Explainable AI: Bridging the Gap Between AI and Human Understanding
Keywords:
Explainable AI (XAI), Artificial Intelligence (AI), Machine Learning (ML), Interpretability, Transparency, Model Accountability, Black-box Models, User Trust, Ethical AI, Human-AI Interaction, Model Explainability, Interdisciplinary Research, Fairness in AI, Context-aware Explanations, Real-time ExplainabilityAbstract
AI (Artificial Intelligence) has been quickening now in all the regions of our lives — medicine, finance, automobile industry, entertainment and what not. Nevertheless, the more advanced AI systems become, the less they reveal about how decisions are made, with far-reaching implications for trust, responsibility and explanation. Introducing Explainable AI (XAI) as a concept to deal with all the above problems and make humans understand, trust and communicate better with AI systems. In this paper, we seek to provide a timely study of the role and expectations of explainability in AI; in particular: what is currently achievable, and what problems still remain. This paper explores this crossover of AI and human interpretability, revealing the role that explainable AI plays between technical supremacy and the human comprehension engine while advocating for more ethical, accountable, reliable AI.
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