However, creating high-quality datasets with LLMs can be challenging.
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.
Large language models have demonstrated great potential to assist programmers in generating code.
Compared to the US-representative sample, AI practitioners appear to consider responsible AI values as less important and emphasize a different set of values.
Transparency around limitations can improve the scientific rigor of research, help ensure appropriate interpretation of research findings, and make research claims more credible.
no code implementations • 21 Jul 2017 • Patrice Y. Simard, Saleema Amershi, David M. Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang, John Wernsing
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.
no code implementations • 16 Sep 2014 • Patrice Simard, David Chickering, Aparna Lakshmiratan, Denis Charles, Leon Bottou, Carlos Garcia Jurado Suarez, David Grangier, Saleema Amershi, Johan Verwey, Jina Suh
Based on the machine's output, the teacher can revise the definition of the task or make it more precise.