Which of the following presents the MOST significant barrier to generative AI model explainability?

The rapid evolution of modern generative AI architectures (option B) is the largest barrier to explainability.

Complex deep learning models like LLMs, diffusion models, and transformer-based architectures involve millions or billions of parameters, making it extremely challenging to determine precisely how outputs are produced.

AAIA notes that explainability challenges arise because:

Model structures are highly complex

Parameter interactions are nonlinear

Internal representations are not human-interpretable

Continuous updates make documentation outdated

Training data and latent representations create opaque reasoning chains

Bias (A) affects fairness, not explainability.

Stakeholder alignment (C) is a governance issue.

Lack of staff experience (D) is a training problem, not a structural barrier.

The inherent technical complexity and speed of model evolution are the primary obstacles.