AC9M10ST01

Elaborations

• AC9M10ST01_E1identifying potentially misleading data representations in the media such as graphs with broken axes and scales that do not start at zero or are nonlinear; recognising when data is not related to the claim, not representative of the population or is deliberately being used to mislead, or support a claim or biased point of view
• AC9M10ST01_E2investigating the source and size of the sample from which the data was collected and deciding whether the sample is appropriately representative of the population
• AC9M10ST01_E3investigating population rates and discussing potential ethical considerations when presenting statistical data involving infection rates, and the number of cases per head of population
• AC9M10ST01_E4using secondary data to predict the number of people likely to be infected with a strain of flu or experience side effects with a certain medication, discussing the ethical considerations of reporting of such data to the wider public, considering validity claims and samples sizes
• AC9M10ST01_E5recognising how the identification of bias is a critical aspect of machine learning and deep learning because biases can significantly impact the fairness, accuracy and ethical implications of artificial intelligence systems
• AC9M10ST01_E6using the concept of Indigenous data sovereignty to critique and evaluate the Australian Government’s “Closing the Gap” report