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Reboot
Answer the given questions:
1. Who comes to your mind when you think of a teacher or cook? Do you think you are biased?
2. What do you mean by bias awareness?
3. AI 's training data can have bias. (State True or False)
AI Bias
AI bias is a phenomenon that occurs when algorithm results are
systematically biased against a certain gender, language, race, wealth,
etc. AI bias leads to a skewed output. Algorithms can have inherent
biases because AI models are created by individuals with conscious
or unconscious preferences, and these preferences, which may not be
discovered until the algorithm is used publicly (as we saw in the case of
the hand dispenser and Google’s photo tagging algorithm).
Bias is one of the biggest challenges facing AI. Although all programmers try to have absolute factual data, there is
an inevitable bias when exploring the depth to which AI can be used. An inherent problem with AI systems is that
they are as good or as bad as the trained data. Bad data usually carries racial, gender, community, or ethnic biases in
algorithms responsible for critical decisions go unrecognised, they can lead to unethical and unfair consequences. In
the future, these biases may worsen, especially as many AI recruitment systems will continue to use incorrect data for
training. Therefore, there is an urgent need to train these systems with unbiased data and to develop algorithms that
are easy to interpret.
Sources of Bias
Data bias happens when some parts of a dataset are given too much
weight or are over-represented implying the dataset doesn’t accurately
reflect what the machine learning model is meant to do, leading to
unfair outcomes and poor accuracy.
Often, biased outcomes often result in discriminate against certain
groups of people, such as those based on age, race, culture, or sexual
orientation. As AI systems become more common, the risk of data bias
is that it can amplify existing discrimination. To prevent any ambiguity
or reduce the biasness, it is important to identify the source of bias and
take the necessary steps.
Addressing AI bias involves thorough examination of datasets, machine learning algorithms, and other elements of AI
systems to identify sources of potential bias.
406 Touchpad Artificial Intelligence (Ver. 3.0)-XI

