There are a squillion ways that tech companies use your data to affect how they deliver you services (for better or worse). On top of the data you consciously or inadvertently provide, companies have access to data they buy from data brokers, who combine data from many sources. Very few of the ways in which these data are used are well understood or well known, because they are usually kept behind closed doors.
“Personalisation” algorithms relying on this data can lead to many negative outcomes as well as the touted positive ones. We believe that algorithms like this should be open sourced. We are therefore interested in building applications that demonstrate either a better way of using or exposing the data, or the extent of the potential negative impacts. We want to make clear why these algorithms should be made more transparent.
The following are some examples of existing projects that you might like to use for inspiration!
Alternative navigation app helps you find something by looking for something else. Algorithm being challenged: journey planners that are usually optimised for speed or distance.
- Shepard, M. (2011). Sentient City: Ubiquitous Computing, Architecture, and the Future of Urban Space. Cambridge, MA: MIT Press.
Journey planner to optimise discovery. Algorithm being challenged: journey planners that are usually optimised for speed or distance.
- citylab.com article
- Traunmueller, M., & Fatah gen. Schieck, A. (2013). Introducing the space recommender system: how crowd-sourced voting data can enrich urban exploration in the digital era. In Proceedings of C&T’13. ACM, USA, 149-156.
A prototype web app for visualising the diverse ecosystem of creativity and innovation spaces. Algorithm being challenged: Recommender systems and social media that suggest places where your existing friends hang out.
- Casadevall, D., Foth, M., & Bilandzic, A. (2018, Dec 5-7). Skunkworks Finder: Unlocking the Diversity Advantage of Urban Innovation Ecosystems. In Proceedings of the 30th Australian Conference on Human-Computer Interaction (OZCHI), Melbourne, VIC. New York, NY: ACM.
News curation fighting filter bubbles by Eli Pariser. Algorithm being challenged: News services such as Apple News that only show you the news you want to see, not the news worth seeing.
- Pariser, E. (2011). The Filter Bubble: What The Internet Is Hiding From You. Penguin UK. See also TED Talk video
Familiar Strangers and Jabberwocky
The individuals that we regularly observe but do not interact with. Algorithm being challenged: All social media platforms that optimise for homophily, since they connect you with people you are friends with, but do not usually support serendipitous encounters with familiar strangers.
- Eric Paulos and Elizabeth Goodman. 2004. The familiar stranger: anxiety, comfort, and play in public places. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '04). ACM, New York, NY, USA, 223-230.
London is Changing
Billboards displaying messages from people who have been displaced from the neighbourhood where the billboard is located in London. Algorithm being challenged: Billboards that display targeted advertisement that appeals to passers-by of a local geographic area.
- Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
- Foth, M., Mitchell, P., & Estrada Grajales, C. (2018). Today’s Internet for Tomorrow’s Cities: On Algorithmic Culture and Urban Imaginaries. In J. Hunsinger, L. Klastrup, & M. Allen (Eds.), Second International Handbook of Internet Research. Berlin, Germany: Springer.
- Dourish, P. (2016). Algorithms and their others: Algorithmic culture in context. Big Data & Society, 3(2).
- Striphas, T. (2015). Algorithmic culture. European Journal of Cultural Studies, 18(4-5), 395–412.