Fairness Hazard Analysis for Socio-Technical Processes: A Multiple-Case Study in Bias-sensitive Organisational Settings
2026-08-24 • Software Engineering
Software Engineering
AI summaryⓘ
The authors present Fairness Hazard Analysis (FHA), a method to find and fix fairness problems in systems where people and technology interact, especially during the early planning stages of software design. They tested FHA in focus groups and real companies, finding it helpful for spotting fairness issues and suggesting fixes. The participants valued the structured approach and identified that some fixes might be hard to apply depending on context. The study also found common ways to reduce unfairness, like having independent reviews and group decisions. Overall, the authors provide a tested way to consider fairness systematically to reduce bias in socio-technical systems.
fairnesssocio-technical systemsrequirements engineeringbiashazard analysisfairness hazard analysissystemic biasmitigationfocus groupscase study
Authors
Giovanna Broccia, Lucio Lelii, Roberto Cirillo, Dario Di Nucci, Samuel Fricker, Fabio Palomba, Giorgio O. Spagnolo, Alessio Ferrari
Abstract
Fairness is increasingly recognised as a first-class requirement in socio-technical processes, where interactions among human actors, software systems, and AI technologies may lead to unfair outcomes in decision-making workflows. If left unaddressed, fairness hazards may accumulate and reinforce systemic bias, highlighting the need to engineer fairness proactively. Despite growing interest in fairness-aware systems, systematic methods for identifying fairness hazards in socio-technical processes and deriving requirements-level mitigations remain limited. To support fairness-by-design during requirements engineering (RE), Fairness Hazard Analysis (FHA) is introduced as a methodology for systematically identifying, analysing, and mitigating fairness hazards. FHA is first assessed through a proof-of-concept validation conducted via two focus groups. Then, a qualitative multiple-case study involving two organisations examines its applicability in real-world settings. The proof-of-concept validation highlighted the benefits derived from the structured nature of the method, and suggested the need to include iterative, dialogic reflection with domain experts. In the multiple case-study where FHA was applied, the practitioners involved were positively impressed by the results and confirmed the relevance of the identified fairness hazards (spanning up to 27% of the process elements), as well as the appropriateness of most of the proposed mitigations, while noting that contextual factors might hinder their implementation. The evaluation also highlighted mitigation patterns, such as independent review and collective decision-making, which can be transferred to different organisations. This paper contributes a structured and empirically validated methodology for integrating fairness considerations in RE and preventing systemic bias in socio-technical processes.