The opening plenary of SICSS-Howard/Mathematica 2022 featured a fireside chat with Dr. Anthony K. Wutoh, the Provost of Howard University, and Dr. Amy Yeboah Quarkume, an Associate Professor of Africana Studies, to kick off the event.

The first Summer Institute in Computational Social Science held at a Historically Black College or University, returns to Howard University for its two-day pre-institute, Praxis to Power for graduate students, postdoctoral researchers, and beginning faculty who needed more time to practice computational methods.

Summer Institute in Computational Social Science site sponsored by Howard University and Mathematica (SICSS-Howard/Mathematica) awards individuals and teams for the inaugural Excellence in Computational Social Science Research Fund as a unique and exclusive benefit offered to alumni of the site.

This blog post is the second of nine in a series called “The Future of Computational Social Science is Black” about SICSS-Howard/Mathematica 2021, the first Summer Institute in Computational Social Science held at a Historically Black College or University.

This blog post is the first of nine in a series called “The future of computational social science is Black” about SICSS-Howard/Mathematica 2021, the first Summer Institute in Computational Social Science held at a Historically Black College or University. As you go through this package of blogs you will experience the journey we took in 2021.

A new Summer Institute in Computational Social Science organized by Howard University and Mathematica promises to bring the power of computational social science to the issues of systemic racism and inequality in America. This marks the first time the successful SICSS model is being hosted by a Historically Black College or University.

And talking about gender equality in social data science means talking about the representation of women in tech and attitudes towards women in tech. It means confronting the stubborn prejudices and perceptions that women can’t code or can’t do stats. It means having a discussion about how as this new community of thought and practice is forming, we have a chance to make it look different than the communities that came before. And in particular, it seems vital to challenge ourselves to do so because of the questions social data scientists are asking and the methods they are using - because of the danger of biased algorithms, of reinforcing inequality through policies based on big but dirty data. 

Watch the panel discussion.