What can audits help improve in relation to AI systems?

Audits play a crucial role in enhancing transparency and explainability in AI systems. By systematically examining the algorithms, data sources, and decision-making processes involved, audits help ensure that the inner workings of AI technologies are scrutinized and understood. This scrutiny is essential because transparency allows stakeholders, including users and regulators, to comprehend how decisions are made by AI systems, which can build trust.

Furthermore, explainability refers to the ability to articulate how specific outputs were generated from the inputs in an AI system. An audit can reveal biases, errors, or decision-making patterns that may not be immediately apparent, thereby facilitating deeper insights into the AI's functioning. This capacity for providing clear and understandable rationales for outcomes is increasingly important, especially in regulated industries where accountability is paramount.

In contrast, while performance and reliability, complexity and cost, and speed and efficiency may also benefit indirectly from effective audits, they do not specifically aim to enhance the critical aspects of transparency and explainability that audits directly address. Understanding the mechanics of an AI system encourages continuous improvement and oversight, which is vital for responsible AI governance.