What does 'explainability' mean in the context of automated systems?

In the context of automated systems, 'explainability' refers primarily to the need for users to understand how and why a system arrives at its decisions or outputs. This concept is crucial, especially in fields that involve significant consequences from automated decision-making, such as healthcare, finance, and autonomous vehicles. By ensuring that users can interpret the reasoning behind a system's actions, organizations can promote trust, accountability, and informed decision-making.

For instance, when an automated system makes a recommendation or decision, users should be able to discern the factors and data that influenced that decision. This understanding can assist in evaluating the accuracy and fairness of the automation, as well as in identifying potential biases that the system may unknowingly carry.

In contrast, the other options address aspects of automation that do not relate specifically to the clarity of reasoning. Operating without human intervention focuses on autonomy rather than understanding, while reliability pertains to the consistency of the system's performance. Speed of data processing deals with efficiency rather than interpretability. Thus, while these factors are important in assessing an automated system’s overall functionality, they do not capture the essence of explainability as the necessity for users to grasp the underlying reasoning of automated outcomes.