What does Watson emphasize regarding explainability in AI?

Watson emphasizes providing insights into how models make decisions and predictions because explainability in AI is crucial for building trust and transparency between the AI system and its users. When users understand how an AI model arrives at certain conclusions, they are more likely to trust its outputs and incorporate those insights into their processes. This is particularly important in sectors like healthcare, finance, and law, where decision-making can significantly impact lives and livelihoods.

By focusing on explainability, Watson supports users in comprehending the rationale behind predictions, thus enabling informed decision-making and fostering accountability. This transparency is essential for ensuring that AI systems are not seen merely as "black boxes," but as tools that can be understood, scrutinized, and improved upon as necessary.

In contrast, while aspects such as decision-making autonomy, algorithm complexity reduction, and data usage protocols could be relevant to AI systems, they do not inherently address the core need for explainability, which is about clarification of process and outcome rather than the operational characteristics of the AI itself.