Mika Han
Knowledge and Technology (Perspectives) – Can algorithms be biased?
What are algorithms?
Look at a few definitions online, then provide
your own definition.
CLAIM
In what way(s) can an algorithm be said to be
biased?
EVIDENCE to support claim
What evidence / examples are there of bias?
Give at least one real-world example.
Finite sequences of mathematically
rigorous instructions, typically used to
solve a class of specific problems or to
perform a computation.
Algorithms might produce biased outputs
due to
 skewed or limited input data
 input of historical data, which
often contains existing biases
 subjective programming decisions
 subjective result interpretation
 lack of ability to understand the
broader context or ethical
implications of their decisions
The algorithms developed by Amazon
was discontinued after discovering they
have favoured male candidates. The
algorithms were trained using resumes
predominantly from male candidates,
which perpetuated gender bias in hiring.
(subjective programming decisions)
Facial recognition algorithms often failed
to recognize face of darker-skinned due
to lack of diversity in input data. (skewed
or limited input data)
REAL WORLD / EXPERT PERSPECTIVE
Give at least one quote from an expert who
claims that algorithms are biased?
Add citation, e.g. (Matthews, 2011)
"When you train your algorithm, you use
historical data that might have biases, and
these will continue" (Ayelet Israeli, 2022)
"...an algorithm can make vastly different
decisions when applied to different
populations" (Nicol Turner-Lee, 2020)
COUNTERCLAIM
In what way(s) might an algorithm be said
to be not biased / have very limited bias?
An algorithm is said to be not biased or has
very limited bias due to
 diverse data collection that reduces
unfair results and eliminates
anomalies
 Bias testing
 Algorithmic fairness techniques

EVIDENCE to support counterclaim
What evidence / examples are there of
bias? Give at least one real-world example.
Mitigating sampling and exclusion
bias
IBM has developed the AI Fairness 360
(AIF360) toolkit, an open-source library
that includes metrics to check for biases in
datasets and machine learning models, and
algorithms to mitigate these biases
(Mitigating samplling and exclusion bias)
Microsoft has developed Fairlearn, an opensource toolkit for assessing and improving
the fairness of AI models, which provides
fairness metrics and mitigation algorithms
to help developers understand and mitigate
bias in their models (Algorithmic fairness
techniques)
Researchers worked with Optum to
reduce the level of bias by 80% in an
algorithm used on more than 200 million
people in US hospitals (diverse data
collection)
REAL WORLD / EXPERT PERSPECTIVE
Give at least one quote from an expert
who claims that algorithms are biased?
Add citation, e.g. (Matthews, 2011)
“If you want the bias out, get the algorithms
in” (Andrew McAfee of MIT, 2019)
CONCLUSION
Give your own judgment based on the
findings of your research.
I believe that all algorithmic toolkits are
biased to a certain extent; it is almost
impossible for algorithms to be completely
unbiased due to imperishable presence of
bias which can be found anywhere in
reality. In fact, development of unbiased
algorithms is unrealistic since all creators of
algorithms are human being, hence are
biased to some degree.
IMPLICATIONS
Why should the bias of algorithms actually
matter / why should be care if they are
biased?
What problems might humans / society face
if this is the case?
Algorithmic bias fosters injustice,
discriminatory outcomes and perpetuate
societal inequalities. Development of biased
algorithms may lead to reinforcement of
harmful stereotypes (e.g., algorithmic
gender bias in Amazon, racist facial
recognition). Humans and the society may
foster negative perceptions and treatments
of minority groups, race, genders, or
others. Furthermore, development of
biased algorithms may establish health
disparity in the healthcare industry. Biased
algorithmic tools may result in incorrect
diagnoses or suboptimal treatment for
patients, which may lead to misdiagnoses,
delayed diagnoses, or failure to
treat/prevent the disease progress.