help研究问题机器学习公平性与数据开发实践在帮助AI/ML模型改善数据质量的同时,如何更公平地分配数据标注员的利益?CHI '22Whose AI Dream? In search of the aspiration in data annotation
lightbulb现实痛点机器学习公平性与数据开发实践数据标注员面临高压、低稳定性和职业发展受限的问题。CHI '22Whose AI Dream? In search of the aspiration in data annotation
help研究问题机器学习公平性与数据开发实践开源公平工具包在功能和适用性上存在哪些差距?CHI '21The Landscape and Gaps in Open Source Fairness Toolkits
help研究问题机器学习公平性与数据开发实践业界从业者对公平工具包的主要使用需求是什么?CHI '21The Landscape and Gaps in Open Source Fairness Toolkits
help研究问题机器学习公平性与数据开发实践现有开源公平工具包如何能够更好地适应工业实际场景?CHI '21The Landscape and Gaps in Open Source Fairness Toolkits
lightbulb现实痛点机器学习公平性与数据开发实践开发者无法选用适合工业需求的公平工具包。CHI '21The Landscape and Gaps in Open Source Fairness Toolkits
help研究问题机器学习公平性与数据开发实践如何向用户解释机器学习系统的训练数据以提高透明度?CHI '21Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency
help研究问题机器学习公平性与数据开发实践数据层面的解释如何影响用户对系统公平性和可信度的感知?CHI '21Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency
help研究问题机器学习公平性与数据开发实践哪些训练数据类别适合向用户传达以帮助识别潜在偏见?CHI '21Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency
lightbulb现实痛点机器学习公平性与数据开发实践用户无法理解机器学习系统的训练数据来源和潜在偏见,难以信任系统决策。CHI '21Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency
help研究问题机器学习公平性与数据开发实践现有的机器学习公平性工具是否能够满足实践中的需求?CHI '21Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits
help研究问题机器学习公平性与数据开发实践在公平性工具中,哪些功能对从业者的决策影响最大?CHI '21Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits
help研究问题机器学习公平性与数据开发实践如何设计公平性评价指标以优化用户体验和实际效用?CHI '21Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits
lightbulb现实痛点机器学习公平性与数据开发实践从业者难以使用现有公平性工具分析模型偏差,做出实用决策。CHI '21Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits