Context and Motivation. Fairness in socio-technical systems is increasingly recognised as a critical requirement, especially in processes involving human-AI interaction. Fairness hazards are situations or factors that threaten the fair treatment of individuals or groups. If left unaddressed, they can accumulate into systemic bias. Therefore, ensuring fairness must be treated as a first-class requirement during system design, rather than a post-hoc fix. Question/Problem. Systematic methods for identifying fairness hazards in socio-technical workflows and translating them into requirements-level mitigations are still missing. Principal Ideas/Results. We propose Fairness Hazard Analysis (FHA), an adaptation of hazard analysis methods from the safety-critical domain to analyse fairness in socio-technical processes. FHA is demonstrated through an AI-assisted hiring case and supported by HumAInFlow, a modelling and simulation platform. The approach is preliminarily evaluated through two focus groups. The feedback from participants highlights FHA’s usefulness for structured fairness analysis, the importance of diverse expertise, and the potential for deeper integration within HumAInFlow. Contribution. This work offers a novel method for integrating fairness into requirements analysis of socio-technical workflows, and provides an LLM-based tool to automate the analysis, marking a shift from bias detection to bias prevention with fairness-by-design.