How do you deal with data bias and fairness in ML vs DL outcomes?
In machine learning (ML) and deep learning (DL), dealing with data bias and fairness is crucial to ensure the accuracy and ethical use of AI models. Data bias can lead to skewed outcomes and reinforce unfair practices. To address this, various techniques such as data preprocessing, algorithmic fairness, and bias detection tools are used to mitigate bias and promote fairness in ML and DL outcomes.
March 19, 2024