Explainable artificial intelligence

Abstract

Artificial Intelligence (AI) has made significant strides across various domains, but the opacity of many AI models, especially in critical sectors like healthcare, finance, and autonomous vehicles, has raised concerns. To address this, Explainable Artificial Intelligence (XAI) has emerged, aiming to shed light on AI decision-making and provide human-comprehensible explanations. Understanding XAI is crucial as it can lead to more transparent, trustworthy, and accountable AI systems. XAI seeks to make complex AI models interpretable, bridging the gap left by black-box models like deep neural networks. Various XAI approaches cater to different use cases, with the choice depending on specific domain requirements. However, integrating XAI into autonomous vehicles poses unique challenges, necessitating solutions that maintain real-time decision-making without compromising safety. Achieving interpretability in deep learning models commonly used in autonomous vehicles is also challenging, requiring novel tailored approaches. Moreover, presenting explanations in a clear, concise manner is essential for user trust. Legal and ethical considerations arise when integrating XAI into autonomous vehicles, requiring comprehensive validation and adherence to regulatory standards. Rigorous evaluation, including quantitative and qualitative measures, is imperative to ensure effectiveness and prevent misleading explanations. XAI holds promise for enhancing transparency and interpretability across various fields, but its integration into autonomous vehicles requires addressing specific hurdles while maintaining real-time capabilities and user-centricity, ultimately fostering public trust and acceptance in transformative technologies like autonomous driving.