Understanding How Humans Inject Knowledge into Machine Learning Workflows through Visual Analytics
arXiv:2607.00969v1 Announce Type: cross Abstract: Visual analytics (VA) plays an increasingly important role in supporting machine learning (ML) workflows. In the field of visualization, such approaches and techniques are referred to as VIS4ML. While ML models are mostly learned automatically, the corresponding ML workflows receive a variety of human inputs, such as data labelling, feature engineering, model architecture designing, hyper-parameter tuning, and so on. In this work, we surveyed over 200 VIS4ML papers to gain an understanding of how humans inject their knowledge into ML workflows
The proliferation of complex ML models necessitates improved human-machine interaction to ensure effective deployment and oversight, making this research timely.
Understanding how humans inject knowledge into ML workflows is crucial for optimizing AI development, ensuring model reliability, and fostering broader adoption of advanced AI systems.
The focus on VIS4ML highlights a growing recognition of the need for intuitive interfaces and methodologies that allow human expertise to effectively guide and refine machine learning processes.
- · AI developers
- · Data scientists
- · Visual analytics companies
- · Tech companies leveraging ML
- · Organizations with opaque ML pipelines
- · Manual, ad-hoc ML workflow approaches
Improved efficiency and accuracy in machine learning model development and deployment.
Increased trust and adoption of AI systems across various industries due to better interpretability and human control.
The democratization of advanced AI capabilities as human-in-the-loop systems become more accessible and intuitive.
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Read at arXiv cs.LG