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    Let us consider a scenario.

                     A school is developing an AI system to recommend after-school clubs to students. The system uses
                     past data about which students have enjoyed various clubs. The following training data is used:

                                                  Club              Boys              Girls

                                                 Football             60                20
                                                   Art                15                55

                     The AI program studies the data and learns that most boys enjoy playing football and most girls
                     enjoy art. Now, imagine a new girl student joins the school.
                     Think:                                                                         fact bits

                       Which club will the AI probably recommend to new students            A study by the MIT Media
                        after studying the given data?                                          Lab found that facial
                                                                                             recognition systems had a
                       Is the AI making the recommendation based on ability or              34.7% error rate for dark-
                        data pattern?                                                       skinned women, highlighting

                       Do you think every student gets an equal chance through                the need for balanced
                        this AI recommendation system?                                             training data.

                       How can the data be improved to help the AI make better
                        and fairer suggestions?

                                                                Ask
                                                                          AGENT
                                                           OrangeAI
                      If an AI makes a biased decision, who should be held responsible—the AI or the humans who trained it?

                              Study

                     ENSURING FAIRNESS

                     A company once created an AI system to help recruit new employees. The system learned from
                     past hiring data, which showed that most of the engineers hired in the past were men. Because of
                     this pattern, the AI started to prefer resumes that looked similar to those of male applicants. This
                     is a good example of how AI can pick up human biases from historical data if the developers are
                     not careful.

                     This situation shows that AI can unintentionally favour certain groups, even though it is supposed
                     to treat everyone fairly. To prevent this, developers need to ensure that AI systems are trained
                     with balanced and diverse data.

                     AI should be designed to benefit everyone, not just a particular group, so that all applicants have
                     an equal chance, regardless of their gender, race or background. This helps create a fairer and
                     more inclusive system.

                                                                                           Data and  Fairness in AI   63

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