How does Watson's model explain and justify its decisions?

How does Watson's model explain and justify its decisions?

• A. Through explainability features that clarify model behaviors, recommendations, and predictions
• B. By using complex algorithms that obscure decision-making processes
• C. Through statistical analysis without user transparency
• D. By requiring user input for every decision made

Answer: (A)

Watson's model incorporates explainability features that enhance transparency and help users understand the reasoning behind its behaviors, recommendations, and predictions. These explainability capabilities are essential in contexts like healthcare, finance, and various industries where understanding the rationale behind AI decisions is crucial for trust and accountability. By providing clear and interpretable outputs, Watson's model aids users in grasping how specific inputs lead to certain predictions, thereby fostering confidence in the technology. This approach not only complies with ethical standards but also aligns with regulatory requirements in many sectors that demand transparency from AI systems. In contrast, options that point toward complexity, lack of user transparency, or an overwhelming need for user input do not accurately capture the goal of Watson's design, which emphasizes clarity and user trust in AI decision-making.

Watson's model incorporates explainability features that enhance transparency and help users understand the reasoning behind its behaviors, recommendations, and predictions. These explainability capabilities are essential in contexts like healthcare, finance, and various industries where understanding the rationale behind AI decisions is crucial for trust and accountability.

By providing clear and interpretable outputs, Watson's model aids users in grasping how specific inputs lead to certain predictions, thereby fostering confidence in the technology. This approach not only complies with ethical standards but also aligns with regulatory requirements in many sectors that demand transparency from AI systems. In contrast, options that point toward complexity, lack of user transparency, or an overwhelming need for user input do not accurately capture the goal of Watson's design, which emphasizes clarity and user trust in AI decision-making.