Explainable Artificial Intelligence for Computer Vision and Quantum Machine Learning

Artificial Intelligence and Machine Learning (AI/ML) are the emulation of human intelligence by computer systems. The AI/ML models have made inroads into Computer vision and Quantum computing due to their tremendous ability for reasoning and problem-solving. Computer vision allows computers to interpret visual information (photos, videos) similar to humans while Quantum computers execute complex tasks more efficiently than regular computers. However, how to explain the reasons for specific outputs is an important challenge with AI/ML models when applied to Computer vision and Quantum computing. Without the explainability of the AI/ML, the reliability and validity of Computer vision and Quantum computing outputs, when AI/ML is applied to them, is of great concern for researchers. To solve the issue of trustworthiness and transparency researchers have come up with the idea of explainable AI/ML (XAI). Therefore, to understand the extent of research on XAI in the area of Computer vision and Quantum computing, this study used the existing literature from the Scopus database and statistically presented the results. The study has found that the research on XAI in Computer vision and Quantum computing has been growing particularly since 2019. For AI/ML in Computer vision, the maximum studies on the XAI are in the healthcare sector accounting for 57% followed by autonomous vehicles. As far as AI/ML in Quantum computing is concerned, the majority of XAI research is related to the healthcare sector accounting for 46% followed by cyber security (20%).