Traceability in the context of artificial intelligence and machine learning refers to the ability to follow the training process of a model and track its performance and results at different stages of the process.
Traceability is important in machine learning because it allows model developers and users to understand how the model has been created, what data has been used to train it, what pre-processing techniques have been applied to the data, what machine learning algorithms have been used and how its performance has been evaluated.
Traceability is also important to ensure transparency and accountability in the development and implementation of machine learning models, especially in sensitive areas such as critical decision-making in healthcare, criminal justice and public safety.
In short, traceability is a key aspect in the development and implementation of machine learning models, as it allows model developers and users to understand and evaluate the training process and model results.
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