ML Tarot Project


The booklet for the deck can be found here. You can learn more about this project in my upcoming DIS 2022 pictorial, co-authored with Daniela Rosner:

Caitlin Lustig and Daniela Rosner. “From Explainability to Ineffability? ML Tarot and the Possibility of Inspiriting Design”. Conference on Designing Interactive Systems. ACM, 2022.

Creation (text lightly edited from the DIS pictorial)

To create the ML Tarot deck, I engaged in a multi-stage process which involved using ML (machine learning) to generate descriptions of the cards and then, using another ML algorithm, using those descriptions to generate the images on the cards.

I began by generating descriptions of the cards to put in a booklet. Tarot decks are often accompanied by booklets that help readers interpret the cards. To generate the descriptions, I used GPT-2 (using a copy of a Colab notebook), a large language model trained on a large corpus of web pages, which can generate human-like text. To use GPT-2, the ML algorithm must first be provided some text, and it will then use this text to predict what words should follow. I provided it with short lightly edited descriptions of cards from These sentences from included information about the traditional imagery on the cards and/or the traditional meaning of the cards. GPT-2 used these sentences to generate longer texts. For each card, I used GPT-2 to generate multiple descriptions (in total, about 132 thousand words) and then for each card, I selected the description with the most “imaginative” imagery, narrative, or writing style.

To generate the images shown on each card, I fed key words or quotes from these descriptions into using their VQGAN+CLIP algorithm. This algorithm can generate images based from text prompts. In some cases, I generated multiple images before picking ones that were the most evocative and representative of the textual aesthetic of the descriptions. Lastly, I created a booklet, linked above, with the ML-generated descriptions and printed cards with the ML-generated images.