Data Culture Group

Winter Data Update: 📈🎭🎤📘

Lots of work happening on data theatre, update on csv,conf and C+J events, and my Community Data boko!

Data Theatre Community and Student Workshops

As part of the Data Theatre Collaborative, I’m working on more inclusive and creative approaches to civic deliberation. To discover how community and youth responded to data and theatre activity we ran a series of summer workshops. We learned so much, and had a lot of fun! Read our IEEE Vis Workshop paper to learn more. In parallel the team worked with students at Northwestern to create “The Numbers”, an exploration of how theatre students approached data as source material. The performance included participation, embodied data, data murals, sonification, and more. We’re digesting all we’ve learned and excited next year to bring those together into an original touring community data theatre production about Boston data.

5 actors kneeling on the floor with one standing apart and looking at their backs

Photo: Amelia Cordischi, Directed by: Jonathan Carr, Scenic Design: Sydney Purdue, Lighting Design: Matthew Brian Cost, Costume Design: Jessica Pribble, Sound Design: Aubrey Dube

Finding Academic Watering Holes

As an academic working at the intersection of fields, it can be hard to find spaces to share work and get inspired by others. cvs,conf and C+J are two that I’ve found rewarding in the last few year.

I was so excited this fall to keynote csv,conf,v9. The event gathers open data enthusiasts, data journalists, civic data specialists, and other like-minded people. Watch my keynote online: Make Mirrors, not Windows. Or read some notes from the event I wrote up.

Photo of all the attendees at csv,conf,v9 (source: John Chodacki)

Later this week I’m heading to Miami for the annual Computation + Journalism Symposium. This event brings together journalists and researchers around the theme of computation and its impacts. I’ll be leading a workshop on data embodiment, presenting a short case study paper on digging into bias in AI models, and introducing a few panels.

AI technologies have rapidly moved into business and research applications that involve large text corpora, including computa- tional journalism research and newsroom settings. These models, trained on extant data from various sources, can be conceptualized as historical artifacts that encode decades-old attitudes and stereo- types. This paper investigates one such example trained on the broadly-used New York Times Annotated Corpus to create a multi- label classifier. Our use in research settings surfaced the concerning blacks thematic topic label. Through quantitative and qualitative means we investigate this label’s use in the training corpus, what concepts it might be encoding in the trained classifier, and how those concepts impact our model use. Via the application of explain- able AI methods, we find that the blacks label operates partially as a general “racism detector” across some minoritized groups. However, it performs poorly against expectations on modern examples such as COVID-19 era anti-Asian hate stories, and reporting on the Black Lives Matter movement. This case study of interrogating embedded biases in a model reveals how similar applications in newsroom settings can lead to unexpected outputs that could impact a wide variety of potential uses of any large language model–story dis- covery, audience targeting, summarization, etc. The fundamental tension this exposes for newsrooms is how to adopt AI-enabled workflow tools while reducing the risk of reproducing historical biases in news coverage.

Book Updates

It’s so validating and rewarding to continue to hear feedback about researchers, activists, designers, and journalists who connect with Community Data. If you haven’t yet, grab a copy online or from your library and let me know what you think! I’m open for talk series and happy to offer advice on reading excerpts for courses if you are teaching related topics. I’ll share some a Spring talk schedule soon and hope to be in your area.

book cover for Community Data: Creative Approaches to Empowering People with Information
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