CRII: SCH: A Computational Framework for Fair Public Health-Related Decisions

社交媒体是日常生活的一部分。一篇关于大学毕业的帖子分享了喜悦的一项重大成就。向朋友通报家人去世的消息可能是一种宣泄。与一位失散已久的朋友进行一次简单的互动，就能唤起对更简单时代的异想天开的童年记忆。鉴于社交媒体的广泛使用，它已被广泛用于与健康相关的广泛关键应用，包括但不限于疾病爆发的早期检测、提取和监测药物不良反应、测量吸烟等健康行为，以及挖掘个人的心理和身体健康。最终，检测和治疗心理和身体健康问题的人可能很快就会在他们已经居住的社交媒体平台上遇到个人。在将这些应用程序集成到从公共政策到个人健康决策的决策过程之前，了解这些工具在现实环境中的表现是至关重要的。具体地说，决定必须在所有因素上都是公平的，如年龄、性别、种族、民族和经济地位。该项目的主要创新之处在于，它能够衡量这些公共卫生监测系统在许多代表性不足的群体中的公平性。总体而言，如果目标是识别和对待在线空间中的个人，或者基于社交媒体数据做出政策决策，我们必须衡量工具的公平性。否则，不道德地使用有偏见的工具可能会增加许多代表性不足群体的健康差距。这个项目将引入一个新的框架来衡量公共卫生监测系统的公平性。主要的挑战是，衡量代表性不足群体的公平性是困难的，因为他们很少出现在标准数据集中，或者更糟糕的是，根本没有出现。此外，及时地为所有感兴趣的人口统计因素的数据添加注释既昂贵又具有挑战性。该项目旨在通过两种方式解决这一限制。首先，它将使用样式传递来生成合成数据，以模拟由代表不足的群体生成的文本的词汇、句法和语义特征。将使用特定群体的合成数据来克服数据稀疏性问题，以衡量公平性。其次，虽然衡量标准人口因素的公平性至关重要，但了解工具对特定社区的表现也是至关重要的。因此，样式转换方法将被扩展以生成特定于地理的文本。主要的挑战将是扩展到大量的地点。该项目将利用对抗性学习的最新进展来应对这一挑战。最后，该项目将通过发布实现该奖项产生的工具和技术的开源软件来影响更广泛的人工智能社区。此外，公职人员将获得易于使用的工具，这些工具描述个别系统的使用如何对特定社区产生不利影响。更重要的是，这些工具将帮助官员对社交媒体产生的数据做出明智的决定。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Social media is a part of everyday life. A post about college graduation shares the joy of a significant accomplishment. Informing friends about a death in the family can be cathartic. A simple interaction with a long-lost friend can bring back whimsical childhood memories of simpler times. Given social media's widespread use, it has been used in a broad range of critical health-related applications, including, but not limited to, the early detection of disease outbreaks, extracting and monitoring adverse drug reactions, measuring health behaviors such as smoking use, and mining individual's mental and physical health. Eventually, the detection and treatment of mental and physical health problems may soon meet individuals in the social media platforms they already inhabit. Before these applications are integrated into decision-making processes from public policy to personal health decisions, it is essential to understand how these tools perform in real-world environments. Specifically, decisions must be fair across all factors, such as age, gender, race, ethnicity, and economic status. The main novelty of this project will be in its capacity to measure the fairness of these public health monitoring systems across many underrepresented groups. Overall, if the goal is to identify and treat individuals in online spaces or make policy decisions based on social media data, we must measure the fairness of the tools. Otherwise, the unethical use of biased tools may increase health disparities for many underrepresented groups.This project will introduce a novel framework for measuring the fairness of public health monitoring systems. The major challenge is that measuring the fairness of underrepresented groups is difficult because they rarely appear in standard datasets, or worse, do not appear at all. Moreover, it is both costly and challenging to annotate data for all demographic factors of interest in a timely manner. This project aims to address this limitation in two ways. First, it will use style transfer to generate synthetic data that emulates the lexical, syntactic, and semantic characteristics of text generated by underrepresented groups. Synthetic data for specific groups will be used to overcome the issues of data sparsity to measure fairness. Second, while it is crucial to measure fairness across standard demographic factors, it is also essential to understand how tools will perform for specific communities. Therefore, style transfer methods will be expanded to generate geographic-specific text. The major challenge will be scaling to a large number of locations. This project will address this challenge by taking advantage of recent advances in adversarial learning. Finally, the project will impact the broader AI community via the release of open-source software that implements the tools and techniques this award generates. Moreover, public officials will gain access to easy-to-use tools that describe how the use of individual systems can adversely impact specific communities. More importantly, the tools will help officials make informed decisions about data generated from social media.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.