Diversifying AI usage in collective intelligence

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.