Ethical and Explainable Machine Learning for Retirement Product Selection: Addressing Bias, Trust, and Long-Term Wealth Preservation

The increasing integration of artificial intelligence and machine learning into retirement advisory systems has transformed pension management, financial planning, and retirement product recommendation processes. Financial institutions now rely on predictive analytics and intelligent recommendation systems to improve personalization, automate financial decision-making, and optimize retirement investment strategies. Despite these advancements, major concerns continue to emerge regarding algorithmic bias, lack of transparency, poor explainability, discriminatory recommendations, and weak accountability in AI-driven retirement advisory systems. These issues threaten investor trust, financial fairness, and long-term retirement wealth preservation. This study develops an ethical and explainable machine learning framework for retirement product selection that balances predictive accuracy with fairness, transparency, consumer protection, and regulatory accountability. The framework integrates interpretable machine learning models, fairness-aware optimization techniques, and explainable artificial intelligence mechanisms to improve retirement recommendation quality while reducing bias across demographic and socioeconomic groups. The study evaluates retirement product suitability using financial and behavioral variables including income level, retirement contribution patterns, investment preferences, portfolio volatility, administrative costs, retirement horizon, and projected post-retirement income sufficiency. Explainability mechanisms such as SHAP values, feature attribution analysis, and Local Interpretable Model-Agnostic Explanations (LIME) are integrated into the recommendation architecture to improve transparency and recommendation traceability. A hybrid predictive framework combining logistic regression, decision trees, random forests, and gradient boosting algorithms is developed and comparatively evaluated using fairness metrics, explainability indicators, investor trust measures, and long-term wealth preservation outcomes. The findings reveal that explainable and ethically governed machine learning systems substantially improve recommendation transparency, reduce discriminatory financial outcomes, strengthen investor confidence, and enhance retirement sustainability without significantly compromising predictive performance. The study contributes to the growing field of responsible financial technology by establishing that ethical and explainable artificial intelligence represents a sustainable and regulation-compliant approach to retirement product recommendation and long-term retirement wealth management.