一句话：提出 ReFAIR，一个基于 NLP 与词嵌入的上下文感知需求工程框架，从用户故事中识别领域和 ML 任务并推荐公平性敏感特征。
机器学习公平性日益重要，但现有公平性支持方法多在模型训练等开发后期使用，缺乏能在早期提供公平性分析的手段。本文提出 ReFAIR，一个上下文感知的需求工程框架，用于从用户故事中分类敏感特征。该框架利用自然语言处理与词嵌入技术，先识别待开发系统的用例领域和机器学习任务，再推荐实现过程中应关注的上下文相关敏感特征。作者在自行构建的合成数据集上评估 ReFAIR，该数据集包含 34 个应用领域的 12,401 条用户故事。结果表明 ReFAIR 具有较高准确率，同时论文也讨论了当前局限。
Machine learning (ML) is increasingly being used as a key component of most software systems, yet serious concerns have been raised about the fairness of ML predictions. Researchers have been proposing novel methods to support the development of fair machine learning solutions. Nonetheless, most of them can only be used in late development stages, e.g., during model training, while there is a lack of methods that may provide practitioners with early fairness analytics enabling the treatment of fairness throughout the development lifecycle. This paper proposes ReFair, a novel context-aware requirements engineering framework that allows to classify sensitive features from User Stories. By exploiting natural language processing and word embedding techniques, our framework first identifies both the use case domain and the machine learning task to be performed in the system being developed; afterward, it recommends which are the context-specific sensitive features to be considered during the implementation. We assess the capabilities of ReFair by experimenting it against a synthetic dataset---which we built as part of our research---composed of 12,401 User Stories related to 34 application domains. Our findings showcase the high accuracy of ReFair, other than highlighting its current limitations.
@inproceedings{paperbot11247,
title = {ReFAIR: Toward a Context-Aware Recommender for Fairness Requirements Engineering},
author = {Carmine Ferrara and Francesco Casillo and Carmine Gravino and Andrea De Lucia and Fabio Palomba},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3639185}
}