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    Consider the following example:
                      ethical minds
                                                                  Hindi Sentence          English Translation from AI
                      Data manipulation to fit AI models            वह डॉ�टर है।                 He is a doctor.
                      can cause biased predictions and
                      affect fairness. To address this,     In this case, the AI assumes the doctor is male, even though
                      accurate,  unbiased  data  should     the Hindi sentence does not specify gender. This happens
                      be  used,  data  processing must      because the training data often contained more examples
                      be  transparent and AI systems        of male doctors. As a result, the AI learns these stereotypes
                      should be regularly audited for       from the data and might apply them when translating. This
                      fairness.
                                                            shows how crucial it is to ensure that training data is balanced
                                                            and diverse to avoid reinforcing gender stereotypes.

                                                                                                        21 st
                                                                                                      Century   #Creativity
                                                                                                       Skills
                                                            concept capsule

                         A machine is trained with the following dataset:

                                                         Training Data                           Label

                                                                                                 Apple

                                                                                                Mango

                         Now, test the above model for this new data:

                                        Will the trained model identify the fruit correctly? Why?

                                        Will the trained model identify the fruit correctly? Why?

                                        Will the trained model identify the fruit correctly? Why?

                                        Will the trained model identify the fruit correctly? Why?

                                       In the sequence 100, 90, 85, 75, 70, 60, ..., what is the rule?
                                               a)  Subtract 5, then subtract 10            b)  Subtract 10, then subtract 5

                                               c)  Subtract 10 each time                   d)  Divide by 1.1

                                                                                           Data and  Fairness in AI    61

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