Leveraging Interpretable Machines for Performance Improvement and Decision – LIMPID

Huge increase of collected data, storage capacity and computing power promote the field of Artificial Intelligence (AI) to the status of panacea to all problems. Indeed, neural networks improved the results in the fields challenging for the handcrafted algorithms previously. However, there is always a price to pay: number of its drawbacks remain unaddressed. In the real world, a decision system with AI can receive an input that is unlike anything it has seen during training. That can lead to the unpredictable behavior. Can we trust the output of such system for a particular input? In LIMPID project, we address this issue of confidence of AI output in the context of face recognition and face quality estimation in images. LIMPID concentrates on a challenge how to estimate the confidence to any response of AI algorithm. This approach can be used in a wide range of applications. LIMPID also proposes the analyses of the image features that highly contribute to the AI algorithm’s decision.