AI 中文总结
本文提出公平性危害分析（FHA）方法论，经概念验证及多案例研究验证，可识别社会技术过程中高达27%的公平性危害，助力在需求工程中整合公平性考量以预防系统性偏见。
AI 中文摘要
公平性正日益被视为社会技术过程中的一类核心需求，在这类过程中，人类参与者、软件系统与AI技术之间的交互可能会导致决策工作流中出现不公平结果。若不加以解决，公平性危害可能会累积并加剧系统性偏见，因此亟需主动对公平性进行工程化处理。尽管人们对公平感知系统的兴趣日益浓厚，但用于识别社会技术过程中公平性危害并推导需求层面缓解措施的系统性方法仍然有限。为了在需求工程（RE）期间支持按设计实现公平性，本文提出了公平性危害分析（FHA），作为一种用于系统识别、分析和缓解公平性危害的方法论。首先通过两个焦点小组开展的概念验证验证对FHA进行评估，随后通过一项涉及两个组织的定性多案例研究考察其在现实环境中的适用性。概念验证验证凸显了该方法结构化特性带来的益处，并指出需纳入与领域专家进行的迭代式对话反思。在应用FHA的多案例研究中，相关从业者对结果印象积极，确认了所识别公平性危害的相关性（占过程元素的比例高达27%），以及多数所提缓解措施的适当性，同时指出情境因素可能会阻碍这些措施的实施。评估还凸显了可迁移至不同组织的缓解模式，如独立审查与集体决策。本文的贡献是提供了一种结构化且经实证验证的方法论，用于在需求工程中整合公平性考量，预防社会技术过程中的系统性偏见。
英文摘要
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.
