The Risks of Empowering “Citizen Data Scientists”

) roll out in the coming years — they’ll need to extend that governance to include AI created by non-data scientists. Given that spotting these risks takes not only technical expertise but also ethical, reputational, and regulatory expertise, this is no easy feat.

Third, related to both of the above, having AI novices spend time developing AI can lead to wasted efforts and internal resources on projects better left on the cutting room floor. And potentially worse than that, faulty models that get used may lead to significant unforeseen negative impacts.for technical, ethical, reputational, regulatory, and legal risks before going to production, without exception.

Ideally, mentors are involved throughout the AI product lifecycle, from the concept phase all the way through to model maintenance. At earlier stages, mentors can help teams avoid significant pitfalls and ensure a robust roadmap is developed. In later stages, they can play a more tactical role, like when the team needs guidance with a deployed model that isn’t performing as well as anticipated. Indeed, this function can also be very useful for experienced data scientists.

Any group in any organization can suffer from group think or simply a lack of imagination. One powerful way out of that is to encourage and provide the resources for everyone who builds AI models to attend AI conferences and summits, where the creativity using AI across all industries and business units is on full display. They may see a solution they want to procure, but more importantly, they may see a solution that inspires them to create something similar internally.AI is in its infancy.

Ethics & Responsibility aside, if we wait to build entire dpts of (real) Data Scientists & our Data to be 100% perfect, we'll never make progress. Sometimes meaningful Data exploration, user friendly tools, small steps are powerful if they help answer business questions.

Reading this title hurt my head 😅 but I know what y'all meant

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