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    This is another example of bias. The teacher is making a decision based on who is loud or confident,
                     rather than who has the best ideas or skills for the project. This is unfair because the quieter
                     students may have important contributions but aren’t given the chance to share them.
                     In both cases, decisions are being made based on unfair factors, not taking into account the
                     abilities or potential of everyone equally. Bias in decision-making can cause missed opportunities
                     for many students.

                     Types of Bias
                     Bias  in AI happens  when the  system  makes  unfair decisions  based  on wrong or incomplete
                     information. Let's look at some types of bias that can affect AI systems:

                      Data bias: Data bias happens when the information used to train an AI system is incomplete
                        or unfair. If the data doesn't represent everyone equally, the AI might not make fair decisions.
                        For example, imagine a school’s AI system that picks students for a competition, but it only
                        looks at the achievements of boys. The AI might unfairly choose more boys, even though girls
                        are just as talented. This is data bias because the system was trained on data that didn’t
                        consider all students equally.

                      Historical bias: Historical bias happens when AI learns from past decisions that were unfair.
                        If the data includes decisions that favoured one group, the AI might keep making the same
                        unfair choices. For example, if a company has hired more men than women in the past, an
                        AI system trained on that data might keep favouring men. This is historical bias because the
                        system is learning from the past, which may not have been fair.

                      Measurement bias: Measurement bias happens when the wrong things are used to judge or
                        measure something. If the wrong factors are considered, the AI might give the wrong results.
                        For example, imagine a school that judges students based only on how many hours they
                        study, instead of how well they perform in tests. This could be unfair because it doesn't show

                        how well the students understand the material. This is measurement bias because the wrong
                        criteria are being used to make decisions.

                                                                                       Ethics and AI Bias Awareness    73

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