Final answer:
Explainability is the aspect of transparency that involves making a system understandable to non-experts. It is crucial in the realm of AI, where there is a need to clarify the legal requirements for transparency to address issues like algorithmic bias and ensure public trust in these systems.
Step-by-step explanation:
The subset of transparency that involves describing the system to a non-expert is known as Explainability. Explainability refers to the degree to which an individual can understand the outcomes of a system, and it is particularly important when discussing the transparency of artificial intelligence (AI). It is about making the operations of the system clear to those who are not experts in the field, enabling them to comprehend how decisions or predictions are made. Legal discussions on AI emphasize the need for greater transparency to address issues like algorithmic bias. However, achieving this level of transparency can be challenging, as current laws may not be fully equipped to handle the complexities of AI systems. As AI continues to develop unpredictably and with a level of autonomy, the difficulty in controlling or comprehending its decision-making processes becomes apparent. Hence, Explainability is essential for making the algorithm's biases and operations understandable to the general public and for establishing trust in AI systems. There is a concern that mandating full transparency for AI could be both costly and difficult to implement under current legal frameworks. This calls for a clearer definition of transparency requirements in legal terms before they can be effectively enforced.