352 citations · 1k across the 42 of their papers we have counts for
10 papers · 1 filter
More Than Words: Towards Better Quality Interpretations of Text Classifiers
Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini +4
The large size and complex decision mechanisms of state-of-the-art text classifiers make it difficult for humans to understand their predictions, leading to a potential lack of tru…
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models
David Nigenda, Zohar Karnin, Muhammad Bilal Zafar +4
With the increasing adoption of machine learning (ML) models and systems in high-stakes settings across different industries, guaranteeing a model's performance after deployment ha…
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng +18
Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and labor-intensive task that depends on the application, the da…
Multiaccurate Proxies for Downstream Fairness
Emily Diana, Wesley Gill, Michael Kearns +3
We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time -- in other words, how can we…
On the Lack of Robust Interpretability of Neural Text Classifiers
Muhammad Bilal Zafar, Michele Donini, Dylan Slack +3
With the ever-increasing complexity of neural language models, practitioners have turned to methods for understanding the predictions of these models. One of the most well-adopted…
On Measuring the Diversity of Organizational Networks
Zeinab S. Jalali, Krishnaram Kenthapadi, Sucheta Soundarajan
The interaction patterns of employees in social and professional networks play an important role in the success of employees and organizations as a whole. However, in many fields t…