UK/US Media Report on Facial Recognition Problems

**** ******** ****** ******* from ****** *** **** month ** *** **** news ******* *** *** controversies **** ****** *********** technology ********* *******.

Gender/Racial ****

** ** ***** ******* ************ ******** "** ** ******?" (*** *****) *** BBC ***** *** ******** that ********** *** ******* on ** "* *** part ** *** *******," with *** ************** ***************** *** *** ****** for ***** ******** *** generally ******* **** *** Internet *** ****** *****.

** ******** *** ****** ************, ******** ******** **************** ********* * **** ** diversity ** "******* *****" as * ************ ****** behind ****** ***********’* ****-****-***** performance **** ****** ** color.

**. **** ** * member ** ************** ******* ******, ********** ********* ** IPVM*********** **** ****, ***** was ******* ** ******-/***-******** computer ************ **********, *********************** **. ****'* ******** to *** ***, ******* that:

** ********** ** **** gender *********** ********a *********** *** ****** **** ********* ****** ************** ********** ** ******* ** ***** (9 - 20%) and darker skin vs lighter skin (10 - 21%). Lighter ***** **** ** ******* *** ****-********** *****, *** ****** ******* **** *** ***** ********** *****. 37% - 83% of classification errors resulted from the misclassification of darker females. Lighter males contributed the least to overall classification error (.4% - 3%). [emphasis added]

**. ****’* ********* *** Ms. **********'* ******** *** been ********* ** ********* **** ******* *** **’********* ********* ** ********* and **********(****), ***** *****:

***** *** ****** ******* Application ******,false ******** ***** *** ******* ** **** *** **** ******* *** **** ***** ******, *** ****** ** ******* ******** ***********. This effect is generally large, with a factor of 100 more false positives between countries. However, with * ****** ** ********** ********* ** ***** **** ****** ** ********, **** *** ***** ******** ***** ** **** ***** *****. With domestic law enforcement images, the highest false positives are in American Indians, with elevated rates in African American and Asian populations; the relative ordering depends on sex and varies with algorithm. [emphasis added]

*** ******** ** *** NIST ***** **** ******** visualized ** ************ ******* ** ******* ******** "** ******: ****** *********** technology — *** *** much ** ** ***** it?," *******, *** *******, that ***** *** ****** (i.e. ****** ********/******** ******) women *** ***** ****** to ** ********* ********** in ** ********:

Facial *********** *** ** ** ******

*********** ******, * ****** ********* at********** ********** *** ******'******* ** ******* & Technology,********** *********** "****** *********** *** [already] **** ******** ** hundreds ** ********* ** cases" **********, ****** **** "the ****** ***'* ******* almost ***** ***** *** that ** [****** ***********] was ****:"

*** ** ***** ***** involve ****** ***** *** Robert ********, ******* ******** men *** **** *********** arrested ***** ** ****** recognition ***** ********* *** profiled ********** *******, ************. **** **. Parks *** **. ******** spent **** ** ******* for ****** **** ******* committed ***** ** ***** generated ** ******** ******** which ** **** *** to *** ******** ** arrest ******** ********* ******* follow-up *************.

********* ****** ********** ****, **** **** caution ***** ** ** exercised ** *** *********** applications ** ****** *********** technology *** ** *** potential *************:

*** ****** *** ***** to **** ** ** arrest ******* *** ** innocent ****** ** ***** to ******, **’* ***** to ***** ****, *** the ****** *** ***** to **** ***.

Facial *********** ******** ****** **** ******** ********, **** ******* ***** **** ****

*** ********** **** *************** ******, ****** ************ *** ********* ** renew *** ** ******** online *** ******* ******'* *******. ** ********* ***** ** *** BBC *******, **. ****** explained *** *** **** Office’s ******* ********** ******** her ******** ******* ******, despite ***** * ******* of ******* *** ********* schemes, *** ** ** inability ** ****** *** eyes, *****, *** *** outline ** *** ****:

**** **** **********, ****** recognition ******** *** **** numerous ******* ***** **** during *** ******* *********** pandemic. ********* ****** ******** ************** ******* ***** ** Great ************ ***** ** ******** to*** *****:

******-*** ** *** ******* [of *** *********** ******* Union ** ***** *******] have **** ********* ****** ******* ************, *** **** is ******* * ****** recognition ******** ****** **** Uber **** ****. ** that ******, ** ** them ********[*****, *****, *** ******** ethnic].

'************' ****** ** **** Uber **** ******** ** be ******** *********** ***** the **** ******* *** delivery ********, ***** ** a ********* ** *** terms.

Lack ** ***** *********

** ****************** *** *** ** broadcast,** **************** **** * ***** of *** ****** *** "the **** ** ****-*********** national ********** ****** *** technology's [****** ***********'*] ***."

**** "**** ** ****-*********** national **********" *** ****** in *** **** ** initiate * ********** ** police *** ** ******************** *********** ********, ******* ********** ***************** *** ********* ****** the ****** *** *** "to '**** ******** ****** time ** ********* *********** rules' ********* *** **********'* ethical ***." **** ********** ************* ** *** ******* to ************ ****** *** moratorium ** ****** *** of ***********, ******* ********** ***************** "** ******* **** have **** ****** *****" the *** **** ****** decision.

***** **** ************* *** ************ ***** Chopra's ******* *** ****** restrictions ** ****** *********** as **** ** ************* ** ** ****** to *** ***** ***********’ use ** ****** *********** software, ****** *********** ********* to ** **** ********** the *******, ********* ** Detroit (***** ****** ******** one ** *** ******* American *** ******** **** *******), ** ********* **** ********* ******* ****** ********** *****. ************, ** *** UK, ************ **************** ****** *********** ** used ** ******’************* ********* **** *** ** government *** ****** ******* banning ****** ***********, ****** the "**** ** ***** [the] ************* **********" ** criminals.

** ***** ** **** uncertainty ********* *** ******** of ****** ***********, ***** is ****** ****-******** ******** regarding *** ******** ******* in ***** **********. ** a ***** *************, **.**** ****** ******* ********** ** *** York**** ****:

********** **** **** *** come **** **, ******* you're ***- ** ****-****** recognition, * *****we **** ***'* **** ****** ********** ******** ** *** ***** *** ** ***** ***** ** ******** **** ******* **** *** ***** ***. So, my hope would be that the research knowledge improves around this and that maybe cities who have moved to ban it can re-open a dialogue just to confirm that they've come down on the right side of this. [emphasis added]

Facial *********** *** ** ******* ********

******* *** **** ** regulation ****** *** *** of ****** ***********, *** private ******, ** ******* on ***** ** *** case ** *** ******* Uber **** *******, ** increasingly ***** ****** *********** technology.

** ******** ** **** over *** **** *** months, *************, ***********, *** industry ******** ** **** Brazil *** ********** ** Mexico **** ********* ******* sector *** ** ****** recognition ** *****, ***** industry ************* ****, *** example, **** ****** *********** as * *** ** help ********* ********* *** customers ** **** ** as * *** ** help ****** ** ******** centers ******* ******** *** remove *********** ** **********, such ** ***** *********** and **********.

**** **** ************ ****************** ******** ************* **** ** *** topic, ****** **** ***** more **** **% ** US ****** ***** *** police **** ****** ***********, trust ***** **** ***** 40% *** ***-*** ***********, private ****** ********.

**** **** **** *** did ****** ********., ********** ********* ******** **** ********, the ******-***** *********** ******** technologies **** ** ******'*********** ******* *** ******* on ****** **** ** facial ***********'* ****** *********** with ******-******* *****, *** reflection ** ***** ****** in ********, *** ******* of **** ********* ** personal *** ********-***** ********* devices, *** ****** ** government ************ **** ********** companies ****** *** *********** pandemic, *** *** ******* public's **** ** ********* thereof:

Importance ** ****** *********

** ******** ** * legal *********, *********** *** experts ***** **** **** public ********* ** ****** regarding *** *** *** risks ** ****** ***********. For *******, ** *** report,** ************** ** *** **** that ***** ** ** requirement ** *** ** for ****** ** ****** an ******** ** ****** recognition *** **** ** his/her ******.

** ** ***** ******* entitled "**********’ **** ****** **** tech ****** **** ********** to **** *****," ************ ********* ********* *** ************* of **** ********* ******* facial ***********-******* *******, ****** that *** **** ******** this **** **** ******** over *** ******* **********, effectively ************ ******** **** pertaining ** ******'* ****** population. ***** ****** **** have * ******** ******* data ********** *** ***** as ****** ***** ** *****çã* de ***** ********, ** ****, ***** includes ********** ****** *** use ** ****** ***********, Brazilian *** ************* ********** ****** ****** *** a ******* **** ** awareness ** ******** ** the ***’* *************. ******* risks **** ** ***** experienced ** ****** **** discussed ** ****** ** Ms. **** ** ********* *********** ******* *** ****** in ************* *******:

****** *********** ** ********** harmful **** ** *****'* work, **** ** ************* someone, **** ** * threat ** ***** ****, they ***** ** *************, falsely *******, ******* ********, but **'* **** * huge ****** **** ** does ****,there's ***** **** ******* ***** **** *** ******** ** ********** ******** ** ******** ** ********* **** *** ******* ** ** * *** **** *****'* *********** ****** ******* *** ******* ******** *********. [emphasis added]

The ******** ** *** ****** *****

* *** ******* **** the ******* *** ** facial *********** ** * perception ** ************* ****** the **********, ** **********'* Ms. ****** ****** *******:

******* **'* * *******, because **'* **** **** does *** **** *********** match ** ***** *******, we're ****** ** *** much ********.We're ******, "**'* ****, *********, ** **** ** *****." [emphasis added]

**. ****** **** ** to ***** **** **** algorithms *** "****" ***** others *** "********." *** end-users, **** ********* ** the ******* ** ********* facial *********** ******** *** be ********* ** ********, thus ******** ************* ***** responsibility *** *************** ******** that *** ** **** cases ****** ** **********, as ******* *** ** Ms. *****, *** ****** who *** ****** ** renew *** ** ******** online:

**'* ******** ***** *********** or ******** ********* **** are ******* *** ******** that ** *** ***** for *** **** ******.

** * ***************** *** **** ***** post ** ***** ************, *** ******* ******* is *** ****** ****** the ********** *** *** the ********** ** *** of ******:

**'* *** ********** **** is *** **** *******, it's *** ****** *** use ** **** ***** hopes.

**** ********* *** ******** by *** ***:

****-********* ********* *******’* ** left ** ******** *****.

Facial *********** ** **** ** ****

**** *** ******** ******** Russia-founded ****** *********** ********************************* **-***************'*$** ******* *** ************* ***** ** ********** of *** **** ****, despite ****** ***** ******** in *** ** *** UK, ****** *********** ** here ** ****.

** *********** ************ *****'* ****** *****, ********* Systems ************* ******************* *** **** *** "robust **********" ** ******* data *** ****** ** favor ** ****** *********** solutions, ****** *** **** can **** **** ***** and ****** ********* *******' exposure:

[*]**** ********** **** ** that ********* ** [********* *************** *****] ****** **** allows ******** ******** ** identify ******* ***** *** wearing **** *****, *********** safe ****** ********* *** avoiding ************ ** ****** spaces, **** ** ******* station *********, ******* ** leaving **** ******** ** the ****** ** ******, who **** *** ********** at **** ** *********.

***** ******* ****** *********** is *** *** ** avoid **** ** ****** recognition **********, ******* ******** is ** ****** ******* it ** **** ** performs ****** **** ****** of *****. *** *******, the ************* *****-********* ****** *********** performs **** ****** **** East ***** ***** **** Western-developed ********:

[*]*** ********** ********* ** China ******* **** ******* FMR [***** ***** ****] on *** **** ***** population … ***** ********** are **** ******, *****, Hik ******, *****, *-**********, Yitu *** *** (******** University **** *******).