Joel Ross

Crowdsourcing is rapidly emerging as a new paradigm for solving problems: rather than having a single expert individual or group find a solution to a problem, the task is instead given to a crowd--a large group of amateur and expert users alike. Indeed, crowdsourcing methods are currently allowing humans to solve difficult computation problems--such as image identification and natural language processing--in place of computers. I propose to explore the potential social, technological, and environmental impacts of using crowdsourcing to perform computational tasks, as well as to develop technological innovations that can support this process. By deepening the understanding of this mode of interaction, my research seeks to enable novel and more effective ways of solving significant social and computational problems.

Amazon.com's Mechanical Turk (http://www.mturk.com) is a micro-task marketplace where requesters can post tasks that, while quick and easy for a human, are very difficult for computers to perform correctly and efficiently--for example, identifying an object in an image or categorizing data. In this project, we exploring the question of who are the Mechanical Turkers, looking to identify the kinds of people who participate in MTurk (both as workers and as requesters) and understand the reasons for their participation. We are also considering the intersubjectivity of this community, looking at what workers and requesters think of one another in order to better understand the social relationships in the rising area of crowdsourcing.

Mobile devices such as mobile phones are becoming more ubiquitous and gaining more capabilities, leading to their increasing use as portable media players. However, the great majority of produced visual media is targeted towards devices with larger displays and greater hardware abilities, making it difficult for this media to be presented on mobile devices. These limitations will only increase as multimedia becomes more and more interactive, such as with increasingly popular mobile games. In this project, we are exploring methods for retargeting visual media to mobile devices, and considering how such methods can be applied to interactive games.

Carbon footprint is fast becoming an accepted measure of people's environmental impact. By understanding the make-up of their carbon footprint, people can understand how various aspects of their lifestyle affect the environment. Currently the most popular way of determining carbon footprint is to use one of many online "calculators" that estimate impact based on certain factors, such as how much a person travels or what size home they live in. But these calculators remain inaccurate, providing wide averages rather than more specific information. Thus we are developing an extendable carbon footprint calculator that will allow for easier and more accurate carbon footprint estimates by combining data mining and participatory culture techniques.

As mobile devices become more ubiquitous, we are likely to see the spread of computational systems for urban sensing and other forms of context identification. However, there has not yet been sufficient consideration of how to reduce the potential environmental impacts of such systems. In an attempt to combat these foreseen impacts, we apply the concept of sustainable sharing to urban sensing, envisioning a distributed mode of interaction in which users can automatically share locally sensed information with other collocated devices. We present simulations and survey findings that demonstrate the viability of such a peer-to-peer interaction, as well as indicate key challenges for deployment such as privacy concerns. Due to energy requirements, sensor sharing using current technology may not yet make urban sensing more sustainable. However, our findings suggest that sharing capabilities and functionality can eventually be a viable interaction mode for environmentally sustainable urban sensing.

More and more, people desire information that enables them to reduce their environmental impact during their daily lives. Although numerous services that offer this information have appeared, current "expert"-driven methods are unable to deal with the vast scope of determining the environment impact of every consumer product. Furthermore, few of these services are able to provide information whenever a user my desire it, such as at the point of purchase. In this paper, we suggest that participatory culture may allow for large-scale environmental impact information via distributed data gathering and analysis. We developed a vision of ubiquitous environmental impact information enabled by participatory culture, in which information for all products is provided by all people and is available at all times.
The prototype can be found at http://www.greenscanner.net