In recruitment AI, the integrity and fairness of the output are almost entirely dependent on the " Sources of data used. " If the training data comes from historical records that contain human bias (e.g., only hiring from certain universities or genders), the AI will codify and automate that bias. The ISACA AAIA™ manual states that auditing the " Data Source " and " Data Lineage " is the most effective way to identify the root cause of algorithmic discrimination. While data retention (Option B) is a general privacy concern, it is secondary to the high-impact risk of biased decision-making inherent in the data sources.