Social Justice

Data Science for Social Justice 2024 (Apply by April 15)

March 15, 2024, 12:00pm
This 8-week workshop will give you the opportunity to learn the essential tools and methods for data science analysis and be introduced to critical frameworks that will enable you to create a project of your own design and to tell stories that can counter the market-first mentality of data science. This workshop has a heavy emphasis on collaboration and peer-to-peer learning, with a significant group work component.
See event details for participation information.

The More Things Change the More They Stay the Same?

December 18, 2023
By Tonya D. Lindsey, Ph.D. Think about how often you hear someone gripe about the deterioration of society and then blame the Internet or social media. This blog suggests that the things we are exposed to virtually are not new but instead present us with more and frequent opportunities to reflect on perennial social problems and find solutions even as we better understand ourselves as individuals in a global community.

Exploratory Data Analysis in Social Science Research

November 14, 2023
by Kamya Yadav. Causal inference has become the dominant endeavor for many political scientists, often at the expense of good research questions and theory building. Returning to descriptive inference – the process of describing the world as it exists – can help formulate research questions worth asking and theory that is grounded in reality. Exploratory data analysis is one method of conducting descriptive inference. It can help social science researchers find empirical patterns and puzzles that motivate their research questions, test correlations between variables, and engage with the existing literature on a topic. In this blog post, I walk through results from exploratory data analysis I conducted for my dissertation project on political ambition of women.

Artificial Intelligence and the Mental Health Space: Current Failures and Future Directions

October 31, 2023
by María Martín López. María Martín López, a PhD student in the department of psychology whose research focuses on large language models within the context of mental illness, gives an overview of current failures and possible future directions of NLP models in the mental health space. She brings up questions that must be considered by all researchers working in this space and encourages these individuals to think creatively about the use of AI beyond direct treatment.

Americanist Linguistics: on Ethics and Intent

October 17, 2023
by Anna Björklund. In this post, Anna Björklund investigates the origin of the linguistic study of indigenous American languages, its inextricable ties to settler-colonialism, and how linguistics can move forward as a field.

Critical Faculty and Peer Instructor Development: Core Components for Building Inclusive STEM Programs in Higher Education

Claudia von Vacano, Ph.D.
Michael Ruiz
Renee Starowicz, Ph.D.
Seyi Olojo
Arlyn Y. Moreno Luna
Evan Muzzall, Ph.D.
Rodolfo Mendoza-Denton, Ph.D.
David Harding, Ph.D.

First-generation college students and those from ethnic groups such as African Americans, Latinx, Native Americans, or Indigenous Peoples in the United States are less likely to pursue STEM-related professions. How might we develop conceptual and methodological approaches to understand instructional differences between various undergraduate STEM programs that contribute to racial and social class disparities in psychological indicators of academic success such as learning orientations and engagement? Within social psychology, research has focused mainly on student-level mechanisms...

D-Lab & Graduate Division create inclusive data science summer program

August 9, 2023
by Vanessa Navarro Rodriguez. UC Berkeley's Social Sciences D-Lab and Graduate Division created the Data Science for Social Justice Program to address underrepresentation in data science. The program teaches diverse students critical data analysis and its applications in addressing societal injustices. The 8-week free summer course for admitted University of California students focuses on Python programming, Natural Language Processing, and value-informed data practices. It aims to empower students from underrepresented backgrounds and to bridge STEM with social justice. This blog post elaborates on the program's creation and features one of the DSSJ students, Robin López, and his reasons for participating.

My Summer Exploring Data Science for Social Justice: Learnings, Tensions & Recommendations

September 5, 2023
by Genevieve Smith. This summer I joined the D-Lab hosted Data Science for Social Justice workshop at UC Berkeley diving into Python – including TF-IDF, sentiment analysis, word embeddings, and more – with a lens towards leveraging data science for social justice. My team explored a Reddit channel on abortion and used computational analysis to answer key questions related to abortion access from before versus after Roe vs. Wade was overturned. Computational social science is incredibly powerful, but I continue to grapple with tensions particularly as it relates to employing machine learning and large language in international research, and end with key recommendations for CSS practitioners.

Artificial Intelligence (AI) Systems, the Poor, and Consent: A Feminist Anti-Colonial Lens to Digitalized Surveillance

September 18, 2023
By Alejandro Nuñez. Today’s digital age has created a sea of endless datafication where our everyday interactions, actions, and conversations are turned into data. The advancements of automated artificial intelligence (AI) systems, and their infrastructure in which they are created and trained on, have catapulted us into an era of consistent monitoring and surveillance.

Data Science for Social Justice Workshop 2023

March 1, 2023, 12:00pm
This 8-week online workshop for currently enrolled UC Berkeley graduate students will give you the opportunity to learn the essential tools and methods for data science analysis and be introduced to critical frameworks that will enable you to create a project of your own design and to tell stories that can counter the market-first mentality of data science.
See event details for participation information.