Ruth Yakubu
Microsoft
LEARN, CONNECT, BUILD
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LEARN, CONNECT, BUILD
Microsoft Reactor
Join Microsoft Reactor and learn from developers
Whether you're building your career or the next great idea, Microsoft Reactor connects you with the developers and startups that share your goals. Learn new skills, meet new peers, and find career mentorship. Virtual events are running around the clock so join us anytime, anywhere!
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How to expose ML model errors, data bias & interpretability with responsible AI
13 July, 2023 | 4:30 PM - 5:30 PM
- Format:
Livestream
Topic: Data Science & Machine Learning
Language: English
The astonishing growth in AI innovations are transforming our lives and society. Companies are adopting AI in their business process and products to gain a competitive advancement. The rapid advancements are seen across many industries such as finance, healthcare, education, manufacturing, etc. As society expectation for AI are evolving, there's increasing scrutiny on what harms AI systems can cause with no transparency or accountability enforced. As a result, there’s growing government compliance regulations on AI in some industries. On the other hand, data scientists, AI developers and decision-makers face the challenge of finding the right tools to enable them to analyze machine learning models for fairness, safety & reliability, explainability and accountability. In this session, we will explore the Azure Machine Learning's Responsible AI dashboard tool that enables data scientists and companies to analyze and debug AI systems to be less harmful to society, more trustworthy and meet compliance requirements.
All times in - (UTC) Coordinated Universal Time
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