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                    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.

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