Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

Abstract Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and…

• EC
  European Commission

  Awards: 618615-EPP-1-2020-1-UKEPPKA2-CBHEJP, 2020-1-UK01-KA201-079167
• E
  Erasmus+

  Awards: 618615-EPP-1-2020-1-UKEPPKA2-CBHEJP, 2020-1-UK01-KA201-079167
• C
  CHIST-ERA

  Award: CHIST-ERA-19-XAI-009