Feb. 5, 2024, 6:43 a.m. | Aditya Bhattacharya Simone Stumpf Lucija Gosak Gregor Stiglic Katrien Verbert

cs.LG updates on arXiv.org arxiv.org

In the realm of interactive machine-learning systems, the provision of explanations serves as a vital aid in the processes of debugging and enhancing prediction models. However, the extent to which various global model-centric and data-centric explanations can effectively assist domain experts in detecting and resolving potential data-related issues for the purpose of model improvement has remained largely unexplored. In this technical report, we summarise the key findings of our two user studies. Our research involved a comprehensive examination of the …

cs.hc cs.lg data data-centric debugging global interactive key learning systems lessons learned machine platform prediction prediction models processes report studies summarizing systems technical vital

Lead Developer (AI)

@ Cere Network | San Francisco, US

Research Engineer

@ Allora Labs | Remote

Ecosystem Manager

@ Allora Labs | Remote

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote