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.
- 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