BEHAVIORAL TEMPORAL FEATURE ATTRIBUTION LATTICE (B-TFAL)
A deterministic protocol that stress-tests black-box AI decisions and produces bounded, regulator-grade confidence artifacts – without model internals.
In layman’s terms: B-TFAL tests an AI decision the way you’d test a bridge.
You remove supports one at a time, measure what changes, and only trust the result if it stays stable. Instead of guessing why a model made a choice, it stress-tests the choice and gives you a confidence score that shrinks automatically if anything looks unstable.
Why this matters
Modern interpretability methods:
- -Sampling-based
- -Non-deterministic
- -Descriptive, not auditable
Result: Explanations that look convincing but can’t be certified.
My approach
B-TFAL reframes explanation as a staged intervention problem.
It introduces:
-Stage-indexed intervention pipeline (T0–T4)
-Deterministic perturbation topology
-Dual stability metrics (attribution + outcome)
-Weakest-link bounded confidence fusion
*KernelSHAP uses sampling; TreeSHAP can be deterministic but requires model structure.
Decision Engine:
Conceptual Architecture
Deterministic intervention lattice that transforms opaque model outputs into stage-aware, normalized confidence artifacts.
Implements explicit temporal indexing (T0–T4), dual stability validation, and bounded confidence fusion to produce structurally auditable decision outputs.
Protocol Guarantees
-Deterministic (zero variance under identical inputs)
-Stage-structured causal tracing
-Explicit confidence bounds
-Model-agnostic execution
- -O(n) runtime
Deterministic Execution Protocol
-Identical inputs produce identical explanation artifacts
-No stochastic sampling or random perturbations
-No gradient access or model internals required
-Explicit stage-indexed intervention pipeline (T0–T4)
-Reproducible lattice topology across runs
Bounded Confidence Synthesis
Confidence is computed through deterministic weakest-link fusion of independent stability components:
- -Attribution stability
- -Outcome stability
As instability increases, fused confidence contracts monotonically and remains explicitly bounded within defined limits.
Empirical Validation
-Zero variance across repeated runs under identical inputs
-Monotonic confidence contraction under controlled instability
-No overconfidence under adversarial perturbations
-Linear time complexity (O(n))
-Browser-executable, no gradients or retraining required
Example output artifact generated by the deterministic B-TFAL protocol.
Example output artifact generated by the deterministic B-TFAL protocol.
Example output artifact generated by the deterministic B-TFAL protocol.
Example output artifact generated by the deterministic B-TFAL protocol.
Stage-Indexed Perturbation Paths
Each intervention is applied along a structured T0–T4 pipeline, enabling controlled causal tracing instead of flat feature masking.
Deterministic Attribution Propagation
Feature impact is normalized and propagated through stage-constrained transitions – with no stochastic sampling.
Dual Stability Evaluation
Attribution balance and outcome sensitivity are computed independently and fused via weakest-link aggregation to prevent structural overconfidence.
— PROJECT NAME
B-TFAL
— ROLE
Inventor
— DATE
Early 2026
Designing a deterministic, intervention-based decision analysis protocol that produces structured, regulator-grade confidence artifacts from black-box AI systems.
The system integrates stage-indexed feature perturbation, entropy-constrained attribution stability, and range-bounded outcome sensitivity into a formally bounded confidence layer with deterministic fusion.
By replacing heuristic explanations with deterministic stress-testing, B-TFAL transforms opaque model outputs into auditable, machine-verifiable decision artifacts.