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Explainable AI with H2O Driverless AI’s Machine Learning Interpretability Module

August 20 @ 6:30 pm - 8:30 pm

Join us this evening to hear from our own Michelle Tanco about Explainable AI with H2O Driverless AI’s Machine Learning Interpretability Module.

Explainable AI is in the news, and for good reason. Financial services companies have cited the ability to explain AI-based decisions as one of the critical roadblocks to further adoption of AI for their industry. Transparency, accountability, and trustworthiness of data-driven decision support systems based on AI and machine learning are serious regulatory mandates in banking, insurance, healthcare, and other industries. From pertinent regulations to increasing customer trust, data scientists and business decision-makers must show AI-based decisions can be explained. H2O Driverless AI does explainable AI today with its machine learning interpretability (MLI) module. This capability in H2O Driverless AI employs a unique combination of techniques and methodologies to explain the results of both Driverless AI models and external models.

Following is a brief agenda for the evening:

6:00 – 6:30 PM: Doors open for networking and pizza

6:30 – 7:15 PM: Michelle’s talk

7:15 – 7:30 PM: Q&A

7:30 PM – 8:00: Networking

Michelle’s Bio:

Michelle is a Customer Solutions Engineer & Data Scientist for H2O.ai. Prior to H2O she worked as a Senior Data Science Consultant for Teradata, focused on leading analytics projects to solve cross-industry business problems. Her background is in pure math and computer science and she is passionate about applying these skills to answer real-world questions. When not coding or thinking of analytics, Michelle can be found hanging out with her dog or playing the ukulele.