22 posts tagged with "best-practices"

When asked to generate recommendation letters for 200 U.S. names, ChatGPT produced significantly different language based on perceived gender - even when given prompts designed to be neutral.

In political discussions, ChatGPT responded with a significant and systematic bias toward specific political parties in the US, UK, and Brazil.

And on the multimodal side, OpenAI's Dall-E was much more likely to produce images of black people when prompted for images of robbers.

There is no shortage of other studies that have found LLMs exhibiting bias related to race, religion, age, and political views.

As AI systems become more prevalent in high-stakes domains like healthcare, finance, and education, addressing these biases necessary to build fair and trustworthy applications.

At the heart of every Generative AI application is the LLM foundation model (or models) used. Since LLMs are notoriously expensive to build from scratch, most enterprises will rely on foundation models that can be enhanced through few shot or many shot prompting, retrieval augmented generation (RAG), and/or fine-tuning. Yet what are the security risks that should be considered when choosing a foundation model?

In this blog post, we'll discuss the key factors to consider when choosing a foundation model.