Childhood Dreams

“What do you want to be when you grow up?” Our installation aims to explore and expose biases in text-to-image models while simultaneously providing viewers with fine-grained control to mitigate these biases. Acknowledging biases and striving for diversity and inclusion at the same time.

We believe our display both challenges preconceived notions and stereotypes about who can occupy certain professions and celebrates diverse aspirations and the notion everyone should have the freedom and opportunity to be whom they want to be, regardless of gender and ethnicity. Children/childhood both represent the future and a naive view of the world, where dreams and aspirations transcend social norms and the answers to the question: “What do you want to be when you grow up?” are limitless, embodying a true celebration of diversity and inclusion.

Participants are welcomed to upload an image (or take a picture of themselves) to the online demo, and provide a childhood dream/profession text prompt. The resulting generated final image along with a ‘comic strip’ that depicts the morphing journey from the original image to the generated one, is then provided. Users have further control of the generated image, by choosing the image style e.g. watercolour, oil painting etc. Participants can also query the timeline of text-to-image models at the bottom of the space, to produce different outputs for their childhood dream/profession text prompt. The ability to query different text-to-image models invites participants to see the incremental changes made to reduce biased image generations, that usually depict stereotypical outputs of professions e.g. an image of a woman along with the text prompt ‘doctor’ would generate a male image. While users can see the changes in generated images, we also wish to highlight we still have a long way to go with centring inclusivity to reduce bias in generative models.

By showcasing the results of these text-to-image driven edits, we prompt viewers to reflect on the potential consequences of these biases on young individuals as they grow up and pursue their aspirations. We wish to emphasise the importance of addressing and mitigating biases within AI technology to create a more diverse, inclusive and equitable future and highlight the need to ensure that AI systems don't perpetuate or amplify societal biases.

The installation aims to spark conversations and raise awareness among the conference attendees about the importance of diversity and inclusivity in AI. By displaying outputs with mitigated biases (that the users can have control over), in addition to the stereotypical outputs, we wish to give the stage to diverse portrayals of professions, as a hopeful glimpse of what could be.

We hope that through this installation we can foster dialogue about the need for inclusivity and diversity in the development of AI systems.