# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Bayesian and Uncertainty Modeling
#### 6.3.1 Technical and Methodological Insights  
The profound explanatory power of Bayesian and uncertainty modeling frameworks lies in their ability to transform abstract statistical drift into tangible narratives of eroding confidence, where uncertainty quantification becomes the common language through which models articulate their changing understanding of the world. These approaches share a fundamental philosophy: rather than treating explanation as a post-detection analysis, they embed interpretability directly into the very fabric of drift awareness. At the heart of this paradigm is the treatment of uncertainty metrics as communicative artifacts that simultaneously signal, quantify, and rationalize distributional shifts. When [PERSON] et al. 's [125] METER framework observes Dirichlet belief distribution flattening--such as when \(\alpha\)-vectors evolve from (30, 2) to (6, 5)--this numerical metamorphosis explicitly narrates the model's loss of evidential certainty about known concepts. Crucially, this uncertainty surge does not merely trigger adaptation; it localizes the impact through hypernetwork-induced layer-specific weight shifts, revealing whether drift primarily affects foundational features or higher abstractions. The resulting shift tables become forensic records, telling engineers precisely where the model needs flexibility to reconcile new realities. This principle of uncertainty-as-narrative permeates the entire class. [PERSON] et al. 's [58] credit risk system demonstrates how Bayesian odds-scaling (\(\gamma\)) translates macroeconomic tremors into business vernacular: a \(\gamma\)-plunge from 1.2 to 0.4 explicitly declares \"default likelihood dropped to one-third of historical norms.\" Here, uncertainty quantification performs triple duty--it rescales predictions, explains the global confidence shift, and anchors hierarchical confidence signaling through star ratings that highlight individual unreliable predictions. The framework's genius lies in weaving \(\gamma\)-rescaling, confidence stars, and SHAP feature attributions into a single causal chain: external shocks alter priors, scores rescale to reflect new evidence, confidence stars redistribute to spotlight uncertainty, and feature bars reveal the drivers. Similarly, [PERSON] et al. 's [49] energy forecasting transforms variance inflation into operational storytelling. When load-prediction uncertainty doubles, the system does not just detect drift--it documents how scenario simulations forced battery-reserve increases, with dispatch logs explicitly stating \"80% of scenarios exceeded baseline.\" This explanation-through-action paradigm makes uncertainty the protagonist in an auditable decision-making drama. What distinguishes these approaches is their commitment to domain-aligned storytelling. [PERSON] et al. 's [114] environmental monitoring does not announce drift through opaque alarms; it curates \"key frames\" of anomalous batches and translates distribution shifts into physical narratives using RuleFit clauses like \(salinity\leq 23.6\wedge temperature\leq 13\Rightarrow oxygen\uparrow\). The disappearance of a high-pH rule becomes a chemist's alert for acidification, while emergent chlorophyll thresholds tell biologists of blooming phytoplankton. [PERSON] et al. [101] achieve similar intuitiveness by encoding uncertainty into evolving cognitive maps: entomologists watch Bayesian belief updates thicken wing-beat frequency branches while thinning harmonic-energy connections--a visual dialect that says \"the model now trusts rhythm over timbre\" without statistical jargon. Both frameworks demonstrate how uncertainty can be mapped to domain-specific lexicons, whether through disappearing chemical rules or shifting entomology decision paths. Underpinning this explanatory richness is a sophisticated repurposing of Bayesian machinery. Earlier methods like [PERSON] et al. 's [123] Polya Tree used posterior beliefs primarily for localization, but contemporary frameworks elevate uncertainty metrics to narrative anchors.