Winning the Adversarial Battle with Open-Source AI Models

Abstract

In the era of proprietary, black-box Generative AI (GAI) models, this paper shifts the focus towards a more transparent and accessible alternative: open-source models. Despite the current dominance of closed-source large language models (LLMs) in the market, such as ChatGPT, their inherent privacy risks and other potential dangers outweigh their advantages. This paper argues that adopting open-source large language models offers legal organizations a more ethical pathway to incorporating GAI into their operations. Open-source models provide firms with complete control over their GAI infrastructure, empowering firms to adopt innovative, responsive, and responsible strategies that align with the ethical mandates of the legal profession and societal expectations of transparency.

This paper also illustrates the advantages of open-sources model such as control, transparency, and reliability. In addition, this paper discusses the regulatory ambiguities surrounding GAI and the incompatibility of non-transparent GAI systems with the ethical mandates and the often-adversarial nature of law practice. The paper concludes that transitioning towards open-source large language models will equip the legal industry to navigate the evolving ethical complexities of GAI.

Keywords: Open-Source, OpenAI, DataPrivacy, Transparency, Machine Learning, Artificial Intelligence