Procrastination in the face of rapid AI advancements is no longer an option. The time for AI Governance is now; the more we delay, the greater the risk of finding ourselves too late to act effectively. It is our responsibility to shape the future of AI, and that future is being written in the present.
The rise of large language models (LLMs) brings both innovation opportunities and potential societal risks, making the responsible development and deployment of LLMs crucial. The current LLM discourse largely misses the key aspect of responsible AI - transparency. Therefore, there's a need for new, human-centered methods to enhance LLM transparency. Transparency aims to foster human understanding and varies according to the differing needs of various stakeholders in different contexts.
Therefore, the design of transparency approaches must consider stakeholders' needs, the types of LLM applications being developed, new usage patterns, challenges, and past learnings from human interaction with information. The provision of LLM transparency
presents unique challenges, requiring the integration of insights from human-computer interaction (HCI) and responsible AI research. Four common model governance strategies are model governance reporting, continuous evaluation, providing capabilities such as explanations, simulations etc. and communicating uncertainty. However, there remain open questions on how these transparency approaches can be effectively applied to LLMs. At Qausal, we provide a framework and product for addressing this.
Model reporting, primarily through documentation, is a cornerstone of responsible AI practices, enhancing transparency around models, AI services, and datasets. Standardized frameworks like the model cards[1] are widely used, detailing key aspects of the model, its intended uses, ethical considerations, and evaluation results. Despite its benefits in helping stakeholders assess a model's suitability, misuse prevention, and context provision for models' development and
