Observability within the context of AI systems is crucial for maintaining transparency, reliability, and trustworthiness. It involves the implementation of tools and processes that enable developers, operators, and stakeholders to gain insights into the AI's performance, decision-making processes, and behaviors by analyzing the system's outputs, logs, and other external indications. This is particularly important in complex systems, such as deep learning models, where the internal workings can be opaque. Effective observability helps in diagnosing issues, understanding model predictions, ensuring compliance with ethical standards, and enhancing overall system governance.
While the concept of observability originates from control theory and was first discussed in the 1960s, its application to AI and computer systems has become increasingly important with the rise of complex and autonomous AI systems in the 21st century.
The development and refinement of observability principles and tools in AI are a collective effort by researchers in machine learning, AI ethics, and systems engineering, rather than attributed to specific individuals. Various open-source communities and tech companies contribute significantly to the advancement of observability tools and practices.