Bias In Facial Recognition Varies By Country, NIST Report Shows

By: Zach Segal, Published on Jul 15, 2020

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.

***** **** ***** **** face *********** ** ********** racist, ******* **** *** of *** **** ********* studies **** ** *********** bias ** **, ********* *********** ****** **** (FRVT) **** *** *** ******** ********* of ********* *** ********* (NIST) ********* **** *** algorithms, *** ***** **** bias ****** ****** *** country ** ***********.

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** **********, **** ******** that ******* ********** ********* by ***** ****** ********* better ** **** ***** faces **** ******* ******** faces ***** *** **** majority ** ********** ********* Eastern ******** ***** **** East ******.

[***************]

Executive *******

***** **** **% ** western ********* ********* ********** that ********* ****** ** Eastern ******** *** **** East ***** **** (***** 10-100 ***** ******), ~**% of ***** ********* ********* algorithms **** ********* ****** on **** ***** *** than ******* ******** ****.

**** ****** ******* ** all **** ***** *****, although *** ** ********, with ~**% ** ********** developed ** **** ***** groups ********** ****** ** Eastern ***** ***** **** European.

Training **** *****

**** ********* ** ***** of ****** ******** ****:

**** ******** **** ******** data, ** ******* **** other ****** ********* ** the ***********, *** ** effective ** ******** ********** false ******** *************. ****, the ******-**** ********** ***** be *** ********** ** investigate *** ******* ** more *******, ******** *******, training ****.

*******, **** *** *** an ********** ******** ** establish ***** *** **** did *** **** ** training ****. ***, **** result *** *** **** with ***** ***** ****** performance ** ***** ***** faces, ** ******* ****** may ******* *** *******.

****, ***** ** *** NIST ********* *******:

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Study ********

**** ****** **** *********** disparities ** ****** *********** algorithms ** ***** ****** facial *********** ****** ****, analyzing ********** **** **** 100 ******. *** ****** of ***** ****, ***** we **** ***** **, they ******** *** ********** in ***** ***** ***** (FMR) ** ****** ***** from ****-******* ** ***** Department *********** ******** (***/*** 39794-5 **************). ** ******* of * ***** ***** would ** ** * friend ******** **** ****** using ***** ****, ******* of *****. ** **** case, **** ******** ***,*** visa *********** ****** **** 441,517 ********* *********** ******, selecting ****** *** ******** to * ****** ** countries ****** *** *** historical ***********. *** **** researchers **** **** ***** of *** ****** ** false ******* **** ******** (all ******* **** ***** because *** **** **** disjoint).

**** ***** *********** ** false ***** *****, **** false ***** ***** ****** in ******* ********* (******, Ukraine, *** ******) *** highest **** **** ******** and **** ****** (*****, Japan, *****, ***********, ********, and *******). *******, *** study ***** ****

**** * ****** ** algorithms ********* ** ***** this ****** ** ********, with *** *****-******** ***** on **** ***** *****.

******** **** ******** **** or ********* *********** ***** be ****** * *** of *** ****** *** ethnic **** ******* ** most ** *** ********** they ******.

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

***** **-*** ***** **** accurate *** *. ********* than *. ******, ****** like * ***** **********, this *** ** **********. The *********** ** ********** can ** **** (**** algorithms *** ***** ***** of ~.***** *** *. Asians *** ~.****** *** E. *********), *** ** some *****, *, ** close ** * ****** would ** ********. ** an ********* ** **** for ************/*:* ********, **** as ***** ****** *********** to ****** **** ******, mistakes ***** ** ********** infrequent *** *** ********* would ** ****** ************. However, ** **** *** identification/1:n *********, **** ** scanning ** **** *** anyone **** ** *********** warrant, *** ***** **** increases ************* **** *, and *** ********* *** have * **** ****** (even **** **** ***) if * ** ***** enough.

**, ***** *** *********** differences *** ***, *** difference *** *** ** an ***** **** *:* verification *** *** ** a **** ***** (**** disparity ****** **** ***) if **** *** *:* identification.

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

**** **% ** ***-****-***** made ********** ********* ****** (a ***** ***** ***** rate) ** ******* ******** faces **** ** **** Asian ****

***, ~**% ** ******* firms (********* *****, *********, and ******) *** ~**% of **** ***** ***** (expanded ** ******* ********* like ****** **** *** in **** **** *** not ** *** ***** of ********* ****** *** the **** *****) ********* better ** **** ***** faces **** ******* ********, as *** ***** ***** highlights:

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********

***** *** ******** ** widespread *********** **** ** a *******, *** **** that ********** ********* ** Chinese *** **** ***** teams ****** * ********* bias **** **** ** other *****, ******* **** much ** *** *********** disparity ** **** *********** accuracy *** ** ****** by *** *** ** diverse ******** ****. **** means **** ********** ****** ensure **** **** *** diverse ******** ****, ******* of **** ******* ** what ** ******* ** use. ** **** ***** that ********* ** ** teams *** **** ** better **********.

** ********, ***-***** ****** ask ***** *********** *********** in *** ********** **** are ******** ******* *** take ********* ****** **** consideration. ***** ** ********* may ******* ** ********** on *** **** ***********, it *** ************ ** the *********** ** ** used **.

***********

***** *** ***** *** rigorously *** **** ***, it *** *** ******* the ******** **** **** or **** ** ********* cause *** ******. **** means **** ***** ** can **** ******** ******* based ** *** *****'* findings, ** ****** ************ state ******** ** ****.

****, **** *****:

*** *** *** * corresponding *********** *** ***** Asian ***** *** *** few ********** [****] **** submitted ** ********** [** India *** **********].

*******, ** ** ***** possible **** ***** ***** Asian **** **** **** that *** *** ******* a ****** ********** ** South ***** **** ****** than ***-***** ***** ***** did. ** ** **** to **** ** ******* data **** ** ***** that *** ***** ****** to ****** ****** *********** neural ********, ** **** teams *** **** ****** on *** **** ****** accessible ********.

*** *** ** *** same ******** *** ******* why **** ***** **** clustered ******** *** *** bias *** ***** **** uniform, **** **** ********** performing ****** ** *** same ******** *** ****** groups.

Poll / ****

Comments (6)

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

***** *** **** ****** was **** * ****** ago, ****, **** ****, is ******* ** **** coverage ** ****** *********** technology. *** *********** *** topics ** *****, ****** share.

** **** ******* ****** out *** **** ****** did *** **** ** training ****. ** *** FR ******** ************ ******* on ******* ********, **** is ***** ****** ***** seem ** ** **** and **** ********. ******* a ****** **** **** training **** *** ******* a **** ** **** is *** ***** ** open ****** ****. **** will, ** ** *******, be ** *********** ***** to ****** **** ** not **** *** ****** to ***** **** ****** acquisition *** ******** **** becomes * *** ******** block ** ******** *** also *** ****** ** which **** ****** ******** sets. *** *********** ****** in **** ******** *** the ******* ************** ****** amassing * ***** **** set ** ******'* *****. This ***** ** *** question **, ** **** extent ***** * ********** training *** ** ******* that ***** ******* *** challenges ** *********** *** bias.

**** ***** ** *** question **, ** **** extent ***** * ********* training *** ** ******* that ***** ******* *** challenges ** *********** *** bias.

***** ***** *****, * can *** * ********* type ** ************* *** all ********** ************. **** would *** **** ***** to ** *** *** AI ** **** *** general ****** *** ******* entities *** ****** ** ethical *********** ** *** algorithms ***** ****.

* ***** ** ******* (Timnit *****, ***** ***********, Briana *********, ******** ******* Vaughan, ***** *******, *** Daumé ***, **** ********) wrote ** *********** ******* proposing ********* ***** ***** lines ********,********** *** ********. **** ***** **** all ******** ****** **** with ********** *** ***** use *** *********** ***** how *** **** *** collected *** **** ** contains.

** ***** ** *********** if *** ********** *** a "**** **** ***** dataset" ***** *** ****** accessible *********** ***** **** data.

***, *********, ********* **** is ** *********** ****, but ** ***** **** lead ** ********* *********** in ******** ******* * machine ******** ********* **** invariably **** *** ********* data ***** ** **** data. *******, ** ***** definitely ***** ******* ******.

** ****, ***** *********. I **** *** ****, its **** ******. ***** is * *** ***** on ***** *** ****** the ** *** *****. My ***** ** ***** will ** * *** more ** **** ** this *****. ******* **** is ******** ** ******* AI, *** **** * human *********** ***'* **** this ***** ** *** fore. ***** ** **** interesting ******* **** ** de-identification, *** ***** ******* related *******.

***** *** *** * agree. ** ** * very ********* ***** *** only ******* **** ********.

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