no code implementations • 13 Mar 2024 • Angela Jin, Niloufar Salehi
Accountable use of AI systems in high-stakes settings relies on making systems contestable.
1 code implementation • 25 Oct 2023 • Nikita Mehandru, Sweta Agrawal, Yimin Xiao, Elaine C Khoong, Ge Gao, Marine Carpuat, Niloufar Salehi
A major challenge in the practical use of Machine Translation (MT) is that users lack guidance to make informed decisions about when to rely on outputs.
no code implementations • 13 May 2022 • Wesley Hanwen Deng, Nikita Mehandru, Samantha Robertson, Niloufar Salehi
Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals.
no code implementations • 13 Jan 2021 • Doris Xin, Eva Yiwei Wu, Doris Jung-Lin Lee, Niloufar Salehi, Aditya Parameswaran
Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning.
no code implementations • 13 Jul 2020 • 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 stakeholders' values.