Skip navigation
Diversity and Inclusion
magnifying glass hovering over the word "bias" Alamy

How Latimer GenAI Model Is Fighting Bias, Improving Representation

Bias within the datasets utilized in generative AI models is a significant concern. Here's how one LLM — Latimer — is attacking this problem.

The buzz around artificial intelligence has created a rush to craft useful generative AI models and assistants. Much of the latest work centers on open source large language models (LLMs).

However, as the use of data depicting individuals and their actions in these models grows for delivering services and making assessments, technologists are increasingly concerned about the portrayal of inclusivity within the technology.

One model that is addressing these concerns is Latimer, a large language model that has been designed to provide cultural information related to African American and Hispanic cultures and to address bias in AI models. Its model development combined with responsible management of training data can light the way for reducing data bias in GenAI models.

LatimerLatimer logo

What Is Latimer?

Latimer was created by John Pasmore as a teaching tool to help people understand how to craft better prompts, particularly ones that involve cultural norms and history.

Latimer is named after Lewis Latimer, an African American inventor and technologist who is best known for refining the carbon filament in the electric light bulb and later becoming a chief draftsman at Thomas Edison's lab. Other inventions included an evaporative air conditioner and an improved toilet system for railcars. In addition, Latimer used his patent expertise to help Alexander Graham Bell file a patent for the telephone.

AlamyLewis Latimer

Figure 1: Latimer was named in honor of African American inventor Lewis Latimer.

How Latimer Works

The look of Latimer's prompt entry page is similar to that of ChatGPT's. But the difference is more than skin deep. While Latimer relies on Meta's Llama-2 GPT, it differs in that it uses unique foundation model augmented with data representing historical events, oral traditional stories, local archives, literature, and current events related to communities of color.

Latimer answering question "Who is Lewis Latimer?

Figure 2: Latimer answers the prompt: "Who is Lewis Latimer?"

Another unique technical aspect is Latimer's retrieval-augmented generation (RAG) model. RAG is a pipeline data model designed to manage a set of documents such that the most relevant documents are matched to prompt queries. There are several steps to the RAG model, including splitting documents from data sources into vector databases, then comparing data and documents as "chunks" of information for accuracy and recency. The collective information is passed to the LLM to derive the final response.

RAG-based LLMs are meant to improve the accuracy of finding and citing information for highly complex queries or knowledge-intensive tasks. Many organizations crafting their LLMs are optimizing them with a variation of the RAG pipeline model. Latimer used its RAG model to reduce bias in the prompt responses.

Latimer enhances its technological edge by leveraging resources dedicated to ensuring data accuracy. The effort begins with the development team, which collaborates with prominent cultural scholar Molefi Kete Asante, a distinguished professor of African, African  American, and communication studies at Temple University, to help continuously refine the model.

Latimer also has an exclusive contract to use licensed content, including from the New York Amsterdam News, a traditional newspaper that covers news and events for the Black community. 

All of these measures have been implemented to ensure Latimer uses high-quality data and avoids disseminating inaccurate cultural information.

How Addressing AI Bias Can Help Combat Systematic Discrimination

Technology experts have long been worried about the role of AI in data bias. AI models have an inherent potential to scale data bias into their model decisions and related outcomes. Artificial intelligence enables programmed devices to execute tasks that once required human intelligence to do. Yet AI systems are susceptible to scaling biases because they can tackle tasks quickly while having their insights limited to training data that could be missing vital information.

For example, the ACLU warns that AI systems being used to evaluate potential tenants can inadvertently perpetuate housing discrimination. These AI-based decision systems rely on court records and other datasets that have their own built-in biases that reflect systemic racism, sexism, and ableism, datasets that are notoriously full of errors. As a result, people are being denied housing because they are deemed ineligible regardless of their actual capacity to afford rent.

One factor contributing to the perpetuation of biases in automated decisions is how the input data is associated. This can be particularly tricky with LLMs. Large language models make associations based on how they are trained, allowing the prompt to emphasize which elements are associated and which are not.

For example, when a programming language reads "1 + 1,"  it uses parameters to tell the computer that the numerals are numbers, not text, and that they can be added to form the number 2. In contrast, LLMs assume that "1 + 1" is "2" based off of seeing examples in the prompts. This approach is called chain-of-thought prompting. Chain of thought is a prompting technique in which the user instructs the model with choices for exploring wording, tense, and mathematical approach of a given prompt.

Thus, LMMs operate as a data layer using concepts and data to craft an outcome or condition, rather than relying on syntax in how it interprets information for an outcome. This difference is also why processes such as RAG have become important in crafting model accuracy beyond prompt engineering.

This underscores the importance of ensuring genuine diverse representation in AI development, necessitating methodologies that go beyond mere platitudes and concepts to establish robust safeguards against potentially harmful outcomes. If there is data representing activity from specific communities, it is vital not to omit that data from model training datasets. Such omission leads to discrimination when deciding which communities receive services and investment, denying opportunities and hindering progress. 

If we treat data the same way as the number in the addition example, it becomes clear that guardrails for LLM assumptions are essential for applications in which there are community and cultural concerns.

Technologists keen on AI should pay attention to how results from models such as Latimer influence how people prompt and how representative the outcome from a model can be. 

Technologists should also pay attention to research being done on such GenAI techniques as chain of thoughts — such as this white paper from the Google Brain team — and emerging discoveries in how RAGs are used for models.

At this stage, it's too early to assess the results. Currently, Latimer is managing a gradual rollout of expanded access, starting with limited organizational access for universities. Miles University, a historically black college or university (HBCU), is the first to give its students access to the model, while another HBCU, Morgan State, was added in January.  A waitlist for public use is available on the Latimer website.

However, because of significant interest and investment, Latimer shows promise in tackling technological bias when it comes to social and educational issues, as well as in implementing inclusive data techniques.

Hide comments


  • Allowed HTML tags: <em> <strong> <blockquote> <br> <p>

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.