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What are the Sources of AI Bias?
Some of the sources of AI bias are:
• Data: AI systems are the result of the data that is fed into them. The data used to train the AI system is the first
step to check for biasness. The dataset for AI systems should be realistic and need to be of a sufficient size.
However, the largest data collected from the real world may also reflect human subjectivity and underlying social
biases. The Amazon AI recruitment system is a good example. It was found that their recruitment system was
not selecting candidates in a gender-neutral way. The machine learning algorithm was based on the number
of resumes submitted over a period of 10 years and most of them were men, so it favoured men over women.
• Algorithms: The algorithms themselves do not add bias to an AI model, but they can amplify existing biases. Let's
look at an example of an image classifier model trained on images in the public domain—pictures of people's
kitchens. It so happens that most of the images are of women rather than men. AI algorithms are designed to
maximise accuracy. Therefore, an AI algorithm may decide that the people in the kitchen are women, despite
some of the images being of men.
• Developers: The last source of AI bias is developers. Those who design AI models focus on achieving the
desired goals. On that path at times, the biases of the developers are reflected in their models. It's important to
note here that ethics and AI bias are not the problems of the machines but of the humans behind the machines.
AI Access
AI access can be acquired by two means:
• Data availability: AI needs access to huge data sets so that it can analyse, draw conclusions, and learn from
them.
• Abilities: AI needs access to capable hardware to turn its learning into useful action. Self-driven cars are
examples of AI with right access.
AI is used on bigger, faster and more expensive machines. AI is a privilege that only a few people can afford and
take advantage of this new technology. This has created a gap between these two classes of people and it gets
widened with the rapid advancement of technology.
Difference between Ethics and Morals
Ethics and morals are related concepts often used interchangeably, but they have distinct meanings and
applications. The word ethics originated from the Greek word ethos. The meaning of ethos is character. The word
morals originated from the Latin word mos.
The meaning of Mos is custom.
Aspect Ethics Morals
Definition Rules provided by an external source Principles regarding right and wrong held by
an individual
Source Institutions, organisations, societal norms Personal beliefs, cultural norms, religious
teachings
Application Specific situations and professional practices Personal behaviour and conduct
Objective Maintain order and fairness in society Foster personal integrity and align with
personal values
Examples Medical ethics, business ethics, legal ethics Personal beliefs about honesty, integrity,
kindness
AI Reflection, Project Cycle and Ethics 141

