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UNMASKING THE
SHADOWS:
THE HIDDEN BIAS IN AI
ABSTRACT:
 This article explains how AI is biased in some topics and explain
how it is affecting humans. Like Gender inequality, Historical
Bias and Medical Bias.
1. INTRODUCTION
Bias is not an uncommon phenomenon. Bias, Inequality and
Discrimination had been practiced by humans since millenniums. It is
only in the last century that women were given the right to vote.
Racism is still an issue in the whole world. As for ageism in society, it
doesn’t seem that major to people who are not aware of it. But
these small acts affect people’s mental health intensively. In job
applications, individuals now omit their date of birth, gender, and
even their names to mitigate bias during the recruitment process.
However, despite these efforts, bias still persists when reviewing
resumes and conducting job interviews. [1]
So, Bias is common in our lives these days. But, Do AI and computers
exhibit bias similar to humans?
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2. HOW AI EXHIBIT BIAS
Yes. However the potential for technology and improvement has
attracted many companies to invest and help in improving AI. Yet,
the presence of unintentional bias and its capacity to cause harm can
adversely affect a company’s reputation. [2]
Bias, whether intentional or unintentional, can significantly impact
corporate organizations globally. It has the potential to perpetuate
prejudices, create injustices, and exacerbate inequalities. To
illustrate this, I will spotlight two real-world instances where bias
within artificial intelligence (AI) systems was identified, leading to
ethical risks. In both cases, proactive measures were taken to
mitigate these risks and promote fairness and equity. By examining
these examples, we can better understand the complex interplay
between bias, technology, and organizational ethics. They are:1. In 2014, a team of software engineers at amazon were
creating a program to review the resumes of job appliers.
Unfortunately, in 2015, they realized that the system was
being biased for women who applied for technical roles.
So, after that amazon did not use it to evaluate candidates
for these biasedness and fairness issues.
2. Fast forward to 2019, San Francisco lawmakers rejected
the adoption of facial recognition technology due to
concerns about its error rates when applied to individuals
with dark skin or women. [3]
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TYPES OF BIAS EXHIBITED BY AI
Some of the types of biased AI are:1. Prejudice Bias:When the data used for training an AI model reflects
existing biases, stereotypes, and societal norms, those
biases become ingrained in the learned model. This
phenomenon is known as prejudice bias. For instance,
when you search for “doctor,” the results predominantly
show male doctors. Conversely, a search for “nurse”
tends to display female nurse images. These patterns
highlight the gender-based stereotypes prevalent in
society. [4]
2. Sample/Selection Bias:This the problem when the data used for training the
machine learning model isn’t large, not representative
enough or is too incomplete enough to train a machine
learning model.
3. Recall Bias:Recall bias occurs during data labelling when labels are
inconsistently applied due to subjective observations. [5]
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SUMMARY:This article explains Bias and Inequality is being common in the
society. But AI is also being biased these days. Like the incidents
happened to amazon while analysing resumes and in 2019, while
dark people or women not recognized by facial recognition model.
Because of various reasons like data being not sufficient, biased data
given to the specific AI model, etc.
REFERENCES
[1] Bradley Robin. “How to Solve AI Bias” (2019-2020):1
[2] Roselli Drew, Matthews Jeanna and Talagala Nisha. “Managing
Bias in AI” (2019): 1
[3] Omowole Agbolade. “Research shows AI is often biased. Here's
how to make algorithms work for all of us” World Economic Forum
(2021)
[4] Cotton Richie. “Data Demystified: The Different Types of AI
Bias” Datacamp (2022)
[5] Holdsworth James. “What is AI Bias” IBM (2022)
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