Description: Model accessibility in the context of explainable artificial intelligence refers to how easily users can access and understand the functionality of an AI model. This means that users, regardless of their technical expertise, should be able to interpret how the model works, what data it uses, and how it makes decisions. Accessibility focuses not only on the user interface but also on the transparency of algorithms and the clarity of explanations provided about the model’s functioning. An accessible model allows users to trust its results, as they can understand the underlying processes and the reasons behind the decisions made. This is particularly important in critical applications, such as healthcare or legal systems, where automated decisions can significantly impact people’s lives. Model accessibility also fosters collaboration among experts in different fields, as it facilitates communication and mutual understanding of how AI solutions can be applied across various domains. In summary, model accessibility is an essential component of explainable AI, aiming to make technology more understandable and reliable for all users.
