There will be a coffee reception after the speaker's talk from 11AM to 11:30AM.

Abstract: How might static, acontextual data support the dynamic and contextual goals of equity and ethical practice? Stakeholders in educational and community contexts routinely use data to identify nuanced patterns of disparities or bias. However, data alone does not tell us what actions to take, and may exacerbate existing societal biases or introduce new ones. To advance equity and ethics related goals, I partner with diverse stakeholders to use data and computing as tools to renegotiate systemic structures of power.

In this talk, I will describe my research in designing sociotechnical systems that foster critical discourse with and about data for equitable, ethical, and community-centric computing education. I will share prior work that advances equity in computing education by contextualizing interpretations of data with domain expertise from students, teachers, curriculum designers. I will also share ongoing work with embedding ethics instruction into university-level computer science curricula. Finally, I will share research co-constructing a learning experience with community-based organizations where young people of color learn about data science to advance advocacy about climate justice in their local communities. At the core of my research are critical and feminist ideas relating to reciprocal partnerships with diverse stakeholders and connections between systems of power and lived experiences. By embracing these ideas when designing computing experiences, we can reimagine data and computation as tools that advance equitable and ethical practices by empowering diverse stakeholders and augmenting existing community assets.

Bio: Benjamin “Benji” Xie (he|they) is an Embedded EthiCS Postdoctoral Fellow at the Stanford University Human-Centered Artificial Intelligence Institute and McCoy Family Center for Ethics in Society. He designs socio-technical interactions for critical discourse with and about data for equitable learning, community advocacy, and ethical AI use. He engages with computing education, human-computer interaction, and critical data studies research communities. He received his PhD in information science from the University of Washington, where he was a National Science Foundation (NSF) Graduate Research Fellow and also a research intern with nonprofit Code.org. His doctoral research explored stakeholders’ interpretations of data for equitable computing education. He received his Master’s and undergraduate degrees in computer science from MIT, where he was an MIT EECS-Google Research and Innovation Scholar researching with MIT App Inventor.

RSVP: https://pitt.co1.qualtrics.com/jfe/form/SV_bjx8SmaMuLxNYBE