Search Results for author:
Found 9 papers, 1 papers with code
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Based on the machine's output, the teacher can revise the definition of the task or make it more precise.
Active Learning with Oracle Epiphany
We present a theoretical analysis of active learning with more realistic interactions with human oracles.
Machine Teaching: A New Paradigm for Building Machine Learning Systems
This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them.
REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
Transparency around limitations can improve the scientific rigor of research, help ensure appropriate interpretation of research findings, and make research claims more credible.
How Different Groups Prioritize Ethical Values for Responsible AI
Compared to the US-representative sample, AI practitioners appear to consider responsible AI values as less important and emphasize a different set of values.
Aligning Offline Metrics and Human Judgments of Value for Code Generation Models
Large language models have demonstrated great potential to assist programmers in generating code.
Supporting Human-AI Collaboration in Auditing LLMs with LLMs
Through the design process we highlight the importance of sensemaking and human-AI communication to leverage complementary strengths of humans and generative models in collaborative auditing.
Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions
However, creating high-quality datasets with LLMs can be challenging.
