Interpretable Approaches to Detect Bias in Black-Box Models
By Sarah Tan. 2018
6 citations recorded by the source corpus.
My dissertation research is grounded in the field of interpretability. I aim to develop methods to explain and interpret predictions from black-box machine learning models to help creators, as well as users, of machine learning models increase their trust and understanding of the models. In this doctoral consortium paper, I summarize my previous and current research projects in interpretability, and describe my future plans for research in this area.
Subfields: Artificial Intelligence
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
- Mohammed Berrada
- Amina Adadi
- Longo Luca
- Giulia Vilone
- Gesina Schwalbe
- Bettina Finzel
- Christian Walter Peter Omlin
- Waddah Saeed
Citations in the corpus
- Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “Interested researchers can also consult these [125]–[127] for further details about this technique.”