705 citations · 1.7k across the 20 of their papers we have counts for
3 papers · 1 filter
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their…
Leveraging Latent Features for Local Explanations
Ronny Luss, Pin-Yu Chen, Amit Dhurandhar +4
As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions…
Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment
Jonathan Dodge, Q. Vera Liao, Yunfeng Zhang +2
Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on developers, users, and the general public to identify fairness problems and make improvem…