All Posts
Funding from the Survival and Flourishing Fund
- 30 October 2024
We received funding from the SFF Fairness Track to fund work developing a benchmark for fair data-driven decision making with LLMs. We will develop a benchmark for both data analytics style decision-making and training fair models on data. Then we will assess state of the art AI systems engaging in these tasks across various levels of AI agency.
Dr. Brown is speaking in the Digital Defense Fund fall seminar series
- 08 October 2024
She is co-leading a session on AI basics titled, “Unmasking AI: the Mysteries and Magic of Artificial Intelligence”.
Dr. Brown is presenting at NSBE PDC
- 23 August 2024
NSBE’s Professional Development conference is a venue for working professionals to skill up. She is presenting the building a portfolio website workshop by invitation
Dr Brown will participate in the AI lab Fireside chat series
- 29 March 2024
title How to anticipate risks in AI-enhanced research
abstract ML-powered AI is increasingly appearing in all areas of research. However, ML is still in many ways a research output, more of a prototype than a polished, reliable, product. Even commercially released systems, because they are software, can be released without the same rigorous testing that commercially available research equipment has been traditionally be subject to. In this talk, I will draw connections between the ML research process and potential risks in applied ML and use of commercial AI in research.
Dr. Brown will present a workshop at NSBE 50
- 22 March 2024
The workshop will be a hands on workshop to build a profile website using github pages.
Dr. Brown gave a keynote at Middlebury College Women in Data Science conference
- 04 March 2024
title Data Science Skills in Unexpected Places
abstract Data science is a highly interdisciplinary pursuit, combining computer science and statistics with domain expertise to make sense of data. This means as data scientists we must work closely with domain experts or develop enough domain expertise of our own for each project. In this talk, I will share the story behind a few data science projects I have done: how high school social studies helped me develop a learning PTSD diagnosis, undergraduate advanced writing helps me measure understanding of fairness in AI, and how student leadership experiences help me design tools for data scientists to build better models.
Welcome to new undergraduates Lani, Noah, Trevor, and Rohan
- 23 January 2024
Lani will be working on understanding how problem formulation is/not described in current ML research.
Trevor and Noah will be joining Brendan to work on internal tooling for the lab.
Welcome to new Undergraduate Researchers Kyle and Brendan
- 10 September 2023
Kyle will be working on applying ML to Antimicrobial Peptide (AMP) design.
Brendan will be working on softare improvements to internal tools used in the lab.
Professor Brown teaching TD
- 31 July 2023
Professor Brown is co-teaching Active Learning in Computer Science and leading CS Thinking and learning for the Talent Development Summer Success Program.
Surbhi Rathore will be the lab’s first PhD Student
- 28 October 2022
Surbhi will stay at URI for a PhD, beginning in January 2023 after finishing her MS this semester.
Paper Accepted to 1st ACM SIGKDD Workshop on Ethical Artificial Intelligence
- 27 June 2022
Dr. Brown and Surbhi Rathore’s paper “Information Theoretic Framework For Evaluation of Task Level Fairness” was accepted to the 1st ACM SIGKDD Workshop on Ethical Artificial Intelligence! The paper will be presented at the EAI-KDD22 Workshop.
Surbhi Rathore has passed her MS Thesis Proposal
- 10 June 2022
Her thesis is derived from our Task Fairness Project and focuses on feature selection.
Welcome new undergraduate RAs Linda, Justin, and Aiden
- 06 June 2022
Linda will join our collaboration with the Boykin lab and be developing ecologically valid stimuli for assessing people’s judgements of ML algorithms.
Aiden and Justin will be evaluating performance of fair machine learning models on data with known biases. Marie will continue working on the experiment tools to support this work.
Teaching Software Carpentry at NSBE
- 23 March 2022
Dr. Brown taught sw carpentry at NSBE Convention notes from the workshop are online
Welcome Emmely and Surbhi as the first graduate students!
- 07 September 2021
Emmely has continued at URI as a MS student and Surbhi has joined the lab to work on Task Fairness.
TD STEM Academy Workshop
- 23 August 2021
Professor Brown and incoming Graduate student Emmely Trejo Alvarez are hosting a workshop for incoming students this week to learn about AI biases by replicating the COMPAS analysis. Resources are available online
New Paper Accepted to EAAMO’21
- 16 August 2021
A paper with the Boykin lab at Brown University was accepted to the inaugural ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. It’s availabe open access on ACM DL It’s an extension of a previous workshop paper, “Opportunities for a More Interdisciplinary Approach to Perceptions of Fairness in Machine Learning”
IBM Academic Award
- 06 August 2021
Dr. Brown Received a 2021 IBM Global University Program Academic Award to develop an Information Theoretic Framework for Evaluating Fairness in AI Applications.
Welcome New Undergraduate Researchers Patrick and Jake
- 01 June 2021
Patrick is working on the Wiggum Project and Jake received an Arts & Sciences Fellowship to work on model-based comparison of fair machine learning algorithms.
Paper Accepted to NeurIPS Workshop on ML Retrospectives, Surveys, and Meta Analyses
- 03 November 2020
Our paper with Malik Boykin’s lab at Brown University, “Opportunities for a More Interdisciplinary Approach to Perceptions of Fairness in Machine Learning” has been accepted to the NeurIPS 2020 Workshop: ML Retrospectives, Surveys & Meta-Analyses.
The paper is on the workshop website
Paper Accepted to NeurIPS Workshop Dataset Curation and Security
- 02 November 2020
Kweku Kwekir-Aggrey’s paper “Measuring Bias with Wasserstein Distance” was accepted to the NeurIPS 2020 Workshop on Workshop on Dataset Curation and Security!
Paper Accepted to NeurIPS Workshop on Consequential Decisions in Dynamic Environments
- 31 October 2020
Jessica Dai’s paper “Label Bias, Label Shift: Fair Machine Learning with Unreliable Labels” was accepted to the NeurIPS 2020 Workshop on Consequential Decisions in Dynamic Environments! She will also present this work at the Women in Machine Learning Workshop.
Attending NSBE
- 19 August 2020
Professor Brown is attending the National Society of Black Engineers Conference this week. She is teaching a Data Carpentry Workshop in Python with other members of the Carpentries.