51 citations · 106 across the 5 of their papers we have counts for
6 papers
Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy
Lucas Rosenblatt, Bernease Herman, Anastasia Holovenko +7
Differential privacy (DP) data synthesizers support public release of sensitive information, offering theoretical guarantees for privacy but limited evidence of utility in practica…
Rethinking Streaming Machine Learning Evaluation
Shreya Shankar, Bernease Herman, Aditya G. Parameswaran
While most work on evaluating machine learning (ML) models focuses on computing accuracy on batches of data, tracking accuracy alone in a streaming setting (i.e., unbounded, timest…
An Algorithmic Equity Toolkit for Technology Audits by Community Advocates and Activists
Michael Katell, Meg Young, Bernease Herman +6
A wave of recent scholarship documenting the discriminatory harms of algorithmic systems has spurred widespread interest in algorithmic accountability and regulation. Yet effective…
The Promise and Peril of Human Evaluation for Model Interpretability
Bernease Herman
Transparency, user trust, and human comprehension are popular ethical motivations for interpretable machine learning. In support of these goals, researchers evaluate model explanat…
Synthetic Data for Social Good
Bill Howe, Julia Stoyanovich, Haoyue Ping +2
Data for good implies unfettered access to data. But data owners must be conservative about how, when, and why they share data or risk violating the trust of the people they aim to…
Data science for urban equity: Making gentrification an accessible topic for data scientists, policymakers, and the community
Bernease Herman, Gundula Proksch, Rachel Berney +5
The University of Washington eScience Institute runs an annual Data Science for Social Good (DSSG) program that selects four projects each year to train students from a wide range…