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Explainable AI for Transparency and Trust

Teams frequently face “build or buy” decisions when evaluating the cost-to-benefit ratio of using external vendors versus investing in building something in-house. In our previous experience, one frequent consideration that would come up is whether the onboarding and maintenance of a new service would cost more time and effort than not having integrated it at all.

This was a particularly sensitive matter when it came to software services that generated recommendations for fraud or abuse detection using analytics or predictive AI. As expected, these systems sometimes produced incorrect results. While it is expected that no system is perfect, one consistent source of frustration encountered by our teams was the lack of transparency around how a given recommendation was made by a selected tool, costing engineers, data scientists, and analysts countless hours spent investigating black box decisions.

For this reason, we chose to make Nuanced a product that not only yields an overall evaluation of whether a given image is likely generated by AI, but also provide some level of interpretability for said decision.