Platform for Research through the Arts and Sciences

How can we create anti-colonial approaches to AI?


How can ‘slow’ principles help us arrive at better ways of designing, deploying and engaging with the technology across different fields?


What can a Slow AI become?


We are excited to share with you the launch of “Slow AI,” a collaboration between the Visual Methodologies Collective at Amsterdam University of Applied Sciences and the Algorithmic Cultures Research Group at Gerrit Rietveld Academie, Amsterdam. Originating from Mariana Fernandez Mora’s ongoing research into decoloniality in AI, this project will focus on laying the ground for developing strategies to address colonial and extractive histories embedded in current AI systems by introducing the concept of slowness to a fast technology. The project will do this through a series of working sessions and material-based research workshops in collaboration with the ARIAS Artificial Worlds group which will culminate in a publication presented together with a symposium.



As Artificial Intelligence becomes increasingly prevalent in every aspect of our lives, it is crucial to address the way it amplifies existing colonial, extractive, and biased structures embedded in it. Issues such as the homogeneity of AI creators, biases, and the environmental impact of the technology are some examples of the urgent need for innovative approaches that counter act them. In response, the Slow AI project introduces the concept of ‘slowness’ to this fast technology, understanding it beyond a temporal metric and rather as a change in modes of engagement that resist notions of speed, efficiency, and optimisation rooted in its colonial and extractive history. It aims to challenge the prevailing trends of extraction, rapid consumption, immediate gratification, and the relentless pursuit of efficiency that have characterised the digital era, particularly within AI.



This research is informed by scholars like Virginia Eubanks, Kate Crawford, Safiya Noble, Ruha Benjamin, and James Bridle looking to emphasise the importance of addressing underlying power dynamics and systemic issues in AI from a multi-disciplinary perspective.




Mariana Fernández Mora (HvA)

Sabine Niederer (HvA)

Flavia Dzodan (Rietveld Academie)

Maarten Groen (HvA)

Zachary Formwalt (Rietveld Academie)

Andy Dockett (HvA)

Janine armin (HvA)



In collaboration with ARIAS Amsterdam and generously funded by the Centre of Expertise Creative Innovation, Slow AI aims to contribute to a more equitable and sustainable technological landscape.



For updates on the research and public events, subscribe to the ARIAS Artificial Worlds newsletter here.



A look at the rich history of local media-making in and around the Bijlmer neighbourhood in Amsterdam-Zuidoost.

Amsterdam-Zuidoost has a long tradition of media-making as a way to be heard, represent themselves, have influence, and create a feeling of home. From the pirate radio stations of the 1970’s all the way to the podcasts and digital radio stations of today, the practice of making and listening to radio strengthens a community identity and contributes to a sense of belonging. Thinking through the broader issue of auditory culture in Amsterdam-Zuidoost, participatory exercises and workshops within the community take a closer look at the listening needs of the local community.

What is being listened to, and why? And does radio still have a place in the listening behaviour of the young people? 

Explore the outcomes of these exercises and workshops at the Research Station itself this spring, where its various drawers contain research materials for each line of inquiry.

Other partners:

An experiment in collaborating with AI to shed light on climate imaginaries. with the Visual Methodologies Collective.

Keywords: climate, artificial intelligence, non-linear narratives, digital ecology, machine learning

“Making climate visible” was the original name of this research and a continuation of a summer school on “Get the picture. Digital methods for visual research” at UvA in 2018. Researchers considered platform-specific methods of collecting, analyzing, and visualizing information for both visual platforms and platforms with a visual component. A meta-part of this project was to try to develop a visual log of this research project. In academic research, when protocols are created they look more “clean” and straightforward than the research process as a whole. In the arts, on the other hand, the process logs are more creative and less structured. This research project first examined and intersected these different approaches.

Since then the project has developed into the research ‘Climate Futures’, as an experiment in collaborating with AI to shed light on climate imaginaries. In the project, AI functions as our co-author, who has (machine) learned about climate imaginaries on the basis of training sets of climate fiction literature, indigenous climate change stories, climate-themed visual arts, and Hollywood ‘climate disaster’ film trailers. Andy Dockett, Carlo De Gaetano, and Sabine Niederer of the Visual Methodologies Collective design query to prompt the machine to create new climate imaginaries, in text, and in visual form. Subsequently, they edit these machine-generated cli-fi narratives and translate them into short stories, podcasts, and artwork.

The following machines and algorithms have been used in the Climate Futures project to date:

Tesla T4 (UUID: GPU-ef1d6b8c-7543-5969-4126-316eabeed5f9)

Tesla K80 (UUID: GPU-c7194ecb-e0a8-c862-1d76-5c6e46847652)

Open AI’s GPT-2 345M language model

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks