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Found 7 papers, 1 papers with code
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning
Active Learning (AL) allows models to learn interactively from user feedback.
Antagonistic AI
The vast majority of discourse around AI development assumes that subservient, "moral" models aligned with "human values" are universally beneficial -- in short, that good AI is sycophantic AI.
Imagining a Future of Designing with AI: Dynamic Grounding, Constructive Negotiation, and Sustainable Motivation
We ideate a future design workflow that involves AI technology.
Supporting Sensemaking of Large Language Model Outputs at Scale
Large language models (LLMs) are capable of generating multiple responses to a single prompt, yet little effort has been expended to help end-users or system designers make use of this capability.
Metric Elicitation; Moving from Theory to Practice
Specifically, we create a web-based ME interface and conduct a user study that elicits users' preferred metrics in a binary classification setting.
Evaluating the Interpretability of Generative Models by Interactive Reconstruction
On synthetic datasets, we find performance on this task much more reliably differentiates entangled and disentangled models than baseline approaches.
Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems
The results of our experiments demonstrate that evaluations with proxy tasks did not predict the results of the evaluations with the actual decision-making tasks.
