Explainability in AI is important for what reason?

Explainability in AI is crucial primarily to maintain transparency and trust. This aspect is vital because as AI systems become more integrated into various sectors, including healthcare, finance, and criminal justice, understanding how these systems make decisions is essential. When stakeholders, including users and regulators, can understand the rationale behind AI decisions, it fosters confidence in the system's fairness and reliability.

Transparency in AI systems helps to identify potential biases in algorithms, ensuring accountability. When users can see how and why a model arrives at certain outcomes, it allows them to verify the model's reasoning and make informed decisions. Ultimately, this trust is necessary for widespread adoption and responsible use of AI technologies, as it reassures individuals and organizations that the systems are operating ethically and effectively.

The other options do not directly relate to the core purpose of explainability. While computational speed, data size, and model accuracy are important in the context of AI development and deployment, they do not address the fundamental need for understanding and trust in AI systems.