Nabeel Seedat
Mihaela van der Schaar
Andreas BedorfAI-powered applications are becoming increasingly widespread across many areas and industries, including e-commerce, finance, manufacturing, medicine, and many more. However, there are many considerations necessary to successfully develop robust and reliable ML systems.
In data-centric AI, we seek to give data centre stage. Data-centric AI views model or algorithmic refinement as less important (and in certain settings, algorithmic development is even considered as a solved problem), and instead seeks to systematically improve the data used by ML systems.
To dive deeper into the topic, we dedicated one of our Inspiration Exchange sessions on Data-centric AI, what it is, challenges, opportunities, and how it can be utilised effectively.
Enabling robust & reliable ML in healthcare and beyond
In this introductory presentation about data-centric AI, Prof Mihaela van der Schaar lays the groundwork for understanding the significance of machine learning for healthcare, and the need for a data-centric lens.
Data-IQ
Nabeel Seedat presents Data-IQ, a framework to systematically stratify examples into subgroups with respect to their outcomes. A new method to making reliable predictions in healthcare based on tabular data. This is a practical example of how data-centric AI can be used to improve real-life work in a clinical setting.
Understanding key data-centric considerations is crucial for reliable machine learning systems in healthcare and beyond. To address this pain point, we created DC-Check: an actionable checklist-style framework to practically engage with data-centric AI — providing the first standardised framework to communicate the design and necessary considerations for data-centric AI/ML pipelines. To learn more and use DC-Check see our dedicated website.
If you would like to learn more about data-centric AI, please have a look at our dedicated research pillar and our publications.
