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Data Week | Unbiased AI: Detecting Unfairness In Data | May 21

Gideon Onyewuenyi Photo

AI Engineer, Pyloop
Rajdeep Biswas Photo

Lead Cloud Solutions Architect, Microsoft
Dr. Brett Werner Photo

Assistant Professor of Data Science, Bellevue University, College of Science & Technology
John Ohakim Photo

Data Analytics & Economic Modeling Expert, Accenture
Hiwot Tesfaye Photo

Sr. Data Scientist, SAS’ Health and Life Sciences Industry Solutions
Irene Bratsis Photo

Data Science Product Manager, Beekin
Data Week | Unbiased AI: Detecting Unfairness In Data
Friday, 21 May, 2021
2 3 pm EDT
Paris (Online)
Free
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Data Week | Unbiased AI: Detecting Unfairness In Data | May 21

Gideon Onyewuenyi Photo

AI Engineer, Pyloop
Rajdeep Biswas Photo

Lead Cloud Solutions Architect, Microsoft
Dr. Brett Werner Photo

Assistant Professor of Data Science, Bellevue University, College of Science & Technology
John Ohakim Photo

Data Analytics & Economic Modeling Expert, Accenture
Hiwot Tesfaye Photo

Sr. Data Scientist, SAS’ Health and Life Sciences Industry Solutions
Irene Bratsis Photo

Data Science Product Manager, Beekin

About This Event

This session is part of GA's Data Week, a weeklong festival of free workshops, speaker sessions and panel discussions around leveraging data to drive business decisions, productivity and career growth. Check out the full schedule here!

Inevitably technology reflects its creators in countless ways. AI systems are made up of machine learning algorithms, which are trained with input data. The decisions that AI systems make are entirely based on the data used to train them. AI is kind of like a black box, being dependent on the data it derives its insights from. So where is this data coming from? How can this black box have such an effect on our lives? From getting a job to even car insurance rates...

Join us as we uncover the unfairness that exists in data, in return creating biased algorithms.

Takeaways

You'll be able to:

  • Discover how AI will affect the future of work
  • Learn different ways to combat unconscious bias
  • Find out the role humans play in biased algorithms

Preparation

None needed!

About the Instructor

Gideon Onyewuenyi Photo

AI Engineer, Pyloop

Gideon Onyewuenyi is an AI Engineer, he is the co-founder of Pyloop and the lead at AI Saturdays Port Harcourt.

Rajdeep Biswas Photo

Lead Cloud Solutions Architect, Microsoft

Rajdeep is a Lead Cloud Solutions Architect at Microsoft focused on the design and implementation of large-scale Big Data, Advanced Analytics, Business Intelligence, and Machine Learning problems. Starting his career as a consultant in Apple iCloud reporting team, Rajdeep has been working in the world of Data and Distributed Computing for the past 15 years.

Dr. Brett Werner Photo

Assistant Professor of Data Science, Bellevue University, College of Science & Technology

Brett is an Assistant Professor of Data Science at Bellevue University. He has been teaching mathematics, statistics, and data science courses for over ten years. Brett’s research interests lie in machine learning and model building.

John Ohakim Photo

Data Analytics & Economic Modeling Expert, Accenture

John Ohakim is a data analytics and economic modeling expert. He currently works as a Data Science Consultant at Accenture in the Applied Intelligence practice. He has a passion for building predictive models and mining structured and unstructured data to find meaningful insights that drive business value and inform strategy. Prior to consulting, he worked in the Consumer Packaged Goods industry and in Academia. He is a podcast junkie and a movie connoisseur-adjacent. He holds a graduate degree from Colorado State University.

Hiwot Tesfaye Photo

Sr. Data Scientist, SAS’ Health and Life Sciences Industry Solutions

Hiwot currently works as a Sr. Data Scientist in SAS’ Health and Life Sciences Industry Solutions team where she advises SAS health care

clients on how to best leverage SAS software to get the most out of their data-driven initiatives. Before joining the health care team, Hiwot worked in SAS’s consulting division where she put her analytics expertise to use by developing visualizations and predictive models to address the division’s pressing business challenges.

Hiwot is passionate about ensuring diverse perspectives are included in the AI and analytics space. She understands that diversity of thought and lived experiences is a powerful tool to bolster the practice of responsible applications of AI and mitigate the risk of biased solutions being built and deployed. Hiwot has served on the leadership council of SAS’s first Black employee resource group – the Black Initiatives Group (BIG) – since its inception, where she leads and participates in efforts to recruit, retain and promote Black talent at SAS.

Before joining SAS, Hiwot was a graduate student at North Carolina State University’s Institute for Advanced Analytics. As part of her Master’s practicum, Hiwot leads a team of students in developing a model to predict patients’ risk of suboptimal diabetes management for UNC Healthcare. Hiwot holds an MSc. in Analytics from North Carolina State University’s Institute for Advanced Analytics and a BSc. in Economics and Nutritional Sciences from the University of Toronto.

Irene Bratsis Photo

Data Science Product Manager, Beekin

Irene Bratsis is an NYC-based data scientist, AI practitioner, and community builder, organizing events and initiatives with Women in AI, NYC & Boston chapter lead Women in Data and founding member of WiCyS Artificial Intelligence Affiliate Women in Trusted AI and Data Haus Book Club. Her background is in economics, international relations, machine learning, and data science, she has spent 12+ years in the private sector working for tech companies and currently works as a data science product manager at Beekin supporting a machine learning platform for real estate investors.

Irene is a writer for Medium, Women in Data, Gesture Blog, Towards Data Science, Analytics Vidya, An Injustice, CodeX, MLearning.ai, and Data-Driven Investor and passionate advocate for diversity, equity and inclusion, and the elimination of bias in AI systems.

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