Place the Domain Expert at the Center of your AI System
It's important to place the domain expert at the center of an AI system. When data scientists and ML engineers build AI systems, leaving the SME out of the loop is a recipe for no-impact solutions.
In my article, I detail how a an AI system is led by the SME, and the data scientist follows the research methodology of the domain expert. Utilizing AI only identifies the patterns of interest- it is up to the domain expert to determine the validity and accuracy of such findings. In this case, the AI outputs either validates the SME's own experience, or it differs. If there is enough noise that a new classification may be in order, is confirmed by the SME that they have seen such anomalies, and there are specific remedies to those identified problems, then a research experiment may be in order to test the new hypothesis.
Only when there are multiple, sound research studies and the thought leaders of the domain accept those experimental results, then this can become a standard in the domain and future AI models can work on improving this new standard through better identification of such patterns, helping the SME deliver on both classification and better solutions of those problems.
Ultimately, the domain expert is running the show. Data scientists should team up with SMEs in the domain of interest, as the domain experts only see AI as another tool for them to improve identification of problems and their solutions for them. AI does not replace the SME, rather, the domain expert dictates to it, and it is the domain expert who determines if the AI system will have impact to their domain.
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