The rapid advancement of artificial intelligence (AI) and automation has ushered in a new era of technological innovation, transforming industries and reshaping societies. However, this technological revolution also brings forth a complex array of legal challenges that demand urgent attention. One of the most pressing legal issues is the question of accountability for AI-driven decisions. As AI systems become increasingly sophisticated, they are being entrusted with making decisions that have significant consequences for individuals and society as a whole. Yet, determining who is liable when an AI system makes an error or causes harm remains a complex legal question. Traditional notions of liability may not be adequate to address the complexities of AI systems, which often operate in a "black box" manner, making it difficult to trace the origins of a decision or error. Another critical legal challenge is the potential for AI to exacerbate existing biases and discrimination. AI systems are trained on vast datasets, which may contain biases that are then reflected in the system's decision-making. This can lead to discriminatory outcomes in areas such as employment, lending, and criminal justice. Addressing this challenge requires developing robust frameworks for ensuring fairness and transparency in AI algorithms, as well as implementing measures to mitigate bias in training data.