WEBINAR
How to Spot and Understand AI Bias within Financial Services
While artificial intelligence is fueling advancements across financial services, practitioners need to be able to spot, understand, and counteract natural bias that occurs. However, not all bias is created equally - some biases can never truly be removed.
Join us as we dissect the world of “AI Bias” - specifically bias within unstructured data, and how non-data scientists can help course-correct for less biased results.
Date Presented: Wednesday, March 30th, 2022 at 12:00 PM EDT
Key Takeaways:
- What types of bias do we encounter with artificial intelligence?
- How do we spot and remove biases?
- What biases are most common within financial services and how can practitioners avoid or call out biases they encounter?
Speakers:
Anshul Vikram Pandey, Ph.D. - CTO & Co-Founder, Accern
Anshul Vikram Pandey is the Co-Founder and CTO of Accern, a no-code AI startup with offices in New York and Bangalore, where he leads the AI technology and innovation efforts. Accern allows Financial Organizations to easily build AI models that uncover risk and investment insights with a no-code development platform. Data teams from the world's leading organizations, such as Allianz, IBM, and Jefferies, are using Accern to build and deploy AI solutions powered by Accern’s adaptive NLP and predictive features. He holds a Ph.D. in Computer Science from NYU, and a B.E. degree in Electrical and Electronics Engineering from BITS Pilani, India. He has published several award winning research papers at top computer science conferences and his research is widely covered in books and popular media such as Reuters, NewScientist, Wired etc. He is also an advisor for Rutgers University’s big data program. He has won several awards in statistics, data science and entrepreneurship, received best doctoral research award, named among top 25 Fintech CTOs, and was listed in global Forbes 30 under 30, 2018.
Josua Krause, Ph.D. - VP of Data Science, Accern
Josua Krause is the VP of Data Science at Accern where he leads the research, development, and deployment of AI models. His focus is on deep representation learning, natural language processing, and adaptive learning at scale. He received his Ph.D. in Explainable Machine Learning at NYU Tandon where he is currently an Adjunct Professor.
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