Member Discussion

Trueface, Facefirst, Anyvision Facial Recognition Bias

What do companies like TrueFace, Anyvision and FaceFirst say about the bias towards people of color and their algorithms? They have been very quiet.

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****** **** *** *** asking *** * ***% proof ** *******.

*** ** *** ******** world **** **** ** to **** *********?

* **** * ****.

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****** **** ****** *********** bias ** ******* **** and **** ** ******** facial ***********, ******* *** these ***** ******* ***** stuck ** *** ***** daily, *** ******** ** bias **** ***** ****** recognition ********* **** ***** us ** *********** ** answer ********* ********* **** clients.

** **** * **** / ** ******* ** these *************** ********* ****.

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*** *******, ******, ********* some **** ***** *** methodology *** ******* **** to ****** **** ** ML ******* **** ****. You *** **** **** about ** **** -*&* | ******, ***.. ** **** **** work ** ** *** progress *** ** **** in ******** **** ** companies *** ******* ** make ** * ********. I'd **** ** **** from *** *********. *** customers ********** ** **** rec ****** ********** ********* about ********* *********** ****** skin ***** *** ******?

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@****** - **, **********,**** ********* * *** pieces ** ******* ** bias **** *** ** viewed *****.

**** ********** ** **- ****** ******** ****** Meetup

******** **** **** ********

****** - ******* **** Mitigation- * ******* **** of ********* ** **** mitigation ******** *** **/** researchers *** *********.

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*** ** ** ****** because ** ****** ** probability. ** **** ***, Face *** ************** (****) allow *** *** * confidence ****** *** *** customer ** ******. ** with * **** ********** and *** **** *** many *******, ** **** too *** ** * confidence ******* *** *** will *** *** **** images.

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*** ** ** ****** because ** ****** ** probability

********. *** *** ****** is *** ****** *** how **** ** *****, which *** **** ************ amongst *******.

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******. ** ** ****** because ** **** ****** data. **** ** *** classic "******* ** - garbage ***" *******.

**** ***** **** ****** haven ***** **** ************ ****. **** ***** to ***** *** * great *******. ***** **** a "******* ****" ** the ***/*** ** * color-calibration ********* *** ***** cameras ***** ******** * middle-aged ***** *****. ** a ******, *****'* ***** performance ******* ******* ******* subjects *** ****** **** performance ********. **'** ****** a ******* ******* **** datasets ***** ******* ** train ****** ****. **** if **** *** * diverse *** ** ***** to ***** ***** ****** nets, **** ***** **** to ****** * *******. This ******** ********** *********** the ********.

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