Auditing Data Inputs

To uncover hidden biases in AI systems, it is crucial to start by auditing the data inputs. The biases often originate from the data used to train these systems. By thoroughly reviewing and analyzing the data for diversity, representativeness, and fairness, we can identify potential sources of bias. This process may involve statistical analyses, diversity measures, and fairness indicators to ensure that the datasets do not unfairly favor or discriminate against any particular group.