Bias in training datasets must be managed for various groups in classification tasks to
ensure parity or equal treatment. With the recent growth in artificial intelligence models and
their expanding role in automated decision-making, ensuring that these models are not
biased is vital. There is an abundance of evidence suggesting that these models could
contain or even amplify the bias present in the data on which they are trained, inherent to
their objective function and learning algorithms; Many researchers direct their attention to …

Bias in training datasets must be managed for various groups in classification tasks to
ensure parity or equal treatment. With the recent growth in artificial intelligence models and
their expanding role in automated decision-making, ensuring that these models are not
biased is vital. There is an abundance of evidence suggesting that these models could
contain or even amplify the bias present in the data on which they are trained, inherent to
their objective function and learning algorithms; Many researchers direct their attention to …