44 citations · 98 across the 10 of their papers we have counts for
11 papers
Using Large Language Models to Generate Engaging Captions for Data Visualizations
Ashley Liew, Klaus Mueller
Creating compelling captions for data visualizations has been a longstanding challenge. Visualization researchers are typically untrained in journalistic reporting and hence the ca…
D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias
Bhavya Ghai, Klaus Mueller
With the rise of AI, algorithms have become better at learning underlying patterns from the training data including ingrained social biases based on gender, race, etc. Deployment o…
Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions
Bhavya Ghai, Mihir Mishra, Klaus Mueller
Understanding the cumulative effect of multiple fairness enhancing interventions at different stages of the machine learning (ML) pipeline is a critical and underexplored facet of…
Fluent: An AI Augmented Writing Tool for People who Stutter
Bhavya Ghai, Klaus Mueller
Stuttering is a speech disorder which impacts the personal and professional lives of millions of people worldwide. To save themselves from stigma and discrimination, people who stu…
WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings
Bhavya Ghai, Md Naimul Hoque, Klaus Mueller
Intersectional bias is a bias caused by an overlap of multiple social factors like gender, sexuality, race, disability, religion, etc. A recent study has shown that word embedding…
Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making
Md Naimul Hoque, Klaus Mueller
The widespread adoption of algorithmic decision-making systems has brought about the necessity to interpret the reasoning behind these decisions. The majority of these systems are…
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang +1
We propose a new active learning (AL) framework, Active Learning++, which can utilize an annotator's labels as well as its rationale. Annotators can provide their rationale for cho…
NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis
Anjul Tyagi, Cong Xie, Klaus Mueller
Recent advancements in the area of deep learning have shown the effectiveness of very large neural networks in several applications. However, as these deep neural networks continue…
Measuring Social Biases of Crowd Workers using Counterfactual Queries
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang +1
Social biases based on gender, race, etc. have been shown to pollute machine learning (ML) pipeline predominantly via biased training datasets. Crowdsourcing, a popular cost-effect…
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang +2
The wide adoption of Machine Learning technologies has created a rapidly growing demand for people who can train ML models. Some advocated the term "machine teacher" to refer to th…
Does Speech enhancement of publicly available data help build robust Speech Recognition Systems?
Bhavya Ghai, Buvana Ramanan, Klaus Mueller
Automatic speech recognition (ASR) systems play a key role in many commercial products including voice assistants. Typically, they require large amounts of clean speech data for tr…