Computing Bias

Part of: Impact of Computing

Bias Lives in Computing Systems Computing innovations can reflect existing human biases because people build them and people supply their data. A biased system produces systematically unfair outcomes for some groups. The exam calls a key point out clearly: bias is not always intentional : it often slips in unnoticed. Where Bias Enters - Biased training data : a hiring model trained mostly on resumes from one group learns to favor that group. - Biased design choices : deciding which features matter encodes the designer's assumptions. - Biased use : even a fair tool can be applied unfairly by its users. Detecting Bias A common technique is comparing outcome rates across groups . If a fair process should approve people at similar rates, a large difference is a red flag worth investigating. Reducing Bias - Audit datasets for representation before training. - Test outputs across groups after deployment. - Include diverse perspectives on the design team. Note that detecting a gap is not proof of bias by itself, real differences can exist, but unexplained, large gaps demand scrutiny. Key Takeaways - Bias can come from data, design, or use . - It is frequently unintentional , so creators m

Challenge: Approval Rate Gap