Data Bugs
AI is a Mirror

Personally - and in Dotdotdot too - we believe that AI is a tool that can be used for good or evil, just like a pen can be used to write poems or war declarations. But how does AI become racist or misogynist? We found the answer in the training phase, where biased data can lead to biased results.

Currently, 90% of the data used to train AI models is produced by just 10% of the population. This results in a dataset that represents a narrow point of view, which is our own. As a Western society, we bring biased concepts into these datasets, which the AI model then learns and reinforces. The more a concept is present in the dataset, the more the algorithm will consider it as the best answer. Eventually, the real problem takes place in what is called Latent Space. A latent space is defined as a multidimensional space that encodes a reduced, meaningful representation of externally observed events. In the latent space, similar samples in the external world are placed next to each other.

The main problem with this approach is representation. As Bernice King said, If you don't think representation matters, you are probably well represented. As Western people, we are aware that we are part of the problem. As designers at Dotdotdot, we know that the best way to communicate complex concepts is to abstract them. So we decided to show the bug in the data by using bugs as data.

Fed up with the simplification of AI as a prompting tool, we decided to spatialize the latent space, giving it a physical dimension. Users were thus able to move within it, understanding the role it plays in image generation. As is the case in latent space, each of the eight insects thus had a conceptual representation, and based on the distance the user had from each insect it had more or less weight on the generated output. Throughout the experience there were then the two different models constantly generating new images, placing each user in front of a visual translation of how diversity in datasets plays a key role.