Artificial Intelligence (AI)

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.

Navigating AI Tools in Open Source Contributions: A Guide to Authentic Development

December 17, 2024
Sahiba Chopra
by Sahiba Chopra. The rise of ChatGPT has transformed how developers approach their work - but it might be hurting your reputation in the open-source community. While AI can supercharge your productivity, knowing when not to use it is just as crucial as knowing how to use it effectively. This guide reveals the unspoken rules of AI usage in open source, helping you navigate the fine line between leveraging AI and maintaining authenticity. Learn when to embrace AI tools and when to rely on your own expertise, plus get practical tips for building trust in the open-source community.

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...

Language Models in Mental Health Conversations – How Empathetic Are They Really?

December 3, 2024
Sohail Khan
by Sohail Khan. Language models are becoming integral to daily life as trusted sources of advice. While their utility has expanded from simple tasks like text summarization to more complex interactions, the empathetic quality of their responses is crucial. This article explores methods to assess the emotional appropriateness of these models, using metrics such as BLEU, ROUGE, and Sentence Transformers. By analyzing models like LLaMA in mental health dialogues, we learn that while they suffer through traditional word-based metrics, LLaMA's performance in capturing empathy through semantic similarity is promising. In addition, we must advocate for continuous monitoring to ensure these models support their users' mental well-being effectively.

LLMs for Exploratory Research

December 10, 2024, 1:00pm
In a fast evolving artificial intelligence landscape, LLMs such as GPT have become a common buzzword. In the research community, their advantages and pitfalls are hotly debated. In this workshop, we will explore different chatbots powered by LLMs, beyond just ChatGPT. Our main goal will be to understand how LLMs can be used by researchers to conduct early-stage (or exploratory) research. Throughout the workshop, we will discuss best practices for prompt engineering and heuristics to evaluate the suitability of an LLM's output for our research purposes. Though the workshop primarily focuses on early-stage research, we will briefly discuss the use cases of LLMs in later stages of research, such as data analysis and writing.

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.

Consulting: Supercharge Your Research with Hugging Face’s Toolkit

October 1, 2024
Iñigo Parra
Supercharge Your Research with Hugging Face’s Toolkit

Are you looking to elevate your research projects with cutting-edge machine learning models? Hugging Face might be just the tool you need. This platform makes it easy to access and implement state-of-the-art models, bringing efficiency and innovation to your work like never before.

Hugging Face is highly user-friendly, even for those new to Python or machine learning. It hosts thousands of models, offering diverse tools from natural language processing and computer...

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.

Conceptual Mirrors: Reflecting on LLMs' Interpretations of Ideas

April 23, 2024
María Martín López
by María Martín López. As large language models begin to engrain themselves in our daily lives we must leverage cognitive psychology to explore the understanding that these algorithms have of our world and the people they interact with. LLMs give us new insights into how conceptual representations are formed given the limitations of data modalities they have access to. Is language enough for these models to conceptualize the world? If so, what conceptualizations do they have of us?

Computational Social Science in a Social World: Challenges and Opportunities

March 26, 2024
José Aveldanes
by José Aveldanes. The rise of AI, Machine Learning, and Data Science are harbingers of the need for a significant shift in social science research. Computational Social Science enables us to go beyond traditional methods such as Ordinary Least Squares, which face challenges in addressing complexities of social phenomena, particularly in modeling nonlinear relationships and managing high-dimensionality data. This paradigmatic shift requires that we embrace these new tools to understand social life and necessitates understanding methodological and ethical challenges, including bias and representation. The integration of these technologies into social science research calls for a collaborative approach among social scientists, technologists, and policymakers to navigate the associated risk and possibilities of these new tools.