Peering Inside AI’s Black Box

Artificial intelligence (AI), especially generative AI (gen AI), is rapidly advancing and reshaping various domains, from creative arts to critical decision-making areas such as healthcare and finance. As gen AI becomes more embedded in daily technologies and processes, understanding how these systems arrive at decisions becomes crucial. The opacity of AI processes, where even the engineers behind these systems cannot fully trace their decision pathways, raises significant concerns about transparency and trust. This paper discusses the evolution of AI from simple expert systems to complex large language models (LLMs) that defy easy explanation. It highlights the challenges posed by inherent biases and the over-reliance on data correlations without logical reasoning. The emergence of explainable artificial intelligence (XAI) seeks to address these issues by making AI systems more understandable and trustworthy. XAI aims to develop methods that not only enhance system transparency but also ensure fairness by minimizing biases. This is increasingly important as AI systems take on roles that directly impact human lives, necessitating models that can be both trusted and verified by users across various levels of technical expertise.