# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Optimization and Resource Scheduling
#### 6.11.1 Technical and Methodological Insights  
The approach to drift explainability proposed by [PERSON] et al., is fundamentally integrated into the operational logic of their ORRIC algorithm, creating a self-revealing system where every technical decision inherently communicates its reasoning about drift [32].The algorithm's core output--a precisely specified retraining and inference configuration pair like \"30% sampling, 28 \(\times\) 28 inference\"--functions as a primary explanation mechanism. This selection represents the exact boundary of computational feasibility within the edge device's constraints, immediately revealing resource allocation priorities. By designating this \"last viable pair\" before budget overrun, the system transparently quantifies how much capacity is being diverted to preempt future drift through retraining versus servicing current inference demands. More subtly, this boundary choice serves as an implicit severity indicator: configurations approaching resource limits signal that the system perceives drift as sufficiently threatening to warrant near-total resource dedication to mitigation. The system further enhances interpretability by distilling complex continuous decisions into four discrete operational regimes: Knowledge-Distillation, Inference-Greedy, Focus-Shift, and Inference-Only. This categorization provides immediate heuristic transparency, allowing operators to instantly recognize which high-level strategy governs current operations. When the system declares \"Focus-Shift\" mode, for example, it directly communicates that imminent drift has triggered significant resource reallocation away from immediate inference tasks. Crucially, these regime labels also serve as predictive justifications that anticipate and explain forthcoming performance changes. An \"Inference-Greedy\" designation not only describes the current resource focus but inherently warns operators that accuracy will likely degrade because proactive retraining is being sacrificed for immediate throughput, directly linking the operational state to the expected consequences. Complementing these qualitative explanations, the formal competitive-ratio bound--expressed as \((1+\alpha)f(0)/f(A_{t}^{max})\)--provides a quantitative foundation for trust. This scalar transforms abstract theoretical guarantees into concrete, interpretable safety margins. It explicitly quantifies how much more efficiently the chosen balance between retraining and inference handles persistent drift compared to a naive Inference-Only baseline. By framing performance relative to this worst-case scenario, operators receive an actionable confidence measure grounded in tangible resource efficiency gains rather than opaque drift metrics. Together, these three interconnected mechanisms--the boundary-revealing configuration output, the causally predictive regime labeling, and the comparative trust quantification--create a coherent explanatory framework where the system's response to drift becomes legible through its operational outputs. The architecture ensures that explanations emerge organically from the algorithm's normal functioning, continuously translating technical resource decisions into operator-understandable narratives about drift mitigation priorities, anticipated impacts, and system confidence. This class clearly aligns with Interpretable AI.