Ruth Yakubu
Microsoft
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LEARN, CONNECT, BUILD
Microsoft Reactor
Neem deel aan Microsoft Reactor en neem live contact op met ontwikkelaars
Klaar om aan de slag te gaan met AI en de nieuwste technologieën? Microsoft Reactor biedt evenementen, training en communitybronnen om ontwikkelaars, ondernemers en startups te helpen bouwen op AI-technologie en meer. Kom kijken.
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How to expose ML model errors, data bias & interpretability with responsible AI
13 juli, 2023 | 4:30 p.m. - 5:30 p.m. (UTC) Coordinated Universal Time
- Notatie:
Livestream
Onderwerp: Datawetenschap & Machine Learning
Taal: Engels
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.
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