Challenges of Building AI Models in Finance and the Importance of Transparency

Transparent data science, profitable AI, and what’s missing from a data science education: Pandata’s Data Scientist in Residence Keith McCormick and Jon Krohn discuss how “insights” can never be the end product of a data science project, how to ensure you have a specific goal at the start of a project that is related to revenue, and why there is so much miscommunication between data scientists and their clients. Exclude the C-suite at your peril!

This episode is brought to you by Glean (glean.io), the platform for data insights, fast. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• What an Executive Data Scientist in Residence is [05:27]
• What A.I. transparency is and how it relates to the field of Explainable A.I. (XAI) [17:34]
• How companies can ensure they profit from AI projects [36:47]
• Possible organization structures for data science teams to be profitable [1:02:41]
• The current gaps in data science education [1:09:58]

Additional materials: www.superdatascience.com/655