CAREER: Understanding and Advancing Fair Representation in Algorithmic Systems

由于在信用评分、招聘和治安等领域存在偏见、歧视和虚假陈述的报道，理解算法系统的社会后果已成为政策制定者、工程师和学者的关键关切。这个项目将专注于了解普通公民是如何受到算法系统的影响、应对和挑战的。研究人员将通过使用定性、历史和人种学的方法来了解人们如何与网络搜索引擎互动，搜索引擎优化模式的影响，以及那些受到算法系统负面影响的人的情况可能如何得到改善。除了主要的研究，这项提案还将资助研究生多学科团队的干预，以教育下一代专家，以解决算法系统中的公平表示和责任问题。算法系统是现代生活中普遍存在的一个方面，如网络搜索、招聘决策、信用排名和确定医疗保险单的成本。当算法系统产生的数据和信息被证明是不准确、有偏见或歧视性的时，就会出现重要的社会问题。这种情况变得更加复杂，因为算法系统是“黑匣子”，其内部工作通常由于专有原因而保密。这个项目将调查算法系统如何塑造普通公民的生活，以及公民如何应对和挑战他们。研究一将深度访谈和自我反省相结合，以了解数据对象的生活经历。研究2将结合文档分析和口述历史访谈，详细介绍搜索引擎优化的历史。研究3将依靠人种志参与者观察来调查算法设计的伦理。然后，这些研究的数据和见解将被用于创建教育干预措施，旨在向相关领域的研究生告知算法系统中的公平代表和责任。该奖项反映了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。

Understanding the social consequences of algorithmic systems has become a key concern for policy makers, engineers, and academics due to reports of bias, discrimination, and misrepresentation in areas such as credit scoring, hiring, and policing. This project will focus on understanding how ordinary citizens are affected by, cope with, and challenge algorithmic systems. The investigator will do so by using qualitative, historical, and ethnographic methods to understand how people interact with web search engines, the effects search engine optimization schemas, and how the situation of those who have been negatively affected by algorithmic systems might be improved. In addition to primary research, this proposal will fund an intervention among multidisciplinary teams of graduate students to educate next generation experts to address issues of fair representation and accountability in algorithmic systems.Algorithmic systems are a pervasive aspect of modern life in areas such as web searches, hiring decisions, credit rankings, and determining the cost of health insurance policies. Important social issues arise when the data and information produced by algorithmic systems turns out to be inaccurate, biased, or discriminatory. This situation is made more complicated because algorithmic systems are "black boxes," the inner workings of which are often kept secret for proprietary reasons. This project will investigate how algorithmic systems shape the lives of ordinary citizens, and how citizens work to cope with and challenge them. Study 1 will combine in-depth interviews and self-reflections to understand the lived experiences of data subjects. Study 2 will blend document analysis and oral history interviews to detail the history of search engine optimization. Study 3 will rely on ethnography participant observation to investigate the ethics of algorithmic design. Data and insights from these studies will then be used to create an educational intervention aimed at informing graduate students from relevant fields about fair representation and accountability in algorithmic systems.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.