Bias In Facial Recognition Varies By Country, NIST Report Shows
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.
Executive *******
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Training **** *****
**** ********* ** ***** ** ****** training ****:
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****, ***** ** *** **** ********* regions:
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** ********, ***-***** ****** *** ***** demographic *********** ** *** ********** **** are ******** ******* *** **** ********* origin **** *************. ***** ** ********* may ******* ** ********** ** *** test ***********, ** *** ************ ** the *********** ** ** **** **.
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***** *** ***** *** ********** *** well ***, ** *** *** ******* the ******** **** **** ** **** to ********* ***** *** ******. **** means **** ***** ** *** **** educated ******* ***** ** *** *****'* findings, ** ****** ************ ***** ******** as ****.
****, **** *****:
*** *** *** * ************* *********** for ***** ***** ***** *** *** few ********** [****] **** ********* ** developers [** ***** *** **********].
*******, ** ** ***** ******** **** every ***** ***** **** **** **** that *** *** ******* * ****** percentage ** ***** ***** **** ****** than ***-***** ***** ***** ***. ** is **** ** **** ** ******* data **** ** ***** **** *** large ****** ** ****** ****** *********** neural ********, ** **** ***** *** have ****** ** *** **** ****** accessible ********.
*** *** ** *** **** ******** may ******* *** **** ***** **** clustered ******** *** *** **** *** often **** *******, **** **** ********** performing ****** ** *** **** ******** and ****** ******.
Poll / ****
** **** ******* ****** *** *** NIST ****** *** *** **** ** training ****. ** *** ** ******** increasingly ******* ** ******* ********, **** is ***** ****** ***** **** ** be **** *** **** ********. ******* a ****** **** **** ******** **** and ******* * **** ** **** is *** ***** ** **** ****** sets. **** ****, ** ** *******, be ** *********** ***** ** ****** will ** *** **** *** ****** to ***** **** ****** *********** *** matching **** ******* * *** ******** block ** ******** *** **** *** extent ** ***** **** ****** ******** sets. *** *********** ****** ** **** scenario *** *** ******* ************** ****** amassing * ***** **** *** ** people's *****. **** ***** ** *** question **, ** **** ****** ***** a ********** ******** *** ** ******* that ***** ******* *** ********** ** performance *** ****.
**** ***** ** *** ******** **, to **** ****** ***** * ********* training *** ** ******* **** ***** address *** ********** ** *********** *** bias.
***** ***** *****, * *** *** a ********* **** ** ************* *** all ********** ************. **** ***** *** only ***** ** ** *** *** AI ** **** *** ******* ****** and ******* ******** *** ****** ** ethical *********** ** *** ********** ***** used.
* ***** ** ******* (****** *****, Jamie ***********, ****** *********, ******** ******* Vaughan, ***** *******, *** ****é ***, Kate ********) ***** ** *********** ******* proposing ********* ***** ***** ***** ********,********** *** ********. **** ***** **** *** ******** should **** **** ********** *** ***** use *** *********** ***** *** *** data *** ********* *** **** ** contains.
** ***** ** *********** ** *** algorithms *** * "**** **** ***** dataset" ***** *** ****** ********** *********** about **** ****.
***, *********, ********* **** ** ** interesting ****, *** ** ***** **** lead ** ********* *********** ** ******** because * ******* ******** ********* **** invariably **** *** ********* **** ***** on **** ****. *******, ** ***** definitely ***** ******* ******.
** ****, ***** *********. * **** the ****, *** **** ******. ***** is * *** ***** ** ***** and ****** *** ** *** *****. My ***** ** ***** **** ** a *** **** ** **** ** this *****. ******* **** ** ******** to ******* **, *** **** * human *********** ***'* **** **** ***** to *** ****. ***** ** **** interesting ******* **** ** **-**************, *** other ******* ******* *******.
***** *** *** * *****. ** is * **** ********* ***** *** only ******* **** ********.
****, **** ***!
***** *** **** ****** *** **** 7 ****** ***, ****, **** ****, is ******* ** **** ******** ** facial *********** **********. *** *********** *** topics ** *****, ****** *****.