lightbulb现实痛点机器学习公平性与数据开发实践现有方法难以在非IID数据中均衡保护隐私与实现机器学习算法的多维公正。UbiComp '23Inflorescence: A Framework for Evaluating Fairness with Clustered Federated Learning
lightbulb现实痛点机器学习公平性与数据开发实践算法设计中的偏见可能导致少数群体在就业、医疗等领域受到不公平对待。CHI '25Preventing Harmful Data Practices by using Participatory Input to Navigate the Machine Learning Multiverse
lightbulb现实痛点机器学习公平性与数据开发实践AI模型在少数群体上常表现较差,影响公平性和可靠性。CHI '25Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems
lightbulb现实痛点机器学习公平性与数据开发实践GLAM领域的性别偏见可能误导公众对历史的理解。CHI '25Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals
lightbulb现实痛点机器学习公平性与数据开发实践AI设计未充分考虑受影响群体的反馈,易导致偏见和不公平。CSCW '24Empowering and Centering Impacted Stakeholders in AI Design
lightbulb现实痛点机器学习公平性与数据开发实践联邦学习模型易受用户上传恶意梯度的后门攻击,不公平性降低用户体验。UbiComp '24SARS: A Personalized Federated Learning Framework Towards Fairness and Robustness against Backdoor Attacks
lightbulb现实痛点机器学习公平性与数据开发实践普适计算中的技术可能因偏见而对某些群体产生歧视或失准。UbiComp '24FairComp: 2nd International Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing