Description: Transparency in machine learning refers to the clarity and openness about the processes and data used in machine learning models. This concept is fundamental in the field of explainable artificial intelligence (XAI), where the goal is for models to be not only effective but also understandable to users and stakeholders. Transparency implies that developers and data scientists must be able to explain how and why a model makes specific decisions, including the interpretation of input data, selected features, and algorithms used. This clarity is essential to foster trust in AI systems, especially in critical applications such as healthcare, criminal justice, and finance, where automated decisions can significantly impact people’s lives. Additionally, transparency helps identify biases in data and ensures that models are fair and equitable. In summary, transparency in machine learning not only enhances the understanding of models but also promotes accountability and ethics in the development of artificial intelligence technologies.
