Diversity is a key ingredient in the recipe for collective intelligence because it brings together a range of perspectives, tools, and abilities; allowing for a more comprehensive approach to problem-solving and decision-making. Gender diversity on corporate boards improves firms’ performance, ethnic diversity produces more impactful scientific research, diverse groups are better at solving crimes, popular juries are less biased than professional judges, and politically diverse editorial teams produce higher-quality Wikipedia articles.
Large language models, like those powering AI systems, rely heavily on datasets or corpora, with a significant part of it based on English content. This dominance is consequential. Just as diverse groups of people yield richer outcomes, an AI trained on diverse linguistic data offers a broader perspective. Each language encapsulates unique thoughts, metaphors, and wisdom. Without diverse linguistic representation, we risk fostering AI systems with limited collective intelligence. The quality, diversity, and quantity of the data they are trained on directly influence their epistemic outputs. Unsurprisingly, large language models struggle to capture long-tail knowledge.
This comes with two major — at least hypothetically — risks: 1) systems that do not fully leverage the knowledge dispersed in the population, 2) the benefits of AI may be more accessible to some groups over others; for instance, speakers of less-dominant languages might not equally benefit from AI’s advancements. It’s not merely about translation; it’s the nuances and knowledge embedded in languages that might be overlooked.
There are also two additional dimensions that could reinforce biases in AI systems: 1) as future models are trained on content that might have been generated by AI, there may be a reinforcing effect where biases present in the initial training data are amplified over time; and 2) techniques such as guided transfer learning may also increase biases if the source model used in transfer learning is trained on biased data.
This introduces a nuanced dimension to the digital divide. Historically, the digital divide was characterized by access to technology, internet connectivity, digital skills, and the socio-economic variables shaping these factors. Yet, with AI, our understanding of what constitutes digital divide should expand. It’s a subtler yet crucial divide that policymakers and development practitioners might not yet fully recognize.
