Humans developed Machines. Humans have 150+ bias and hence now Machines are biased too ! It talks about, Evolution of Bias in AI and solution to the big problem.

1. The Evolution of Face Recognition Through the Years
and The Real AI Bias
Back in the days, hand-drawn sketches were used to represent human face visually.
But it wasn’t until the invention of photography that portraits became widespread forms
of representation and identification. However, smart criminals could still get through by
altering their physical appearance.

2. Alphonse Bertillon (1880-1914), one of the forefathers of forensic science
invented a technique known as bertillonage in 1879, which emerged as a
promising standardized, foolproof biometric identification system.
A chart from Bertillonage system
After bertillonage (facial recognition) flopped, at end of 19th century it was
completely replaced by fingerprinting.​ Other identification technologies also drew
interests like voice, iris, genetic codes and even by the walk. The 9/11 attacks and
subsequent “War on Terror” vastly expanded and changed mass surveillance tactics: ​it
brought back facial identification as a preferred method of identification.

3. Authorities expanded public video surveillance and analyzed massive troves of security
camera and social media images. Governments also invested heavily in developing new
technologies.
Soon enough, companies like Apple and Facebook emerged as the leaders in facial
recognition technology.
In 2014, DeepFace ​made headlines​ when its 97 percent accuracy beat the FBI’s Next Generation
Identification system which was only 85 percent accurate
Facebook DeepFace in action

4. Real Big Problem – Bias in AI
There are about 150 human biases that affect how we make decisions. These biases
can easily make their way into AI systems. These systems are used by businesses as
well as governments to make important decisions and can lead to wrong decisions.

5. AI – in particular, both machine learning and deep learning – take large data sets as
input, distill the essential lessons from those data, and deliver conclusions based on
them. If the input data are biased – say, consisting of mostly young white males (our
‘garbage in’), then the AI will recommend mostly young white males (predictably, the
‘garbage out’). This is called “​algorithmic bias​.”

6. MIT Media Lab Project
Joy Buolamwini, who led the study from MIT Media Lab found these observations
In this way, bias in facial recognition threatens to reinforce the prejudices of society;
disproportionately affecting women and minorities, potentially locking them out of the
world’s digital infrastructure, or inflicting life-changing judgements on them.
Amazon – ACLU Test
Test conducted by the American Civil Liberties Union (ACLU) on Amazon’s facial
recognition software, Rekognition found racial bias. ​Amazon replied​ saying it was due to
wrong threshold set by the user. Amazon scraped their secret AI recruiting tool that
showed bias against women. Amazon isn’t the only technology giant experiencing
pushback from its own employees about how products are sold to and used by the US
government.

7. Google was criticized​ after its image recognition algorithm identified African Americans
as “gorillas.” Google ‘fixed’ its racist algorithm by removing gorillas from its
image-labeling tech.
Other examples include:
● Photo sets used to train image-recognition algorithms that identify men in the
kitchen as women.
● Job-listing systems that show more high-paying jobs for men than women.
● Automated criminal-justice systems that assign higher bail or longer jail
sentences to black people than white people.

8. Responsible use of technology
Businesses which rely on AI must act responsibly or they might get into some legal risk
and public condemnation. The world would be a very different place if we were able to
restrict people from buying computers because of a possible threat of its misuse. The
same can be said about the everyday technology in our lives.
There are many ways in which technology can help mankind. Example, preventing
human trafficking, inhibiting child exploitation, reuniting missing children with their
families, building educational apps for children and prevent crimes. And at the same
time it can also help businesses by enhancing security and simplifying everyday
procedures.
Achilles Heel of AI – Bad Data
Once the AI system learns something out of a certain data it tries to generalise its
understanding to situations and scenarios accordingly. Therefore, systems built using
data from one region perform less accurately in different regions, i.e, AI system
developed using data from western countries will not perform at par in the Asian
countries.
The AI Hierarchy of Needs
Think of AI as the top of a​ pyramid of needs​. Yes, self-actualization (AI) is great, but you
first need food, water and shelter (data literacy, collection and infrastructure).
Data Is The Foundation For Artificial Intelligence And Machine Learning