Operationalizing GDPR Art. 22 impact assessments for ML-driven credit scoring

Jurisdiction: EU, DE Our team is building a credit-worthiness model that uses ~40 features (transaction history, employment signals, geographic indicators). The model outputs a score that directly influences approval decisions — which triggers GDPR Art. 22 (automated individual decision-making). We've completed the initial DPIA, but the practical questions that keep coming up in implementation: 1. Human-in-the-loop design: What does 'meaningful human review' look like in practice? Our current process is a 2-click override by a case worker who sees the same features the model used. Is that sufficient, or does the reviewer need access to the full reasoning chain? 2. Explainability vs. model complexity: We tested both XGBoost (good SHAP values) and a transformer-based model (better AUC, poor per-prediction explainability). Regulators seem to prefer the interpretable model even though it's less accurate. How have your teams balanced this trade-off? 3. Right to contest: Art. 22(3) gives data subjects the right to 'express their point of view and contest the decision.' We built a reconsideration form, but the turnaround SLA is unclear. Have you seen guidance on reasonable timelines? Interested in hearing how other teams have operationalized these requirements at scale. This is peer experience exchange, not a request for legal advice.