• An AI model learns the potential biases in data and even amplifies biases. It is advisable to implement techniques to not only remove potential biases in data during data cleansing but also remove or mitigate model bias in model development.
• These techniques are divided into three based on the implementation stage of the bias mitigation method: pre-processing, in-processing, and post-processing. Select and implement an appropriate technique depending on the AI model, objectives, and mission.