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    DATA AND

                                      3                                        FAIRNESS IN AI

                     PRIMARY PREVIEW

                        Role of Data                                            Understanding Bias
                        Ensuring Fairness

                     A group of artists in Seoul, Korea, collected photographs of clouds that looked a bit like human
                     faces. They decided to feed these pictures into an AI face-detection program. To their surprise,
                     the AI identified all the cloud images as faces. As humans, we sometimes imagine faces in the
                     clouds too, but this project highlighted an issue with the AI system's training data. The AI’s face
                     recognition  system  was trained  using  more  images  of light-skinned  men, which caused  it  to

                     incorrectly identify the cloud images as faces. This shows how AI can make mistakes if the data it
                     learns from is not balanced or diverse.

                     The example makes us realise that:
                       AI looks  for patterns  it  has learned from its
                        training data.

                       The training data influences what AI detects,
                        which means if the data  is unbalanced,  the
                        AI's predictions can be biased or incorrect.

                       Given the widespread use of AI across many
                        fields, it is essential to carefully examine and
                        ensure the balance and diversity in the training
                        data to avoid errors and ensure fairness in AI systems.

                     ROLE OF DATA

                     Imagine training an AI program to recognise different kinds of balls, like a football and basketball
                     from images. The AI will be provided with many pictures of each type of ball to help it learn how
                     to identify them. It studies these images carefully, looking for patterns such as the round shape
                     and texture of a football and the larger size and bounciness of a basketball. Over time, the AI
                     learns to distinguish between the different balls based on these patterns. After learning from

                     these examples, the AI can then try to correctly identify new images of balls it hasn’t seen before.

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