Artificial intelligence (AI) systems are increasingly influencing various aspects of our lives, from hiring and lending to education and law enforcement. However, these systems often perpetuate existing social biases, particularly against Black people. The issue stems from the data used to train AI models, which can reflect and amplify societal prejudices.
Large language models like ChatGPT have been found to associate African American English with negative traits, revealing a hidden bias that can impact decision-making processes. For instance, a study noted that when prompted with text written in African American English, these models consistently associated it with negative traits, more so than the worst stereotypes people openly held in the 1930s.
The lack of representation of Black people in AI development and training data contributes significantly to these biased outcomes. Historically, Black voices and perspectives have been underrepresented in the data used to train AI models. Much of the formal text used in training data, such as news articles and books, has skewed away from Black perspectives due to who was writing it.
Moreover, AI models have been known to exhibit bias in various areas, including:
- Facial Recognition: Misidentifying individuals with darker skin tones at higher rates than those with lighter skin tones, leading to wrongful arrests and other issues.
- Hiring Tools: Preferring male candidates over female candidates due to biased training data.
- Criminal Justice: Predictive policing algorithms disproportionately targeting minority communities.
Ultimately, addressing AI bias requires a multifaceted approach that involves increasing representation, developing more inclusive AI systems, and promoting transparency and accountability.
