Transparency

Transparency refers to the ability to clearly understand and interpret the processes, decisions, and outcomes of a data science model. It involves making the data, algorithms, and assumptions used in the model accessible and understandable to stakeholders, including end-users, regulators, and other interested parties. Transparency is essential for building trust in data science models and ensuring that they are fair, ethical, and accurate. It also enables stakeholders to identify and address potential biases, errors, or other issues that may arise in the model. Transparency can be achieved through various means, such as documentation, visualizations, and explanations of the model's inputs, outputs, and decision-making processes.
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