# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Fuzzy Transparency
#### 6.8.1 Technical and Methodological Insights  
The profound strength of the Fuzzy Transparency class resides in its foundational philosophy: explainability is not merely an output but the very fabric of adaptation itself. Across all five studies--[PERSON] et al. [129], [PERSON] et al. [120], [PERSON] et al. [102], [PERSON] et al. [136], and [PERSON] et al. [126]--the dynamic evolution of linguistically grounded rules constitutes the explanation for drift. When concepts shift in the data stream, these systems eschew opaque statistical alarms in favor of translating change into human-interpretable narratives through immediate, visible modifications to their fuzzy rule structures. [PERSON]'s neuro-fuzzy hybrid embodies this principle by embedding transparency into its core mechanics. Rather than simply flagging divergence through a metric, it exposes precisely how drift redefines feature interpretations--such as the membership boundary for \"high SrcBytes\" expanding from 80 KB to 120 KB as new fraud patterns emerge. This direct visibility of rule adjustments empowers operators to bypass accuracy metrics entirely, using the updated IF-THEN clauses as a diagnostic ledger to justify decisions like retraining. Building on this, a shared strategy amplifies clarity through deliberate sparsity. [PERSON]'s OSSR-NNRW exemplifies this approach. Its online recalibration of sparse weights transforms coefficient shifts into a self-documenting drift log: variables losing relevance abruptly vanish from the non-zero weight chart--like \"oxygen-enrichment flow\" disappearing in blast-furnace monitoring--while newly critical features emerge with fresh coefficients. This sparse tableau functions as an intuitive visual explanation, where the zeroing of weights explicitly signals deprecated relationships without demanding statistical literacy. Similarly, [PERSON]'s leRSPOP employs rough-set reduction to ruthlessly prune low-support rules after each data batch, distilling the active rule list into a high-impact change report. The disappearance of a clause like \"speed < 45 km/h\" or the sudden appearance of \"volume > 1200 veh/h\" directly narrates shifting data dynamics, sparing operators the need to decipher complex metrics. Linguistic anchoring further bridges technical adaptation and domain intuition, turning mathematical shifts into natural language. [PERSON]'s EEG recognition system maps changes in Gaussian parameters (\(\xi_{k}\), \(\delta_{k}\)) to clinically meaningful statements--e.g., neurologists observe that \"the High band-energy range has migrated from 80 uV\({}^{2}\) to 120 uV\({}^{2\prime}\)\". [PERSON]'s Fuzzy Hoeffding Tree extends this transparency, framing splits and withering branches as evolving stories: a new branch declaring \"Humidity Ratio: Medium \(\rightarrow\) Highnow best separates occupancy\" or a stale \"High-Humidity\" path vanishing communicates drift through plain-language rules, rendering thresholds interpretable without manual decoding. This consistent mapping to terms like Low, Medium, and High ensures that explanations resonate with domain practitioners. Notably, the papers refine and extend each other's approaches to enhance explanatory depth. [PERSON] introduces a knowledge-preserving ensemble layer where frozen pre-drift models safeguard historical context: if a discarded attribute regains relevance, original rules resurface via voting, preventing \"explanatory amnesia\"--a significant advance over [PERSON]'s approach, where deprecated features leave no trace. [PERSON], however, innovates by merging drift explanation with data diagnostics: outlier clusters visibly shrink affected coefficients, exposing data-quality issues intrinsically. [PERSON], meanwhile, temporalizes drift interpretation through rule lifecycles (appearing, evolving, vanishing branches), and [PERSON] explicitly links ensemble weight redistribution to rule updates, revealing not just what changed but why the model's confidence shifted.