Search Results for author:
Found 7 papers, 1 papers with code
Aligning Offline Metrics and Human Judgments of Value of AI-Pair Programmers
While functional correctness is clearly an important property of a code generation model, we argue that it may not fully capture what programmers value when collaborating with their AI pair programmers.
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.
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.
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.
Active Learning with Oracle Epiphany
We present a theoretical analysis of active learning with more realistic interactions with human oracles.
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.
