探究开发者为何使用公平性工具，发现效果感知和习惯是关键驱动力。
From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?
- 基于技术接受模型，分析开发者使用公平性工具的动机
- 性能预期与使用习惯显著影响工具采纳意愿和实际使用
- 适合关注AI伦理落地、工具设计优化的研究者与实践者
随着机器学习系统在各行业的广泛应用，其公平性与偏见问题日益突出。为缓解模型偏见，公平性工具包应运而生，成为应对伦理挑战的关键手段。然而，在软件开发场景中，这些工具的采纳仍缺乏深入研究，尤其是个体认知与行为因素的影响尚未明晰。本研究基于统一技术接受与使用理论（UTAUT2），通过面向软件从业者的大规模问卷调查，采用偏最小二乘结构方程建模（PLS-SEM）分析数据。结果表明，性能预期与使用习惯是驱动公平性工具采纳的核心因素。研究建议：提升工具易用性，将偏见缓解流程嵌入常规开发流程，并提供持续支持，以帮助从业者切实感受到定期使用的价值。这为促进工具更广泛的应用提供了实证依据。
原文摘要 · Abstract (English)
As the adoption of machine learning (ML) systems continues to grow across industries, concerns about fairness and bias in these systems have taken center stage. Fairness toolkits, designed to mitigate bias in ML models, serve as critical tools for addressing these ethical concerns. However, their adoption in the context of software development remains underexplored, especially regarding the cognitive and behavioral factors driving their usage. As a deeper understanding of these factors could be pivotal in refining tool designs and promoting broader adoption, this study investigates the factors influencing the adoption of fairness toolkits from an individual perspective. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT2), we examined the factors shaping the intention to adopt and actual use of fairness toolkits. Specifically, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from a survey study involving practitioners in the software industry. Our findings reveal that performance expectancy and habit are the primary drivers of fairness toolkit adoption. These insights suggest that by emphasizing the effectiveness of these tools in mitigating bias and fostering habitual use, organizations can encourage wider adoption. Practical recommendations include improving toolkit usability, integrating bias mitigation processes into routine development workflows, and providing ongoing support to ensure professionals see clear benefits from regular use.
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