Mitigating AI bias

AI bias is an algorithmic bias that either comes from the model itself through its learning process or the data it used to learn from. The most obvious solution to mitigate bias is not programmatic mitigation methods but ensuring fair processes when collecting data. A data collection and preparation process is only truly fair when it not only ensures the resulting data is balanced by sensitive attributes but also ensures all inherent and systematic biases are not included.

Unfortunately, a balanced dataset based on the sensitive attribute does not guarantee a fair model. There can be differences in appearance among subgroups under the hood or associative groups of the data concerning multiple factors, which can potentially cause a biased system. Bias, however, can be mitigated partially when the dataset is balanced compared to without concerning the observable sensitive groups. But what are all these attributes? It might be easier to identify data attributes in...