Navigating the Ethical Landscape of Large Language Models
As a longtime CIO (DSI in french) and Lecturer for more than 24 years, I’ve witnessed firsthand how emerging technologies can reshape organizational strategies, cultural norms, and the fundamental ways we interact with one another. Today, one of the most fascinating — and challenging — frontiers lies in the world of artificial intelligence, particularly within large language models (LLMs). LLMs are transforming how we communicate, learn, and conduct business. These models can generate human-like text, code, and even reason through complex problems, but with such great capability comes significant responsibility. As stewards of technology, we need to consider the ethical dimensions that underpin the deployment and use of LLMs. 1. Bias and Fairness: A model’s outputs often reflect the data it’s trained on. If that data skews toward certain cultural, gender, or racial biases, the model can inadvertently amplify harmful stereotypes. Ensuring fairness means diversifying training sets,...