White-Box Completeness for Artificial Intelligence
Abstract Current research on explainable and white-box artificial intelligence faces prominent issues: conceptual disarray, divergent perspectives, and a disconnect between theory and practice. The foremost priority is therefore to return to the field’s purest objectives and explore its most fundamental questions. To this end, this paper introduces the axiomatic criterion of white-box completeness (WBC). Specifically, an agent is white-box complete if and only if its behavior can be bidirectionally approximated by human interpretable and manipulable mathematical forms with bounded error. It is a formalization for realizing three ultimate objectives for trustworthy AI: discernible learned knowledge and behaviors, knowledge extraction from AI, and human knowledge injection. Therefore, the ultimate pursuit of explainable AI has been transformed from the vague goal of “making AI interpretable” into a precise mathematical problem. Keywords: White-box completeness; Interpretability; Explainable artificial intelligence; Trustworthy artificial intelligence; Knowledge extraction and injection.