Recently, you were presented with cases about data analytics in the real world. One case involved an unfair conclusion about the performance of women who worked at a business. It demonstrated that data can sometimes be true, yet unfair. In addition, it highlighted the importance of asking, "Why?" when reviewing the results of data analysis.

Another example involved data analysts prioritizing fairness and going out of their way to ensure their data was as fair as possible. Because they were working with sensitive and potentially biased health data, they chose to collaborate with social scientists in order to better understand the social context behind that data.

If you need to, return to the video to refresh your understanding of the examples before you continue. Then, discuss the first case and how the analysts at that company could improve their process:

What could they have done differently to be fairer in their analysis?

What could have made their conclusion less biased?