Bias In Facial Recognition Varies By Country, NIST Report Shows

Published Jul 15, 2020 16:17 PM

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.

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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 *******

***** **** **% ** ******* ********* developed ********** **** ********* ****** ** Eastern ******** *** **** **** ***** ones (***** **-*** ***** ******), ~**% of ***** ********* ********* ********** **** performed ****** ** **** ***** *** than ******* ******** ****.

**** ****** ******* ** *** **** Asian *****, ******** *** ** ********, with ~**% ** ********** ********* ** East ***** ****** ********** ****** ** Eastern ***** ***** **** ********.

Training **** *****

**** ********* ** ***** ** ****** training ****:

**** ******** **** ******** ****, ** perhaps **** ***** ****** ********* ** the ***********, *** ** ********* ** reducing ********** ***** ******** *************. ****, the ******-**** ********** ***** ** *** developers ** *********** *** ******* ** more *******, ******** *******, ******** ****.

*******, **** *** *** ** ********** designed ** ********* ***** *** **** did *** **** ** ******** ****. And, **** ****** *** *** **** with ***** ***** ****** *********** ** South ***** *****, ** ******* ****** may ******* *** *******.

****, ***** ** *** **** ********* regions:

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Study ********

**** ****** **** *********** *********** ** facial *********** ********** ** ***** ****** facial *********** ****** ****, ********* ********** from **** *** ******. *** ****** of ***** ****, ***** ** **** focus **, **** ******** *** ********** in ***** ***** ***** (***) ** people ***** **** ****-******* ** ***** Department *********** ******** (***/*** *****-* **************). An ******* ** * ***** ***** would ** ** * ****** ******** your ****** ***** ***** ****, ******* of *****. ** **** ****, **** compared ***,*** **** *********** ****** **** 441,517 ********* *********** ******, ********* ****** who ******** ** * ****** ** countries ****** *** *** ********** ***********. The **** *********** **** **** ***** of *** ****** ** ***** ******* that ******** (*** ******* **** ***** because *** **** **** ********).

**** ***** *********** ** ***** ***** rates, **** ***** ***** ***** ****** in ******* ********* (******, *******, *** Russia) *** ******* **** **** ******** and **** ****** (*****, *****, *****, Philippines, ********, *** *******). *******, *** study ***** ****

**** * ****** ** ********** ********* in ***** **** ****** ** ********, with *** *****-******** ***** ** **** Asian *****.

******** **** ******** **** ** ********* nationality ***** ** ****** * *** of *** ****** *** ****** **** present ** **** ** *** ********** they ******.

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How ** ********* *********** ***********

***** **-*** ***** **** ******** *** E. ********* **** *. ******, ****** like * ***** **********, **** *** be **********. *** *********** ** ********** can ** **** (**** ********** *** error ***** ** ~.***** *** *. Asians *** ~.****** *** *. *********), and ** **** *****, *, ** close ** * ****** ***** ** observed. ** ** ********* ** **** for ************/*:* ********, **** ** ***** facial *********** ** ****** **** ******, mistakes ***** ** ********** ********** *** the ********* ***** ** ****** ************. However, ** **** *** **************/*:* *********, such ** ******** ** **** *** anyone **** ** *********** *******, *** error **** ********* ************* **** *, and *** ********* *** **** * huge ****** (**** **** **** ***) if * ** ***** ******.

**, ***** *** *********** *********** *** bad, *** ********** *** *** ** an ***** **** *:* ************ *** can ** * **** ***** (**** disparity ****** **** ***) ** **** for *:* **************.

Effect ** ********* ******

**** **% ** ***-****-***** **** ********** performed ****** (* ***** ***** ***** rate) ** ******* ******** ***** **** on **** ***** ****

***, ~**% ** ******* ***** (********* Dahua, *********, *** ******) *** ~**% of **** ***** ***** (******** ** include ********* **** ****** **** *** in **** **** *** *** ** the ***** ** ********* ****** *** the **** *****) ********* ****** ** East ***** ***** **** ******* ********, as *** ***** ***** **********:

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********

***** *** ******** ** ********** *********** bias ** * *******, *** **** that ********** ********* ** ******* *** East ***** ***** ****** * ********* bias **** **** ** ***** *****, implies **** **** ** *** *********** disparity ** **** *********** ******** *** be ****** ** *** *** ** diverse ******** ****. **** ***** **** developers ****** ****** **** **** *** diverse ******** ****, ******* ** **** relying ** **** ** ******* ** use. ** **** ***** **** ********* in ** ***** *** **** ** better **********.

** ********, ***-***** ****** *** ***** demographic *********** ** *** ********** **** are ******** ******* *** **** ********* origin **** *************. ***** ** ********* may ******* ** ********** ** *** test ***********, ** *** ************ ** the *********** ** ** **** **.

***********

***** *** ***** *** ********** *** 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 / ****

Comments (6)
JH
John Honovich
Jul 15, 2020
IPVM

****, **** ***!

***** *** **** ****** *** **** 7 ****** ***, ****, **** ****, is ******* ** **** ******** ** facial *********** **********. *** *********** *** topics ** *****, ****** *****.

(3)
Avatar
Salvatore D'Agostino
Jul 15, 2020
IDmachines

** **** ******* ****** *** *** NIST ****** *** *** **** ** training ****. ** *** ** ******** increasingly ******* ** ******* ********, **** is ***** ****** ***** **** ** be **** *** **** ********. ******* a ****** **** **** ******** **** and ******* * **** ** **** is *** ***** ** **** ****** sets. **** ****, ** ** *******, be ** *********** ***** ** ****** will ** *** **** *** ****** to ***** **** ****** *********** *** matching **** ******* * *** ******** block ** ******** *** **** *** extent ** ***** **** ****** ******** sets. *** *********** ****** ** **** scenario *** *** ******* ************** ****** amassing * ***** **** *** ** people's *****. **** ***** ** *** question **, ** **** ****** ***** a ********** ******** *** ** ******* that ***** ******* *** ********** ** performance *** ****.

(1)
AH
Anthony Hackett
Jul 15, 2020

**** ***** ** *** ******** **, to **** ****** ***** * ********* training *** ** ******* **** ***** address *** ********** ** *********** *** bias.

***** ***** *****, * *** *** a ********* **** ** ************* *** all ********** ************. **** ***** *** only ***** ** ** *** *** AI ** **** *** ******* ****** and ******* ******** *** ****** ** ethical *********** ** *** ********** ***** used.

ZS
Zach Segal
Jul 15, 2020
IPVM • IPVMU Certified

* ***** ** ******* (****** *****, Jamie ***********, ****** *********, ******** ******* Vaughan, ***** *******, *** ****é ***, Kate ********) ***** ** *********** ******* proposing ********* ***** ***** ***** ********,********** *** ********. **** ***** **** *** ******** should **** **** ********** *** ***** use *** *********** ***** *** *** data *** ********* *** **** ** contains.

** ***** ** *********** ** *** algorithms *** * "**** **** ***** dataset" ***** *** ****** ********** *********** about **** ****.

***, *********, ********* **** ** ** interesting ****, *** ** ***** **** lead ** ********* *********** ** ******** because * ******* ******** ********* **** invariably **** *** ********* **** ***** on **** ****. *******, ** ***** definitely ***** ******* ******.

(1)
Avatar
Salvatore D'Agostino
Jul 15, 2020
IDmachines

** ****, ***** *********. * **** the ****, *** **** ******. ***** is * *** ***** ** ***** and ****** *** ** *** *****. My ***** ** ***** **** ** a *** **** ** **** ** this *****. ******* **** ** ******** to ******* **, *** **** * human *********** ***'* **** **** ***** to *** ****. ***** ** **** interesting ******* **** ** **-**************, *** other ******* ******* *******.

(2)
ZS
Zach Segal
Jul 15, 2020
IPVM • IPVMU Certified

***** *** *** * *****. ** is * **** ********* ***** *** only ******* **** ********.