FairCVtest演示：在公平自动招聘中使用测试床了解多模态学习中的偏差
FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment
摘要：
为了研究当前基于异构信息源的多模态AI算法如何受到数据中敏感元素和内部偏见的影响，该演示器在基于简历的自动招聘测试平台上进行了实验：FairCVtest。如今，决策算法在社会中的存在正在迅速增加，同时对其透明度以及这些算法成为新的歧视来源的可能性的担忧也在增加。此演示展示了招聘工具背后的人工智能（AI）从非结构化数据中提取敏感信息的能力，并以不受欢迎（不公平）的方式将其与数据偏差结合起来加以利用。此外，该演示包括一个新的算法（SensitiveNets），用于识别感知学习，消除了我们的多模态AI框架中的敏感信息。
英文摘要：
With the aim of studying how current multimodal AI algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, this demonstrator experiments over an automated recruitment testbed based on Curriculum Vitae: FairCVtest. The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new sources of discrimination are arising. This demo shows the capacity of the Artificial Intelligence (AI) behind a recruitment tool to extract sensitive information from unstructured data, and exploit it in combination to data biases in undesirable (unfair) ways. Aditionally, the demo includes a new algorithm (SensitiveNets) for discrimination-aware learning which eliminates sensitive information in our multimodal AI framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
