Explaining a Machine Learning blackbox

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
DevRain
  • 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.
  • Linkedin , Facebook
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