Social Justice

Teaching Truth, Resisting Erasure: Disability Politics in a Changing America

February 25, 2025
Jane (Mango) Angar
by Jane (Mango) Angar. Disability is a social construct shaped by systemic exclusion rather than an inherent impairment. Society predominantly views disability through medical and economic lenses, leading to discrimination and marginalization. Disability rights have been hard-won through activism, yet disabled individuals still face poverty, social isolation, and violence. Recent policy rollbacks threaten disability protections, requiring vigilance from educators and advocates. Historical patterns show that marginalized groups are often the first targets of oppressive regimes. Teaching history with truth and resilience is an act of resistance. Activism, awareness, and collective action remain crucial in defending disability rights and promoting social justice.

Teaching Data Science as a Tool for Empowerment

February 18, 2025
Elijah Mercer
by Elijah Mercer. Data literacy is a powerful tool for empowerment, especially for historically marginalized communities. Through Data Cafecito at Roadmap to Peace and helping teach Data 4AC at UC Berkeley, Elijah Mercer helps bridge the gap between data, advocacy, and justice. Data Cafecito fosters culturally responsive data practices for Latinx-serving organizations, while Data 4AC challenges students to critically analyze data’s role in systemic inequities. Drawing from his experience in education, Mercer uses interactive teaching methods to make data accessible and meaningful. By centering storytelling and community-driven insights, he aims to equip individuals with the skills to use data for social change.

Why Data Disaggregation Matters: Exploring the Diversity of Asian American Economic Outcomes Using Public Use Microdata Sample (PUMS) Data

February 11, 2025
Taesoo Song
by Taesoo Song. Asian Americans are often overlooked in discussions of racial inequality due to their high average socioeconomic attainment. Many academic and policy researchers treat Asians as a single racial category in their analysis. However, this broad categorization can mask significant within-group disparities, leaving many disadvantaged individuals without access to vital resources and policy support. Song emphasizes the importance of data disaggregation in revealing Asian American inequalities, particularly in areas like income and homeownership, and demonstrates how breaking down these categories can lead to more targeted and effective policy solutions.

The Creation of Bad Students: AI Detection for Non-Native English Speakers

January 21, 2025
Valeria Ramírez Castañeda
by Valeria Ramírez Castañeda. This blog explores how AI detection tools in academia perpetuate surveillance and punishment, disproportionately penalizing non-native English speakers (NNES). It critiques the rigid, culturally biased notions of originality and intellectual property, highlighting how NNES rely on AI to navigate the dominance of English in academic settings. Current educational practices often label AI use as dishonest, ignoring its potential to reduce global inequities. The post argues for a shift from punitive measures to integrate AIs as a tool for inclusivity, fostering diverse perspectives. By embracing AI, academia can prioritize collaboration and creativity over control and discipline.

Tom van Nuenen, Ph.D.

Data/Research Scientist, Senior Consultant, and Senior Instructor
D-Lab
Social Sciences
Digital Humanities

I work as a Lecturer, Data Scientist, and Senior Consultant at UC Berkeley's D-Lab. I lead the curriculum design for D-Lab’s data science workshop portfolio, as well as the Digital Humanities Summer Program at Berkeley.

Former research projects include a Research Associate position in the ‘Discovering and Attesting Digital Discrimination’ project at King’s College London (2019-2022) and a researcher-in-residence role for the UK’s National Research Centre on Privacy, Harm Reduction, and Adversarial Influence Online (2022). My research uses Natural Language Processing methods to...

Human-Centered Design for Migrant Rights

October 29, 2024
Victoria Hollingshead
by Victoria Hollingshead. In honor of the 2024 International Day of Care and Support, Victoria Hollingshead shares her recent work with the Center for Migrant Advocacy’s Direct Assistance Program and their innovative approach to supporting Overseas Filipino Workers (OFWs) using generative AI. OFWs, especially female domestic workers in the Gulf Cooperation Council (GCC), are vulnerable to exploitation from foreign employers and recruitment agencies while having limited access to legal support. Using a design thinking framework, Victoria and CMA’s Direct Assistance team co-designed a proof of concept to enhance the legal and contract literacy among OFWs in the Kingdom of Saudi Arabia, a top destination country. This project shows promise in leveraging emerging technologies to empower OFWs, enhancing the Philippines' reputation as a migrant champion and supporting the nation's broader push for digital transformation.

The Case for Including Disability in Social Science Demographics

October 15, 2024
Jane (Mango) Angar
by Mango Jane Angar. As we celebrate Disability Awareness Month at the D-Lab alongside the UC Berkeley scholarly community, how can we, as social scientists, individually promote accessibility and inclusion? To advance accessibility, we should focus on addressing the barriers faced by individuals with disabilities, using our research to provide insights for effective policy recommendations. Although most of us do not focus on disability-related issues, including disability as a demographic characteristic in our data collection can greatly enhance our understanding of diverse populations and improve the comprehensiveness of our analyses. This small step can contribute to broader efforts toward inclusion and social equity.

Leveraging Large Language Models for Analyzing Judicial Disparities in China

October 8, 2024
Nanqin Ying
by Nanqin Ying. This study analyzes over 50 million judicial decisions from China’s Supreme People’s Court to examine disparities in legal representation and their impact on sentencing across provinces. Focusing on 290 000 drug-related cases, it employs large language models to differentiate between private attorneys and public defenders and assess their sentencing outcomes. The methodology combines advanced text processing with statistical analysis, using clustering to categorize cases by province and representation, and regression models to isolate the effect of legal representation from factors like drug quantity and regional policies. Findings reveal significant regional disparities in legal access driven by economic conditions, highlighting the need for reforms in China’s legal aid system to ensure equitable representation for marginalized groups and promote transparent judicial data for systemic improvements.

Data for a Just U.S. - Using Data Science to Empower Marginalized Communities

September 3, 2024
Elijah Mercer
by Elijah Mercer. In this blog post, I share how working with marginalized communities through data science has transformed my understanding of the field. My journey from crime analysis to founding Data for Just US reveals the profound impact data can have when used to empower and uplift underserved populations. I explore the challenges and rewards of this work, illustrating how data science can drive social change and foster a more equitable future.

AI Ethics in Action: UC Berkeley’s Data Science for Social Justice Workshop

August 28, 2024, 5:00pm
Claudia von Vacano, Ph.D., Founding Executive Director of D-Lab, introduces the Data Science for Social Justice Workshop, highlighting its goals, structure, and outcomes. Three students who have participated in the workshop present lightning talks on their experience with DSSJ, highlighting their personal journeys, the projects they worked on, and what they gained from the workshop.