AI-Based Consumer Behaviour Modelling Under Algorithmic Transparency and Regulatory Constraints: A Governance-by-Design Framework Using the EU AI Act and Digital Services Act as Benchmarks

AI-driven consumer behaviour modelling powers targeting, ranking, recommendations, dynamic pricing, and churn prediction, but it increasingly operates under legal requirements for transparency, risk management, and accountability. This paper develops a governance-by-design framework for non‑EU jurisdictions by using the EU Artificial Intelligence Act (Regulation (EU) 2024/1689) and the EU Digital Services Act’s recommender‑system transparency orientation as comparative benchmarks. Drawing on the OECD Recommendation on AI and the NIST AI Risk Management Framework, we translate benchmark obligations into implementable lifecycle controls: data governance, model documentation, explainability, bias evaluation, audit logging, post‑deployment monitoring, and incident response. To strengthen decision usefulness, we add a quantitative scenario layer that compares governance tiers over a 2026–2035 horizon on expected consumer‑harm incidents, model performance retention under drift, and a regulatory‑risk premium proxy. Results provide a modular control architecture, an implementation sequence, and metrics to reduce regulatory and reputational tail risks while preserving commercial effectiveness.