What type of risks does functionality in AI assessment consider?

Functionality in AI assessment is primarily concerned with robustness and scalability because these factors determine how well an AI system performs under various conditions and how easily it can be integrated into larger systems or operations. Robustness refers to the AI's ability to handle unexpected inputs and maintain performance despite uncertainties or adversities, which is crucial in real-world applications where data and environments can be unpredictable. Scalability pertains to the AI's capacity to grow and handle increased loads or more complex tasks efficiently. These two aspects are critical in ensuring that an AI system remains effective and reliable as its usage intensifies or as operational demands evolve.

While other types of risks, such as intellectual property risks or user adoption risks, are important in the broader context of AI governance and deployment, they do not directly pertain to the functionality assessment of the AI system itself. Intellectual property risks relate to legal ownership of algorithms or data, user adoption risks focus on how users accept and utilize the technology, and financial market risks deal with investment and economic factors. The core assessment of an AI’s functionality is distinctly centered on its performance reliability and ability to adapt, which is why robustness and scalability are the correct focus in this context.