Conversations around AI ethics often focus on a suite of incredibly important topics such as data security and privacy, model bias, model transparency, and explainability. However, each time we use large AI models (e.g., diffusion models, LLMs), we reinforce a host of additional potentially unethical practices that are needed to build and maintain these systems.

In this episode, Jake and David discuss some of these unsavory topics, such as human labor costs and environmental impact. Although it's a bit of a downer, it's crucial for each of us to acknowledge how our behavior impacts the larger ecosystem and recognize our role in perpetuating these practices.

"Explainable AI", aka XAI, refers to a suite of techniques to help AI system developers and AI system users understand why inputs to the system resulted in the observed outputs.

Industries such as healthcare, education, and finance require that any system using mathematical models or algorithms to influence the lives of others is transparent and explainable.

In this episode, Jake and David review what XAI is, classical techniques in XAI, and the burgeoning area of XAI techniques specific to LLM-driven systems.

Prompt engineering involves a lot more than simply getting smarter with how you structure the prompts you enter in an LLM browser interface.

Furthermore, a growing body of peer-reviewed research provides us with best practices to improve the accuracy and reliability of LLM outputs for the specific tasks we build systems around.

In this episode, Jake and David review evidence-based best practices for prompt engineering and, importantly, highlight what proper prompt engineering requires such that most of us likely cannot call ourselves prompt engineers.