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Recent work in HCI suggests that users can be powerful in surfacing harmful algorithmic behaviors that formal auditing approaches fail to detect. However, it is not well understood how users are often able to be so effective, nor how we might support more effective user-driven auditing. To investigate, we conducted a series of think-aloud interviews, diary studies, and workshops, exploring how users find and make sense of harmful behaviors in algorithmic systems, both individually and collectively. Based on our findings, we present a process model capturing the dynamics of and influences on users’ search and sensemaking behaviors. We find that 1) users’ search strategies and interpretations are heavily guided by their personal experiences with and exposures to societal bias; and 2) collective sensemaking amongst multiple users is invaluable in user-driven algorithm audits. We offer directions for the design of future methods and tools that can better support user-driven auditing. 

Recently, there have been increasing calls for computer science curricula to complement existing technical training with topics related to Fairness, Accountability, Transparency and Ethics (FATE). In this paper, we present Value Cards, an educational toolkit to inform students and practitioners the social impacts of different machine learning models via deliberation. This paper presents an early use of our approach in a college-level computer science course. Through an in-class activity, we report empirical data for the initial effectiveness of our approach. Our results suggest that the use of the Value Cards toolkit can improve students' understanding of both the technical definitions and trade-offs of performance metrics and apply them in real-world contexts, help them recognize the significance of considering diverse social values in the development and deployment of algorithmic systems, and enable them to communicate, negotiate and synthesize the perspectives of diverse stakeholders. Our study also demonstrates a number of caveats we need to consider when using the different variants of the Value Cards toolkit. Finally, we discuss the challenges as well as future applications of our approach. 

Despite slow adoption in the US, mobile payments are thede facto solution for hundreds of millions of users in China for everything from paying bills to riding buses, from sending virtual "Red Packets'' to buying money-market funds. In this paper, we use the theoretical lens of infrastructure to study users' interactions with ubiquitous mobile payment systems in China, focusing on Alipay and WeChat Pay, the two dominant apps on the market. Based on data from a survey (n=466) and follow-up interviews (n=12) with users in China, we describe the diverse usage patterns across physical, social, and digital ubiquity, and a series of challenges people face. Reflecting on the lessons we learned from the Chinese case -- in particular, problems and pitfalls -- we discuss some implications both for design and for policy. Our findings have important implications for other countries that have been moving towards greater adoption of mobile payments. 

Ensuring effective public understanding of algorithmic decisions that are powered by machine learning techniques has become an urgent task with the increasing deployment of AI systems into our society. In this work, we present a concrete step toward this goal by redesigning confusion matrices for binary classification to support non-experts in understanding the performance of machine learning models. Through interviews (n=7) and a survey (n=102), we mapped out two major sets of challenges lay people have in understanding standard confusion matrices: the general terminologies and the matrix design. We further identified three sub-challenges regarding the matrix design, namely, confusion about the direction of reading the data, layered relations and quantities involved. We then conducted an online experiment with 483 participants to evaluate how effective a series of alternative representations target each of those challenges in the context of an algorithm for making recidivism predictions. We developed three levels of questions to evaluate users’ objective understanding. We assessed the effectiveness of our alternatives for accuracy in answering those questions, completion time, and subjective understanding. Our results suggest that (1) only by contextualizing terminologies can we significantly improve users’ understanding and (2) flow charts, which help point out the direction of reading the data, were most useful in improving objective understanding. Our findings set the stage for developing more intuitive and generally understandable representations of the performance of machine learning models