Advances, challenges and opportunities in creating data for trustworthy AI

By Weixin Liang, Girmaw Abebe Tadesse, Daniel E. Ho, Li Fei-Fei, Matei A. Zaharia, Ce Zhang, James Y. Zou. 2022

535 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics, Safety, Robustness & Interpretability, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Douwe Kiela
2. Mohit Bansal
3. Nazneen Rajani
4. Tristan Thrush
5. Yacine Jernite
6. Yejin Choi
7. Margaret Mitchell
8. Angelina McMillan-Major

In Safety, Robustness & Interpretability

1. Tristan Thrush
2. Douwe Kiela
3. Angelina McMillan-Major
4. Yacine Jernite
5. Alexandra Sasha Luccioni
6. Margaret Mitchell
7. Nazneen Rajani
8. Adina Williams

In AI Ethics, Fairness & Society

1. Angelina McMillan-Major
2. Douwe Kiela
3. Yacine Jernite
4. Alexandra Sasha Luccioni
5. Margaret Mitchell

Citations in the corpus

• Measuring Data (2022, methodology): “…of measuring data can be understood against the backdrop of increased “data-centric AI” research, with groups in academia and industry stressing the importance of “systematic methods to evaluate, synthesize, clean and annotate the data used to train and test [AI models]" (Liang et al., 2022).”
• Multimodal data fusion for cancer biomarker discovery with deep learning (2023, background): “To make progress in multimodal studies, there is a dire need for data orchestration platforms 157 , but also appropriate regulatory frameworks to preserve patients’ privacy 162 .”