11 citations · 28 across the 9 of their papers we have counts for
10 papers
-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge
Quan Shi, Alexandra Zytek, Pedram Razavi +2
Conversational agents are increasingly deployed in knowledge-intensive settings, where correct behavior depends on retrieving and applying domain-specific knowledge from large, pro…
Explingo: Explaining AI Predictions using Large Language Models
Alexandra Zytek, Sara Pido, Sarah Alnegheimish +2
Explanations of machine learning (ML) model predictions generated by Explainable AI (XAI) techniques such as SHAP are essential for people using ML outputs for decision-making. We…
LLMs for XAI: Future Directions for Explaining Explanations
Alexandra Zytek, Sara Pidò, Kalyan Veeramachaneni
In response to the demand for Explainable Artificial Intelligence (XAI), we investigate the use of Large Language Models (LLMs) to transform ML explanations into natural, human-rea…
Pyreal: A Framework for Interpretable ML Explanations
Alexandra Zytek, Wei-En Wang, Dongyu Liu +2
Users in many domains use machine learning (ML) predictions to help them make decisions. Effective ML-based decision-making often requires explanations of ML models and their predi…
Lessons from Usable ML Deployments and Application to Wind Turbine Monitoring
Alexandra Zytek, Wei-En Wang, Sofia Koukoura +1
Through past experiences deploying what we call usable ML (one step beyond explainable ML, including both explanations and other augmenting information) to real-world domains, we h…
The Need for Interpretable Features: Motivation and Taxonomy
Alexandra Zytek, Ignacio Arnaldo, Dongyu Liu +2
Through extensive experience developing and explaining machine learning (ML) applications for real-world domains, we have learned that ML models are only as interpretable as their…
MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series
Dongyu Liu, Sarah Alnegheimish, Alexandra Zytek +1
Detecting anomalies in time-varying multivariate data is crucial in various industries for the predictive maintenance of equipment. Numerous machine learning (ML) algorithms have b…
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
Furui Cheng, Dongyu Liu, Fan Du +5
Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks. Although many ML models perform promisingly, issues with model…
Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making
Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan +1
Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts - who often have no expertise in ML or data science - are asked t…
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
Sean McGrath, Parth Mehta, Alexandra Zytek +2
As machine learning (ML) models are increasingly being employed to assist human decision makers, it becomes critical to provide these decision makers with relevant inputs which can…