一句话：系统梳理2018–2025年以数据集为中心的偏见识别与缓解方法，强调持续、有人工监督的主动治理。
该文针对算法公平性中数据偏差导致的不公平结果，系统综述2018至2025年间以数据集为中心的偏见处理研究。作者提出研究问题，设计检索策略、研究筛选、数据提取与分析协议，梳理公平维度、度量指标、预处理与处理中方法、人工参与、效果与副作用及伦理考量。证据表明，融入人工监督的主动迭代方法对缓解算法歧视、提升AI可信度至关重要；忽视这些因素可能在高风险场景中放大对边缘群体的偏见，造成社会危害。
Algorithmic fairness refers to the equitable treatment of users regardless of their identity, demographics, or other personal characteristics, whereas biased AI may produce unfair outcomes when the input data reflect imbalances or underrepresentation of certain groups. This systematic literature review analyzes studies published between 2018 and 2025 that report approaches for handling bias in data and datasets, with particular attention to dataset-centered techniques. We examine how bias is identified, mitigated, and reflected in model outcomes. Our method defines research questions and details the search strategy, study selection procedure, and protocols for data extraction and analysis. The review maps fairness dimensions, the metrics employed, pre-processing and in-processing methods, the role of human participation, observed effects and side effects, and the ethical considerations that inform practice. The evidence suggests that proactive, iterative approaches incorporating human oversight are essential for mitigating algorithmic discrimination and enhancing the trustworthiness of AI systems. Neglecting these considerations risks significant societal harm and the perpetuation of bias against marginalized groups in high-stakes contexts, particularly in an era where AI is increasingly widespread.
@inproceedings{LecaBS26,
title = {A Systematic Review of Bias Detection and Dataset Improvement Methods for Fair AI Systems},
author = {Matheus de Morais Leca and Mariana Bento and Ronnie de Souza Santos},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3806705}
}