In the context of AI, what is meant by the term 'explainability'?

The term 'explainability' in the context of AI refers to the transparency of AI decision-making processes. This concept emphasizes the importance of understanding how AI systems arrive at their conclusions and decisions. It allows stakeholders, including developers, users, and regulators, to gain insights into the reasoning behind the decisions made by AI models. Explainability is especially crucial in high-stakes applications, such as healthcare, finance, and law enforcement, where the implications of decisions can significantly affect human lives or societal outcomes.

By fostering transparency, explainability helps build trust in AI systems, ensuring that users can comprehend and verify the model's outputs and the factors influencing them. This is vital for accountability and ethical AI governance, as it enables stakeholders to assess whether the AI is operating fairly and without bias.

Other options highlight different aspects of AI capabilities, such as efficiency in data processing, usability through intuitive interfaces, and the capacity to handle large datasets. However, none of these address the core idea of explainability, which is fundamentally about the clarity and understanding of the decision-making processes employed by AI systems.