44 citations · 104 across the 47 of their papers we have counts for
7 papers · 1 filter
Do LLMs exhibit human-like response biases? A case study in survey design
Lindia Tjuatja, Valerie Chen, Sherry Tongshuang Wu +2
As large language models (LLMs) become more capable, there is growing excitement about the possibility of using LLMs as proxies for humans in real-world tasks where subjective labe…
PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans
Giang, Nguyen, Valerie Chen +2
Nearest neighbors (NN) are traditionally used to compute final decisions, e.g., in Support Vector Machines or k-NN classifiers, and to provide users with explanations for the model…
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines
Matthew Barker, Emma Kallina, Dhananjay Ashok +6
Even though machine learning (ML) pipelines affect an increasing array of stakeholders, there is little work on how input from stakeholders is recorded and incorporated. We propose…
Learning Personalized Decision Support Policies
Umang Bhatt, Valerie Chen, Katherine M. Collins +4
Individual human decision-makers may benefit from different forms of support to improve decision outcomes, but when each form of support will yield better outcomes? In this work, w…
A Case Study on Designing Evaluations of ML Explanations with Simulated User Studies
Ada Martin, Valerie Chen, Sérgio Jesus +1
When conducting user studies to ascertain the usefulness of model explanations in aiding human decision-making, it is important to use real-world use cases, data, and users. Howeve…
Assisting Human Decisions in Document Matching
Joon Sik Kim, Valerie Chen, Danish Pruthi +2
Many practical applications, ranging from paper-reviewer assignment in peer review to job-applicant matching for hiring, require human decision makers to identify relevant matches…
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan +1
AI explanations are often mentioned as a way to improve human-AI decision-making, but empirical studies have not found consistent evidence of explanations' effectiveness and, on th…