Projects 2021
Activism Always: A Student Initiative for Data in the Social Impact Sector
CLTC Sponsored Project
Fellows: Chelsie Lui, Hoa Nguyen, Mikayla O’Reggio, Jin Pu
Our project is to provide an AI platform and service that not only collects and produces data insights for the mission driven sector, but formats recommendations specifically for non-data experts. We aim to provide a detailed framework of the issues that nonprofits face in regards to a lack of data and develop detailed recommendations to solve the issues that this sector faces. By having this research, Activism Always will then be able to combine a traditional consultancy service with our powerful AI platform to produce data-driven research and analysis service to support the long-term social engagement goals of mission driven organizations.
Website: www.activismalways.com
Alternative Emergency Response for People Who Cannot Call 911
Fellows: V de la Vega, Gabriella Wong
AccesSOS is a nonprofit organization that provides emergency response services to those who cannot call 911 (deaf, hard of hearing, speech disabilities, domestic violence survivors, etc.). Text to 911 services are not available everywhere, and accesSOS aims to bridge the gap by providing a website designed for smartphones that translates text to speech, until all 911 call centers acquire text to 911 technology.
Right now accesSOS has three options for emergency requesters: ambulance, police, and/or fire truck. This project will involve interviewing San Francisco’s Street Crisis Response Team and 911 dispatchers to understand the information needs emergency responders have in order to effectively provide alternative emergency responses. Based on the information we learn, accesSOS will include a fourth option: alternative emergency response. This project aims to identify the quickest and easiest design for people requesting alternative emergency services while also ensuring that emergency responders have the information they need. (Twitter, Instagram, Facebook)
The Distressing Ads that Persist: Uncovering the Persuasive Logics and Emotional Harms of User Targeted Diet Ads
AFOG Sponsored Project
Fellows: Liza Gak, Seyi Olojo
Our project aims to uncover the emotionally predatory nature of online advertising on disenfranchised populations. While there have been a variety of privacy and security concerns about data collection for advertising, online targeted advertising can also cause emotional harm due to the hyper-individualized, seemingly invasive nature of ad delivery. We are investigating how online targeted ads inflict emotional harm, and specifically how targeted diet and food ads are harmful to people recovering from disordered eating issues. In this project, we use virtual ethnography and semi-structured interviews to understand how diet ads contribute to emotional distress, and what strategies users take to mitigate the reach of these ads. We hope to continue our inquiry into emotional and psychological responses towards harmful diet ads by analyzing the rise of collective action within online communities.
Examining the Landscape of Digital Security and Privacy Assistance for Racial Minority Groups
CLTC Sponsored Project
Fellows: Abby Krishnan, Joanne Ma, Moses Namara, Nikita Samarin
Recent events have placed a renewed focus on the issue of racial justice in the United States and other countries. One dimension of this issue that has received considerable attention is the security and privacy threats and vulnerabilities faced by communities of color. This project aims to combine insights about elevated computer security and privacy risks faced by Black communities in the United States with research on improving end-user security and privacy behaviors through direct assistance and education. In particular, we will focus on community-level security advocates who raise awareness about threats to digital safety among members of their local communities and provide actionable mitigation strategies against those threats. Through interviews with security advocates, we plan to identify, categorize, and evaluate the practices for educating members of Black communities in the United States about threats to their security and privacy.
Leveraging the Communicative, Social and Health Benefits of Drumming in Early Childhood
Fellows: Jon Gillick, Jeremy Gordon, Pierre Tchetgen
The purpose of this project is to create a platform for embodied learning that builds the capacity of parents and teachers to interact with young learners’ in ways that helps them understand and acquire the skills necessary for multimodal meaning-making and active social-emotional engagement via rhythm. The objectives of the project are to design, implement and disseminate a system using machine learning and sensing technologies to gamify literacy skills development via rhythm and movement. This global community of practice will connect learners of all ages through rhythm as the common language and aims to create a bridge for children to learn, compete and have fun together. Parents and teachers could use it at home, in the classroom, outside in the community (playgrounds, parks), to encourage their language and social-emotional development so they can track the children’s improvement over time.
Solidarity, Not Charity!: Mutual Aid, Scalability, and Building Community Resilience
AFOG Sponsored Project
Fellows: Darya Kaviani, Tonya Nguyen
Mutual aid groups increasingly rely on online infrastructure to carry out their operations. However, mutual aid groups suffer from burnout, dominance behaviors, and failures to address intersectional power structures. To address these problems, past groups have customized their own networked infrastructures as a form of political participation. This includes an array of innovative structures including Zoom calls, ICE-raid hotlines, and automated systems for volunteer-reimbursements. However, the best strategies and design implications for mutual aid and other systems of care remains unclear and understudied. We plan to explore how mutual aid technologies and infrastructures are designed, built, and maintained.