57 citations · 89 across the 5 of their papers we have counts for
5 papers
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements
Samantha Robertson, Zijie J. Wang, Dominik Moritz +2
Machine learning (ML) models can fail in unexpected ways in the real world, but not all model failures are equal. With finite time and resources, ML practitioners are forced to pri…
Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust
Wesley Hanwen Deng, Nikita Mehandru, Samantha Robertson +1
Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals. However, in order to use MT…
Not Another School Resource Map: Meeting Underserved Families' Information Needs Requires Trusting Relationships and Personalized Care
Samantha Robertson, Tonya Nguyen, Niloufar Salehi
Public school districts across the United States have implemented school choice systems that have the potential to improve underserved students' access to educational opportunities…
Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms
Samantha Robertson, Tonya Nguyen, Niloufar Salehi
Across the United States, a growing number of school districts are turning to matching algorithms to assign students to public schools. The designers of these algorithms aimed to p…
What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design
Samantha Robertson, Niloufar Salehi
Emerging methods for participatory algorithm design have proposed collecting and aggregating individual stakeholder preferences to create algorithmic systems that account for those…