  The rapid adoption of generative Artificial Intelligence (AI) tools that can
generate realistic images or text, such as DALL-E, MidJourney, or ChatGPT, have
put the societal impacts of these technologies at the center of public debate.
These tools are possible due to the massive amount of data (text and images)
that is publicly available through the Internet. At the same time, these
generative AI tools become content creators that are already contributing to
the data that is available to train future models. Therefore, future versions
of generative AI tools will be trained with a mix of human-created and
AI-generated content, causing a potential feedback loop between generative AI
and public data repositories. This interaction raises many questions: how will
future versions of generative AI tools behave when trained on a mixture of real
and AI generated data? Will they evolve and improve with the new data sets or
on the contrary will they degrade? Will evolution introduce biases or reduce
diversity in subsequent generations of generative AI tools? What are the
societal implications of the possible degradation of these models? Can we
mitigate the effects of this feedback loop? In this document, we explore the
effect of this interaction and report some initial results using simple
diffusion models trained with various image datasets. Our results show that the
quality and diversity of the generated images can degrade over time suggesting
that incorporating AI-created data can have undesired effects on future
versions of generative models.
