I’m working on an AI project where the model performance itself isn’t the main challenge. Accuracy and validation are reasonable, and the outputs are fairly consistent. A simplified version of the logic looks like this: risk_score = model.predict_proba(X)[0][1] if risk_score > 0.8: recommendation = “block” elif risk_score > 0.5: recommendation = “review” else: recommendation(Read More)
I’m working on an AI project where the model performance itself isn’t the main challenge. Accuracy and validation are reasonable, and the outputs are fairly consistent.
A simplified version of the logic looks like this:
What I’m trying to reason through is what happens around this logic in practice.
In some cases, teams treat the output as guidance. In others, it effectively becomes the decision. Over time, that line can blur, especially once this logic is embedded into workflows and automation.
The question I’m wrestling with isn’t about model quality, but about design and accountability. How do teams decide where human judgment should remain explicit? How do you prevent recommendations from quietly becoming defaults? And how do you keep ownership of outcomes clear as systems scale?
Looking for perspectives on how others structure AI-assisted decisions so that roles, responsibility, and intent stay clear.
Alex
KnowledgeKingX
Nellabh
