51 citations · 106 across the 5 of their papers we have counts for
Showing 2017Show all
3 papers · 1 filter
cs.AI2017★ 44 cited
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…
cs.CY2017★ 51 cited
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…
cs.CY2017★ 1 cited
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…