6: Explains how human bias could affect the usefulness of AI solutions AND supports with reference to the video. 5: Describes how, and refers to a scenario(s) in the video. 4: Outlines how human bias could affect AI solutions and refers to the video. 2-3: Identifies some sources/examples of human bias AND/OR effects on AI AND/OR outlines how AI learns. 1: Some relevant information.

AI develops its ability by analysing vast amounts of data. The quality of these data can be influenced by human bias, and in turn, affect the usefulness of AI solutions.

Human bias may be explicit or implicit. Explicit biases are intentional. For example, a researcher may favour male physicists and only collect pictures of male physicists for training an AI solution, as shown in the video. Implicit biases operate unconsciously and may be shaped by social and cultural factors. Whether the bias was explicit or implicit, the choice of male physicists would suggest to the AI solution that physicists are male.

Human bias can occur in various stages of the development of AI solutions. As shown in the photo library example, training data needs to be diverse and representative, otherwise AI solutions may produce inaccurate or unhelpful responses. The process of labelling training data may also introduce biases as people may have different interpretations of the same data. The video shows that if most people draw shoes in particular ways, certain types of shoes will not be recognised as a shoe.