Behind the Big-Box Black Box: The Hidden Cost of Enterprise AI

Artificial intelligence has rapidly entered geoscience interpretation, but most commercial implementations remain automation-first—closed, opaque, and detached from human reasoning. These systems accelerate processing but erode scientific accountability by producing untraceable results that interpreters must validate without understanding.

This presentation argues for a paradigm shift toward interactive deep learning (IDL), in which interpreters remain within the learning loop through continuous, real-time feedback. Such systems promote transparency, reproducibility, and data-centric iteration, allowing experts to guide model behavior instead of merely assessing its output. Quantitative comparisons demonstrate that interactive workflows achieve faster network convergence, drastically lower discard rates, and significantly reduce post-processing workload relative to automated “black-box” methods.

Theoretical foundations draw from human–computer symbiosis, emphasizing that cognition and decision-making should remain distributed between human and machine. Responsible adoption of interactive deep learning in seismic interpretation rests on three principles—transparency, accountability, and synchronous guidance – which together reestablish the interpreter as a scientific partner rather than a passive consumer of algorithmic output. Re-centering AI on interactivity transforms it from a tool of automation into an engine of collaboration that enhances, rather than replaces, human expertise in subsurface interpretation.