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#ExplainableAI #AITransparency #DataScienceTeams Transparent data science, profitable AI, and what’s missing from a data science education: Pandata’s Data Scientist in Residence Keith McCormick and @JonKrohnLearns 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 (https://glean.io), the platform for data insights, fast. Interested in sponsoring a SuperDataScience Podcast episode? Visit https://www.jonkrohn.com/podcast for sponsorship information. In this episode you will learn: • [00:00:00] What an Executive Data Scientist in Residence is • [00:16:06] What A.I. transparency is and how it relates to the field of Explainable A.I. (XAI) • [00:35:18] How companies can ensure they profit from AI projects • [01:01:11] Possible organization structures for data science teams to be profitable • [01:08:29] The current gaps in data science education Additional materials: https://www.superdatascience.com/655