Tracing Bias Origin and Predicting Bias Emergence Across AI Platforms via Cryptographic Provenance Graphs and Neural Granger Causal Consensus

Existing bias detection systems for AI recommendation platforms identify that bias exists but cannot trace where it originated, how it propagated across platforms, or predict when it will emerge before detection thresholds are crossed. This paper presents a unified architecture addressing these provenance, prediction, and governance gaps through four technically distinct contributions. First, we introduce the Bias Provenance Directed Acyclic Graph (BPDAG), a cryptographically secured graph structure that traces bias origin and cross-platform propagation using a novel neural-linear Granger causal consensus method. This method combines component-wise LSTM networks with Group Lasso sparsity enforcement, multi-head temporal attention attribution, variable-lag adaptive Granger causality with BIC-optimized lag selection, Daubechies-4 wavelet decomposition at five resolution levels, Hidden Markov Model regime-switching with three propagation states, and conditional transfer entropy with confounding platform control via kNN KSG entropy estimation. Each BPDAG node receives a SHA-256 hash-chained cryptographic provenance identifier anchored via blockchain Merkle root checkpoints, creating a tamper-evident causal attribution record. Second, we introduce a conformal bias emergence prediction system that forecasts bias before detection thresholds are crossed, producing emergence probability forecasts at five horizons (1, 3, 7, 14, and 30 days) with distribution-free conformal prediction intervals guaranteeing at least 95% marginal coverage and certified adversarial robustness bounds via randomized smoothing. Third, we present a synergistic bidirectional integration architecture where BPDAG-derived provenance context vectors condition emergence predictions (reducing false positive rates by at least 35%), while conformal emergence forecasts direct provenance traversal (reducing mean reconstruction latency by at least 55%). Fourth, we describe a production-grade streaming BPDAG architecture with O(log N) amortized incremental update per event, p99 latency below 500 milliseconds, horizontal geographic partition scaling, and exactly-once delivery semantics. Extended simulation over 50 monitored AI platforms, 5,247 bias propagation campaigns, 1,200 adversarial injection scenarios, 8 regulatory jurisdictions, and a 365-day continuous operation horizon validates all performance claims.