2019 Data Science for Social Good projectsEmily Keller2019-08-13T19:06:09-08:00
ADUniverse: Evaluating the Feasibility of (Affordable) Accessory Dwelling Units in Seattle
Types of accessory dwelling units (click to enlarge any image)
Project leads: Rick Mohler, Associate Professor, Department of Architecture, University of Washington; and Nick Welch, Senior Planner, City of Seattle Office of Planning and Community Development
Data science lead: Joseph Hellerstein
DSSG fellows:Emily A. Finchum-Mason, Yuanhao Niu, Adrian Mikelangelo Tullock, Anagha Uppal
Project Summary: Seattle has the nation’s seventh most expensive housing market and third-largest homeless population despite being only its 18th-largest city. Exacerbating this challenge is the fact that three-quarters of the city’s land zoned for housing is reserved for low-density, detached single- family dwellings that few households can afford.
However, this offers an opportunity to increase Seattle’s affordable housing stock through homeowner-developed accessory dwelling units (ADUs)—small, separate residences within or behind single-family homes. ADUs provide access to high-opportunity neighborhoods, have relatively lower rents, allow older adults to age in place, and provide supplemental income for homeowners. Despite these benefits, fewer than two percent of Seattle’s 135,000 single-family lots currently have an ADU. Regulatory, financial, design, and permitting challenges stymie ADU production. Reducing these barriers can increase production and improve our housing landscape.
This project will advance that goal by providing a citywide feasibility analysis and a prototype web-based interactive tool that estimates parcel-level suitability for a detached ADU given property characteristics, housing submarket, and neighborhood-level socioeconomic conditions. The tool would enable individual homeowners to assess the feasibility of building an ADU, and aid nonprofits and policymakers in exploring ADUs as part of an anti-displacement strategy. In particular, the DSSG team will conduct an analysis for policymakers currently considering the development of a program to provide ADU financing for lower-income homeowners struggling with rising costs in exchange for renting the ADU to a lower-income tenant.
This project will integrate data on factors that influence the physical and financial feasibility of constructing a detached ADU, including property data from the King County Assessor, environmental data from the City of Seattle, real estate market data, and spatial analysis of site conditions. It will involve geospatial analysis, econometric analysis, model building and interface design using a range of public data sources that must be joined and rendered interoperable.
Developing an Algorithmic Equity Toolkit with Government, Advocates, and Community Partners
Project lead: Mike Katell, PhD Candidate, UW Information School
Data science lead: Bernease Herman
Faculty Advisor: Peaks Krafft, Senior Research Fellow, Oxford Internet Institute
Community Engagement Lead: Meg Young, PhD Candidate, University of Washington Information School
DSSG fellows: Corinne Bintz, Vivian Guetler, Daniella Raz, Aaron Tam
Project Summary: In partnership with ACLU of Washington, fellows will create an Algorithmic Equity Toolkit—a set of tools for identifying and auditing public sector algorithmic systems, especially automated decision-making and predictive technologies. Extensive evidence demonstrates harms across highly varied applications of machine learning; for instance, automated bail decisions reflect systemic racial bias, facial recognition accuracy rates are lowest for women and people of color, and algorithmically supported hiring decisions show evidence of gender bias. Federal, state, and local policy does not adequately address these risks, even as governments adopt new technologies. The toolkit will include both technical and policy components: (1) an interactive tool that illustrates the relationship between how machine learning models are trained and adverse social impacts; (2) a technical questionnaire for policymakers and non-experts to identify algorithmic systems and their attributes; and (3) a stepwise evaluation procedure for surfacing the social context of a given system, its technical failure modes (i.e., potential for not working correctly, such as false positives), and its social failure modes (i.e. its potential for discrimination when working correctly). The toolkit will be designed to serve (i) employees in state and local government seeking to surface the potential for algorithmic bias in publicly operated systems and (ii) members of advocacy and grassroots organizations concerned with the social justice implications of public sector technology.
In this project, fellows will gain hands-on experience using and auditing machine learning algorithms in surveillance contexts, with a focus on understanding the inner workings of these algorithms. To center the perspectives of those directly affected by such systems, the team will also engage community partners’ input on the Algorithmic Equity Toolkit throughout its development.
This project touches on issues and methodologies relevant to a wide range of fields, such as law, public policy, sociology, anthropology, design, social justice, data science, programming, artificial intelligence, geography, mathematics, statistics, critical gender and race studies, history, urban studies, and philosophy.
Understanding Congestion Pricing, Travel Behavior, and Price Sensitivity
Project lead: Mark Hallenbeck, Director, Washington State Transportation Center, University of Washington
Project Summary: Traffic congestion is a worldwide issue with environmental, health, and economic impacts. We cannot build our way out of it. Funding is limited and land for building new roads is finite. Building bigger roads in urban areas simply leads to more traffic and more congestion. Therefore, transportation authorities need new ways to optimize the performance of existing and future roadways. Many regions like the Puget Sound are exploring a variety of congestion pricing schemes to both generate revenue for roadway system improvements and to more sensibly manage traffic flow.
The Washington State Department of Transportation has partnered with the DSSG program to better understand travel behavior on I-405’s congestion priced Express Lanes. The DSSG team is examining: the time savings achieved by I-405 Express Lanes users; how those time savings vary given facility price; and how socio-demographic, geographic, and mode choice factors affect how the benefits and costs of the Express Lanes are distributed. Finally, DSSG will also examine how user behavior affects roadway performance. The intent is to provide WSDOT with important information needed to understand the effectiveness of current congestion pricing policies and guide future pricing policy decisions.
Natural Language Processing for Peer Support in Online Mental Health Communities
Alex Ware on Unsplash
Project leads:Tim Althoff, Assistant Professor, Computer Science & Engineering, University of Washington; and Dave Atkins, Research Professor, Psychiatry and Behavioral Sciences, University of Washington
Data science lead: Valentina Staneva
DSSG fellows:Shweta Chopra, David Nathan Lang, Kelly McMeekin
Project Summary: Everyone encounters challenges in life – whether that includes stress from school or work or more significant problems like depression and addiction. When we hit these challenges, we often reach out to friends and family for emotional support and problem-solving help. Peer support is an extension of this and has a long and well-researched history as a first line of intervention for mental health and addiction problems. Traditionally, peer support was in-person, but technology advances mean that support can be ‘crowdsourced’ and scaffolded and thus taken to scale.
However, by its nature, peers are not licensed counselors, and thus, it is critical to provide feedback on what really works and is helpful vs. what might be very well-intentioned… but not so helpful. Our Data Science for the Social Good project will focus on using data from an online peer support platform to better understand what types of responses are the most helpful to young adults sharing their struggles online. We will pursue this objective by analyzing a large-scale dataset of around 100 million posts and interactions. We then want to use these insights to develop tools and trainings for peers to help them be as helpful as they can when supporting others in need.