to which open source training sets. One interesting glitch in this scenario are the privacy considerations around amassing a large data set of people's faces. This leads to the question of, to what extent could a synthethic training set be created that would address the challenges of performance and bias.
Create New Topic
Salvatore D'Agostino
Hi Zach, great reference. I know the work, its very useful. There is a lot going on there and around the US and world. My guess is there will be a lot more to come on this topic. Solving bias is critical to ethical AI, and from a human perspective let's hope this comes to the fore. There is some interesting related work on de-identification, and other privacy related aspects.
Create New Topic