The swift development of pattern recognition algorithms has ushered us in a proliferation of AI companies offering diverse capabilities. From object recognition to biometrics and Optical Character Recognition (OCR), the marketplace is crowded with options. While AI fuels progress in biometric algorithms, it introduces a critical challenge—algorithm bias. This bias poses problematic hurdles for both technology developers and end-users, burdening individuals with challenges arising from AI technology failures. In this presentation, we will examine a qualification framework extending beyond technical aspects. This framework can guide organizations in selecting identification and biometric technologies, addressing the complexity of algorithm bias.