Mitigating Algorithmic Bias in Predictive Models

This article considers the issue of systematic errors in predictive machine-learning models generating disparate outcomes for different social groups and proposes a holistic approach to its mitigation. The risks and increasing legal requirements, along with corporate commitments to ethical AIs, drive the relevance of this study. The work herewith attempts to develop a bias-source taxonomy at data collection and annotation, proxy-feature selection, model training, and deployment stages; also, it tries to compare pre-, in-, and post-processing methods' effectiveness on representative datasets measured by demographic parity, equalized error rates, and disparate impact. This article is unprecedented in undertaking a two-level approach: first, a systematic review of regulatory definitions (NIST, IBM) and case studies (COMPAS, healthcare-service prediction, face recognition) that identified key bias factors from sample imbalance to feedback loops; second, an empirical comparison of Reweighing, adversarial debiasing, threshold post-processing techniques alongside flexible multi-objective strategies—YODO (via AI Fairness 360 and Fairlearn libraries)—considering acceptable accuracy losses. The root source of unfairness remains data bias; hence, pre-processing must be undertaken (rebalancing, synthetic oversampling), while in- and post-processing can essentially harmonize group metrics at some cost in accuracy reduction Furthermore, without continuous online monitoring and documentation (datasheets, model cards), the balanced model risks losing fairness due to dynamic feedback effects. Bringing together technical fixes with rules and making the audit process official ensures the ability to copy and openness, which is key for long-term faith in AI systems. This article will help machine-learning builders, AI-responsibility experts, and checkers find ways to find, gauge, and lessen algorithmic bias in live models.