Human-Centered Data Science Lab

These several recent years have seen the rise of crowdsourcing as an exciting new tool for getting things done. For many, it was a way to get tedious tasks done quickly, such as transcribing audio. For many others, it was a way to get data: labeled image data, transcription correction data, and so on. But there is also a layer of meta-inquiry: what constitutes crowdsourcing? Who is in the crowd, and why? What can they accomplish, and how might the software that supports crowdsourcing be designed in a way to help them accomplish more?

Each of the last two conferences I have attended, CSCW2012 and UIST2011, had a “crowdsourcing session,” spanning a range of crowdsourcing-related research. But only a short while before that, the far bigger CHI conference contained only one or two instances of “crowdsourcing papers.” So what happened in the last few years?

At some point in the last decade, crowdsourcing emerged both as a method for getting lots of tedious work done cheaply, and a field of inquiry that resonated with human-computer interaction researchers. Arguably, this point historically coincided with the unveiling of Amazon Mechanical Turk platform, which allowed employers, or “requesters,” to list small, low-paid tasks, or “human-intelligence tasks (HITs)” for anonymous online contractors, or “workers,” to complete. In Amazon’s words, this enabled “artificial artificial intelligence” – the capacity to cheaply get answers to questions that cannot be automated.

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Professor and SCCL director Cecilia Aragon and her colleagues received the best paper award at the IEEE/IAPR International Conference on Biometrics in India this weekend, for their paper, “Biometric Authentication via Oculomotor Plant Characteristics.”

Michael Brooks, a member of SCCL, along with collaborators Jevin West, Carl Bergstrom, and Cecilia Aragon, submitted their “hoptree” navigation system to the University of Washington Center for Commercialization. The hoptree assists users with browsing large hierarchical data sets (including file systems, web sites, or other trees) by displaying a branching navigation history over the hierarchy.

In Russian, there is a name for a light blue, and a name for a dark blue, but not for what English-speaking people call blue. Indeed, language shapes our understanding of color differences and categories.

Although it can be entertaining to think about how other factors, like gender, affect categorization of color, the survey deployed and analyzed by xkcd’s Randall Munroe showed that chromosomal gender mostly doesn’t matter (and that nobody can spell fuchsia).

Color is used widely in scientific visualization. The influence of language/culture on color perception impacts the interpretation of such visualization to some extent, but other measures can be taken to improve the use of color in visualizations:

• Using variations across the color spectrum? Remember how cyan is just brighter than blue? You can adjust for that!
• Using discrete colors, either along a single hue, or across many distinct hues? You can find sets of distinct, evenly-spaced colors to use!

Written by lab member, student, and deliciousness enthusiast Katie Kuksenok. Read more of her posts here on the SCCL blog, or on her own research blog, interactive everything.