Context: Bias in AI and software systems has raised widespread concern due to its role in perpetuating discrimination, prompting the emergence of the concept of software fairness debt. Objective: This study explores how fairness debt in software is portrayed in gray literature, aiming to identify real-world examples, root causes, and effects. Method: Using a query-based approach, we retrieved and analyzed 79 articles from gray literature, applying content analysis guided by an established fairness debt conceptual model. Results: We identified 23 examples of fairness debt (e.g., racism, sexism), 32 causes (e.g., training, societal, and historical bias), and 14 primary effects (e.g., proliferation of discrimination, reinforcement of stereotypes). Conclusion: Our findings extend the fairness debt framework with empirical insights from nonacademic sources, offering a broader understanding of software fairness and guiding future research and mitigation strategies.