Description
In the rapidly evolving landscape of Machine Learning (ML), significant advancements like Large Language Models (LLMs) are gaining critical importance in both industrial and academic spheres. However, the rush towards deploying advanced models harbors inherent ethical tensions and potential adverse societal impacts. The keynote will start with a brief introduction to the principles of ethics, viewed through the lens of philosophy, emphasizing how these fundamental concepts find application within ML. Grounding our discussion in tangible realities, we will delve into pertinent case studies, including the BigScience open science initiative, elucidating the practical application of ethical considerations. Additionally, the keynote will touch upon findings from my recent research, which investigates the synergy between ethical charters, legal tools, and technical documentation in the context of ML development and deployment.