TY - JOUR
T1 - Human–AI Jurisdiction Boundaries in Computational and Data-Driven Materials Engineering
AU - Wei Liu
AU - Zhang Min
JF - Journal of Computational and Data-Driven Materials Engineering
JO - J. Comput. Data-Driven Mater. Eng.
SN - 3149-9368
Y1 - 2025
VL - 4
IS - 1
SP - 127
N2 - The computational and data-driven paradigm has fundamentally reshaped materials engineering, enabling the navigation of vast chemical and structural spaces through machine learning, high-throughput computation, and autonomous workflows. Yet this transformation has also exposed a critical conceptual gap: the absence of explicit, structured boundaries that govern the division of cognitive labor between human experts and artificial intelligence systems. Without such boundaries, AI contributions risk overstepping domains requiring physical intuition, ethical judgment, and contextual synthesis, while human oversight may inadvertently constrain the scale and speed that define modern discovery pipelines. This manuscript introduces the Epistemic Jurisdiction Framework (EJF), an original systems-level model that delineates jurisdiction layers, interfaces, and feedback mechanisms tailored to the materials discovery ecosystem. The EJF maps the flow from raw data to validated discovery through distinct zones of human primacy, AI autonomy, and negotiated hybrid spaces, emphasizing representation–inference interactions and computational steering logics. Grounded in the recent literature on machine learning for materials, explainable systems, and data-driven infrastructures, the framework offers a conceptual scaffold for infrastructure-level design rather than performance optimization. Its implications extend to the construction of more robust, interpretable, and sustainable computational ecosystems in which human and AI capabilities are aligned rather than blurred. The EJF thereby provides a foundation for next-generation materials engineering platforms that preserve epistemic integrity while fully exploiting computational scale.
UR - https://iamrp.net/k608010423
ER -