• What data preprocessing techniques are essential for preparing employee-related data for turnover prediction?
• Which machine learning algorithms are most effective for predicting employee turnover, and how do we determine the best one for our organization?
• How can we incorporate qualitative factors like job satisfaction and employee engagement into a predictive model for turnover?
• What evaluation metrics should be used to assess the performance of a turnover prediction model?
• How can we address potential biases in the data to ensure the turnover prediction model is fair and accurate across different employee groups?

• How can we design analytical models that assess cultural fit alongside technical skills during the recruitment process?
• What data sources and metrics are most effective for incorporating company culture into recruitment optimization models?
• How can we ensure that analytical models used in recruitment do not introduce bias and accurately reflect the company’s cultural values?
• What role does natural language processing (NLP) play in evaluating cultural fit during the recruitment process?
• How can recruitment models be continuously updated to adapt to evolving company culture and changing organizational needs?

We continue with 2 more rounds of our interactive World Café sessions.

At the end of the last World Café round, moderators wil give a short summary of each world café in front of the audience to showcase all key takeaways.  

All results, pictures, and key takeaways of the World Cafés will be photographed after the event and made available in the event app hubs101.