In the field of legal data, where security and privacy are critical, the challenges of using artificial intelligence (AI) are significant. Despite the difficulties, progress has been made in gathering data and developing AI models for legal purposes. However, with little margin for error, it’s crucial to handle AI predictions with care.
Explainable AI (XAI) is key in this situation. It offers a way to make AI decisions in the legal field more transparent and understandable. By explaining how AI models come to their conclusions, we can trust their predictions more. This ensures that the AI’s reasoning is sound and aligns with legal standards.
In conclusion, we’ll be discussing the advances made in this area and our suggested approach towards it. This discussion aims to show how XAI can help make AI tools more reliable and useful in legal settings, moving us closer to accurate and trustworthy AI insights.
Shiva is an AI Developer at Westernacher Solutions, specializing in the legal domain. She focuses on developing AI-based solutions to innovate and streamline legal processes. With a strong background in Natural Language Processing (NLP), Shiva has successfully led and contributed to various projects encompassing both NLP and computer vision technologies. Her work is characterized by a commitment to leveraging AI to solve complex challenges in the legal field, demonstrating a blend of technical expertise and domain-specific knowledge.
