多模态AI中的偏差：公平自动招聘的试验平台
Bias in Multimodal AI: Testbed for Fair Automatic Recruitment
摘要：
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。事实上，许多相关的自动化系统已被证明基于敏感信息做出决策或区分某些社会群体（例如，用于个人识别的某些生物识别系统）。为了研究当前基于异构信息源的多模态算法如何受到数据中敏感元素和内部偏差的影响，我们提出了一个虚拟的自动招聘测试平台：FairCVtest。我们训练自动招聘算法使用一组多模态合成配置文件有意识地与性别和种族偏见评分。FairCVtest显示了这种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见结合起来。最后，我们提出了一个列表，最近的作品开发技术，能够消除敏感信息的决策过程中的深度学习架构。我们已经使用其中一种算法（SensitiveNets）来实验识别感知学习，以消除我们的多模态AI框架中的敏感信息。我们的方法和结果展示了如何生成更公平的基于AI的工具，特别是更公平的自动化招聘系统。
英文摘要：
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. In fact, many relevant automated systems have been shown to make decisions based on sensitive information or discriminate certain social groups (e.g. certain biometric systems for person recognition). With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious automated recruitment testbed: FairCVtest. We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases. FairCVtest shows the capacity of the Artificial Intelligence (AI) behind such recruitment tool to extract sensitive information from unstructured data, and exploit it in combination to data biases in undesirable (unfair) ways. Finally, we present a list of recent works developing techniques capable of removing sensitive information from the decision-making process of deep learning architectures. We have used one of these algorithms (SensitiveNets) to experiment discrimination-aware learning for the elimination of sensitive information in our multimodal AI framework. Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.
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。
