Recent discourse in the field of AI-driven collective intelligence has raised an interesting point for consideration. Namely that the maturing of the discipline has brought with it an increasingly narrow gaze in its approach to using artificial intelligence to further its goals.
This is both a valid concern and one that has serious ramifications if it not addressed in any reasonable amount of time.
At first, it may seem odd to pin diversity as a core problem in this field. Indeed, the ‘collective’ within the name collective intelligence implies a distinct opposition to homogeneity.
After all, drawing on a wide range of thoughts, ideas, and expertise is what allows collectives who set out to solve a problem or achieve a goal to thrive and push for real change.
But as has been made clear in the ongoing conversations around this topic, it is a lack of diversity in the AI practices used to assist collective intelligence that is a cause for concern.
The cited problem is an overreliance on certain types of AI methods.
If a majority of collective intelligence outlets could be gathered and then asked how they utilise AI within their knowledge generation and problem-solving tasks, the answer would be distinctly similar.
AI is used in most cases to order and make sense of vast datasets, with machine learning acting as the tool by which structure is forged from chaos.
The reason for this is obvious, to take tasks humans aren’t capable of and relegate them to an AI.
What is also obvious is why such a proliferation of this tactic has occurred in recent years.
As an increased number of exterior industries have begun embracing the potential for machine learning to assist in them furthering their own goals.
And within this expansion lies the catalyst for fading diversity in the use of AI within collective intelligence.
As collective intelligence has further entrenched itself in more and more industries, it has naturally become beholden to the direction and intent of the sphere it now operates within.