CAREER: Understanding and Advancing Fair Representation in Algorithmic Systems

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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.