Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours

Rideshare platforms, when assigning requests to drivers, tend to maximize profit for the system and/or minimize waiting time for riders. Such platforms can exacerbate biases that drivers may have over certain types of requests. We consider the case of peak hours when the demand for rides is more than the supply of drivers. Drivers are well aware of their advantage during the peak hours and can choose to be selective about which rides to accept. Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e.g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver. Such a system can be highly unfair to riders. However, increasing fairness might come at a cost of the overall profit made by the rideshare platform. To balance these conflicting goals, we present a flexible, non-adaptive algorithm, \lpalg, that allows the platform designer to control the profit and fairness of the system via parameters $\alpha$ and $\beta$ respectively. We model the matching problem as an online bipartite matching where the set of drivers is offline and requests arrive online. Upon the arrival of a request, we use \lpalg to assign it to a driver (the driver might then choose to accept or reject it) or reject the request. We formalize the measures of profit and fairness in our setting and show that by using \lpalg, the competitive ratios for profit and fairness measures would be no worse than $\alpha/e$ and $\beta/e$ respectively. Extensive experimental results on both real-world and synthetic datasets confirm the validity of our theoretical lower bounds. Additionally, they show that \lpalg under some choice of $(\alpha, \beta)$ can beat two natural heuristics, Greedy and Uniform, on \emph{both} fairness and profit

Answering a calling: medical professionals' digital careers in crowdsourcing

One of the most striking trends in individuals’ careers over the last decade has been the dramatic increase in the proportion of the labor force working beyond their employers’ physical boundaries because of the digital revolution in the gig economy. This trend has drawn much attention in the changing nature of work, workplace and careers. However, little empirical research has explored how and why individuals behave in the interface between online platforms and traditional organizations. In my dissertation, I explore these questions by studying medical professionals’ digital careers in the Chinese healthcare crowdsourcing industry, also known as “mobile doctors.” First, by analyzing approximately 240-hour observations and 43 interviews with Chinese physicians, I identify a key issue in this new career – time conflict between crowdsourcing and traditional work. The findings show that physicians respond to time conflict in a variety of ways, including time theft, an essential yet under-researched construct in the crowdsourcing literature which reflects the tension between traditional work and crowdsourcing. Second, by analyzing archival data of 4,034 doctors’ 3.1 million time records on a Chinese healthcare platform across half a year, I show that time theft for crowdsourcing is related to the traditional work context, including hospitals’ boundary control and offline crowd worker social groups. Finally, I further explore, via interview data, why such seemingly costly and deviant time theft is adopted by mobile doctors. The findings reveal that medical professionals assume the extra burden of working for crowdsourcing with the hope of answering unfulfilled occupational callings in traditional work and adding meaning to their work. Overall, these findings contribute to a better understanding of the shifting nature of work and careers in the digital economy by documenting and explaining mobile doctors’ participation in this new world of work

Crowdsourcing Literal Review

Our user feedback framework requires some robust techniques in order to tackle the scalability issue of schema matching network. One approach is employing crowd-sourcing/human computation models. Crowdsourcing is one of cutting-edge research areas which involves human computers to perform pre-defined tasks. In this literal review, we try to explore some certain concepts such as task, work-flow, feedback aggregation, quality control and reward system. We show that there are a lot of aspects which can be integrated into our user feedback framework

Crowdfunding in the accommodation realm and pandemic times: The resilient case of CleanBnB

Crowdfunding campaigns have recently promoted a range of new business models in different contexts. This study investigates crowdfunding in the accommodation realm from a socio-cultural perspective and across its international dynamics. Drawing on complexity theory, the study explores the successful case of CleanBnb, the leading crowdfunded company in the Italian short-term rental market, and informs hospitality actors on the coping strategies implemented to challenge the Covid-19 pandemic. The study adopts a case study approach, combining primary data collected through an in-depth interview of the CEO and the analysis of secondary data from different company reports. The results highlight the importance of (1) business diversification, (2) grouping opportunities and (3) widening of service range as key factors in pandemic business survival for start-ups operating in the accommodation realm. The study finally discusses post-pandemic scenarios for both the traditional hotel industry and sharing economy operators by offering managerial insight

Design and Implementation of a Scalable Crowdsensing Platform for Geospatial Data

In the recent years smart devices and small low-powered sensors are becoming ubiquitous and nowadays everything is connected altogether, which is a promising foundation for crowdsensing of data related to various environmental and societal phenomena. Very often, such data is especially meaningful when related to time and location, which is possible by already equipped GPS capabilities of modern smart devices. However, in order to gain knowledge from high-volume crowd-sensed data, it has to be collected and stored in a central platform, where it can be processed and transformed for various use cases. Conventional approaches built around classical relational databases and monolithic backends, that load and process the geospatial data on a per-request basis are not suitable for supporting the data requests of a large crowd willing to visualize phenomena. The possibly millions of data points introduce challenges for calculation, data-transfer and visualization on smartphones with limited graphics performance. We have created an architectural design, which combines a cloud-native approach with Big Data concepts used in the Internet of Things. The architectural design can be used as a generic foundation to implement a scalable backend for a platform, that covers aspects important for crowdsensing, such as social- and incentive features, as well as a sophisticated stream processing concept to calculate incoming measurement data and store pre-aggregated results. The calculation is based on a global grid system to index geospatial data for efficient aggregation and building a hierarchical geospatial relationship of averaged values, that can be directly used to rapidly and efficiently provide data on requests for visualization. We introduce the Noisemap project as an exemplary use case of such a platform and elaborate on certain requirements and challenges also related to frontend implementations. The goal of the project is to collect crowd-sensed noise measurements via smartphones and provide users information and a visualization of noise levels in their environment, which requires storing and processing in a central platform. A prototypic implementation for the measurement context of the Noisemap project is showing that the architectural design is indeed feasible to realize