Ozymandias: Using Artificial Intelligence and Machine Learning to Map Urban Power Structures and Produce Fairer Results for All Many digital applications aim to use technology to make better-informed decisions on issues like mobility, affordable housing, public safety, education, and livability of communities. However, even when these applications help inform better outcomes, the results may not end up being significantly different, in part because the power structures that make decisions do not respond to these inputs. Ozymandias proposes to utilize Artificial Intelligence and Machine Learning to view municipal decision-making processes and map power structures, index relationships, quantify influence and uncover bias in the murky backwaters of representative democracy. Using Portland, Maine as a pilot area, the project will use this information to improve the chances to make meaningful change. The goal is to trace the invisible power structures that shape how projects are selected, approved, delayed, or stymied. The team will work with community groups to enable data transparency, increasing public engagement in the creation of more equitable and fair decisions.

Team: Jeff Levine, Addy Smith-Reiman, Liz Trice, Chris Miller, Tom Nosal, Tim Wallace

Co-HATY Accelerator: Digitally mediated co-housing for internally displaced peoples in Ukraine Co-HATY Accelerator proposes to develop a co-housing development tool with local community partners in Ukraine. Current mechanisms for identifying housing during disaster events prioritize temporary housing; Co-HATY Accelerator instead identifies unused or underused housing in existing areas that can be rehabilitated for inhabitation at low cost and during a rapid timeframe, providing quality long or longer-term housing for displaced individuals. Co-HATY Accelerator utilizes a digital platform to establish collaboration between actors involved in housing creation and affected communities; promoting resident connectivity to existing communities. Co-HATY aims to serve hundreds of people displaced in the Russian war against Ukraine, identifying optimal locations for potential building renovation projects by facilitating a process that emphasizes community building. The Co-HATY’s site identification will occur through four phases including: crowdsourcing, social media scraping, visual AI, and GIS analysis. Co-HATY will also occupy a renovated building to provide actual emergency housing for IDPs. The project blends an inclusive co-design process for co-housing site selection and development, with the renovation of thirty units to provide emergency housing that will be hosted on the Co-HATY platform. Co-HATY aims to provide a new model for participatory design within disaster responses.

Team: Brent D. Ryan, Kateryna Lopatiuk, Anastasiya Ponomaryova, Yegor Vlasenko, Herman Mitish, Maryna Osnach, Marjo van Schaik, Maria Gryshenko, Anna Pashynska, Bohdan Volynsky