The Challenge 

Focusing on digitization leads to strategic advantages in all forms, and using vast amounts of data is essential. Data-driven techniques are powerful tools with endless possibilities, which is why we firmly believe that it comes with a great deal of transparency and interpretability. However, at the moment the ever-increasing black box is only getting worse; in our addiction to accuracy, we throw model upon model but transparency and control are lacking. Doing so, we do not only decrease interpretability enormously, but also diminish sustainability of model performance and loss of reputation and trust.

The Solution

We have to show that responsibility is necessary for a sustainable data-driven future. By improving transparency and explainability of data-driven applications, we can help everyone achieve their strategic business goals in an ethical manner. Increasing societal acceptance and interpretability of machine learning can only be done by focusing on transparent data usage, for which a strong digital foundation is key. We focus on this foundation, mainly by assessing and/or improving data quality and data warehouses (storage); all while emphasizing responsibility and control.

The Mission

Today's techniques enable everyone to capture everyday's data to use these for any business purpose. The computing power of machines keeps surging, so increasingly data can be used to achieve goals more accurately. However, the means should not surpass the end; data is a powerful tool but must be handled responsibly. We must act now, and shift the sector's focus from being addicted to accuracy towards transparency and interpretability in order to really fit data science into society. Only then can we overthrow certain boundaries and work on the ecosystem of tomorrow.

This project is being coached by