Explaining a Machine Learning blackbox

Talk video

Talk presentation

As Data Scientists we want to understand machine learning models we have built. “Why did my model make this mistake?”, “Does my model discriminate?”, “How can I understand and trust the model's decisions?”, “Does my model satisfy legal requirements?” are commonly asked questions.

In this presentation we will talk about machine learning explainability and interpretability - two concepts that could help us really understand ML models.

Oleksander Krakovetskyi
  • CEO of DevRain, Ukrainian IT company
  • Co-founder and CTO of DonorUA - the intellectual system of blood donors recruiting
  • PhD. in Computer Science
  • Microsoft Regional Director, Microsoft Artificial Intelligence Most Valuable Professional
  • Microsoft Certified: Azure Data Science Associate
  • Linkedin , Facebook
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