- 28 October 2022
Surbhi will stay at URI for a PhD, beginning in January 2023 after finishing her MS this semester.
- 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.
- 10 June 2022
Her thesis is derived from our Task Fairness Project and focuses on feature selection.
- 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.
- 23 March 2022
- 07 September 2021
Emmely has continued at URI as a MS student and Surbhi has joined the lab to work on Task Fairness.
- 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
- 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”
- 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.
- 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.
- 22 February 2021
- 27 January 2021
- 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
- 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!
- 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.
- 08 October 2020
- 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.