Digital Discrimination in the Sharing Economy: Evidence, Policy, and Feature Analysis

Applications (apps) of the Digital Sharing Economy (DSE), such as Uber, Airbnb, and TaskRabbit, have become a main facilitator of economic growth and shared prosperity in modern-day societies. However, recent research has revealed that the participation of minority groups in DSE activities is often hindered by different forms of bias and discrimination. Evidence of such behavior has been documented across almost all domains of DSE, including ridesharing, lodging, and freelancing. However, little is known about the under- lying design decisions of DSE systems which allow certain demographics of the market to gain unfair advantage over others. To bridge this knowledge gap, in this dissertation, we investigate the problem of digital discrimination from a software engineering point of view. To develop an in-depth understanding of the problem, we first synthesize existing evidence on digital discrimination from interdisciplinary literature. We then analyze online user feedback, available on social media channels, to assess end-users’ awareness of discrimination issues affecting their DSE apps. We then introduce a novel protocol for drafting and evaluating nondiscrimination policies (NDPs) in the DSE market. Our objective is to assist DSE developers with drafting high quality and less ambiguous NDPs. Finally, we propose and evaluate a modeling framework for representing discrimination concerns affecting popular DSE apps along with their relations (synergies and tradeoffs) to other system features and user goals. Our objective is to visualize such complex domain knowledge using formal notations that software developers can easily understand, communicate, and utilize as an integral part of their app design process. The impact of the proposed research will extend to the entire population of DSE workers, targeting the deep racial and regional disparities in the DSE market and helping people in resource-constrained communities to overcome key barriers to participation and adaptation in one of the fastest growing software ecosystems in the world

Using GitHub in Large Software Engineering Classes: An Exploratory Case Study

GitHub has been recently used in Software Engineering (SE) classes to facilitate col- laboration in student team projects. The underlying tenet is that the technical and social feature of GitHub can help students to communicate and collaborate more effectively as a team as well as help teachers to evaluate individual student contribution more objectively. To shed more light on this, in this case study, we explore the benefits and drawbacks of using GitHub in SE classes. Our study is conducted in a software engineering class of 91 students divided into 18 teams. Our research method includes an entry and an exit surveys and a qualitative analysis of students’ commit behavior throughout the period of the project. Our findings show that a) enforcing GitHub in SE classes can be an effective approach for enhancing students’ skills in configuration management and version control, and b) despite the steep learning curve, most teams managed to optimize their commit behavior over time. In terms of student evaluation, our analysis exposed the risks of using GitHub for individual effort assessment. The work in this paper provides several valuable insights for researchers and makes several recommendations for practitioners (teachers) about integrating GitHub in SE classes

Annotating Privacy Policies in the Sharing Economy

Applications (apps) of the Digital Sharing Economy (DSE), such as Uber, Airbnb, and TaskRabbit, have become a main enabler of economic growth and shared prosperity in modern-day societies. However, the complex exchange of goods, services, and data that takes place over these apps frequently puts their end-users' privacy at risk. Privacy policies of DSE apps are provided to disclose how private user data is being collected and handled. However, in reality, such policies are verbose and difficult to understand, leaving DSE users vulnerable to privacy intrusive practices. To address these concerns, in this paper, we propose an automated approach for annotating privacy policies in the DSE market. Our approach identifies data collection claims in these policies and maps them to the quality features of their apps. Visual and textual annotations are then used to further explain and justify these claims. The proposed approach is evaluated with 18 DSE app users. The results show that annotating privacy policies can significantly enhance their comprehensibility to the average DSE user. Our findings are intended to help DSE app developers to draft more comprehensible privacy policies as well as help their end-users to make more informed decisions in one of the fastest growing software ecosystems in the world