Model tuning is the experimental process of finding the optimal parameters and configurations for a machine learning (ML) model that result in the best possible desired outcome with a validation dataset. Single objective optimization with a performance metric is the most common approach for tuning ML models. However, in addition to predictive performance, there may be multiple objectives which need to be considered for certain applications. For example,

Fairness – The aim here is to encourage models to mitigate bias in model outcomes between certain sub-groups in the data, especially when humans are subject to algorithmic decisions. For example, a credit lending application should not only be accurate but also unbiased to different population sub-groups.
Inference time – The aim here is to reduce the inference time during model invocation. For example, a speech recognition system must not only understand different dialects of the same language accurately,

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