Is AI Speaking Your Language?
The language of African Americans has been given many labels over the past fifty years, including Black English, Ebonics, African American English (AAE), African American Vernacular English (AAVE), and, most recently, African American Language (AAL).
I can’t help but wonder: Will AI keep up? Should it? After reading a recent Tech Crunch Article Titled, “Black Founders are Creating Tailored ChatGPTs for a More Personalized Experience,” I got the confirmation of what I felt in my spirit: YES.
In the rapidly evolving world of artificial intelligence, the importance of representation and cultural nuance cannot be overstated. As we integrate AI into more aspects of our lives, these tools must reflect all users' diverse experiences and voices. Without an intersectional lens and equitable approach, we risk perpetuating existing biases and excluding entire communities from the benefits of AI advancements.
The Challenge of Cultural Nuance in AI
ChatGPT and similar AI tools struggle with cultural nuance, often providing generalized answers that lack the specificity needed for certain communities. This issue highlights a broader problem: many AI models are not built with people of color in mind, resulting in tools that do not accurately reflect or serve diverse populations.
According to the article, many Black founders have recognized this gap and are developing AI models tailored to Black and brown communities. Latimer.AI and Erin Reddick's ChatBlackGPT aim to provide culturally relevant responses and better represent these communities' experiences.
The Importance of an Intersectional Lens
When building AI tools, it's essential to consider the diverse experiences and perspectives of all users. This means not only including data from various cultural backgrounds but also involving people from these communities in the development process. AI models trained on Eurocentric and Western-biased data will inevitably fail to capture the full spectrum of human experience.
For instance, Latimer.AI adjusts its language to reflect more culturally sensitive terminology, such as referring to "enslaved" or "freedom-seeking people" instead of "runaway slaves." This level of cultural attunement is critical for creating AI that respects and accurately represents the histories and experiences of marginalized communities.
Representation Matters
There is power in seeing oneself reflected in the tools we use. I intentionally select voices in tools like Google Home that sound like mine. It adds to the experience and affirms my identity. This desire for representation is not new; we saw similar pushback decades ago when we demanded diversity in dolls like Barbie. Today, the call for inclusion extends to the digital realm.
Black founders are responding to this need by creating AI models that prioritize Black information sources and reflect their communities' linguistic and cultural nuances. For example, Tamar Huggins' Spark Plug translates educational material into African American Vernacular English (AAVE), ensuring that Black students see themselves in their education and are more engaged as a result.
Addressing Bias in AI
The resistance to inclusive AI often stems from a discomfort with change and a reluctance to acknowledge systemic biases. Those who view inclusion as a problem need to interrogate why they feel threatened by efforts to make AI more representative. Just as we fought for representation in toys and media, we must continue to push for equity in technology. Creating inclusive AI is about more than just avoiding bias; it’s about actively promoting diversity and ensuring that AI benefits everyone. Companies like CDIAL.AI are addressing the lack of African languages in AI models by working with native speakers and linguists to build a multilingual voice-first model that supports the continent's diverse speech patterns.
The Future of Inclusive AI
The future of AI is personalized and inclusive. As more Black-owned AI models emerge, we move closer to a world where technology reflects the richness of human diversity. These initiatives fill a crucial gap and set a precedent for developing AI, with equity and representation at the forefront.
Building inclusive AI requires a deliberate and thoughtful approach. It means involving diverse voices in the development process, using culturally relevant data, and continuously striving to understand and respect the experiences of all users. By doing so, we can create AI tools that truly serve everyone, fostering a more equitable and inclusive digital future.
---
By embracing an intersectional lens and equitable approach in AI development, we can ensure that these powerful tools reflect and respect the diverse experiences of all communities. Representation in AI is not just about avoiding bias; it’s about affirming identities and empowering users. Those resistant to these changes should examine why inclusion feels like a threat, and recognize that a truly inclusive AI benefits us all.
