“Algorithmic discrimination poses ethical and social challenges as machine learning systems increasingly drive decisions in areas like hiring and finance. In this context, software fairness debt has surfaced as a concept to capture the cumulative shortfalls in fairness that occur when software systems prioritize objectives such as efficiency or accuracy over equity. This paper presents a comprehensive mapping of fairness debt and algorithmic discrimination within software engineering literature, analyzing the root causes, effects, and mitigation strategies documented in recent studies. By exploring cases of fairness debt, we highlight the societal implications and technical costs associated with neglecting fairness in software development, from perpetuating social inequalities to increasing long-term maintenance burdens and potential legal liabilities.”