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Accepted Paper:
Design anthropology, algorithmic bias, and new possibilities: Co-designing an art recommendation algorithm
Matt Artz
(Drew University)
Paper short abstract:
This paper details how design anthropology was used to imagine new possibilities and co-design a patent-pending recommendation algorithm for the art industry.
Paper long abstract:
As algorithms become increasingly responsible for discovering information, how we choose to design them will significantly impact our collective lived experience. One example is how algorithmic bias affects the estimated 50 million people that make up the creator economy. This group of independent creators is financially dependent on recommender systems to suggest their content. Currently, most recommender system designs produce rich-get-richer dynamics, resulting in structural inequalities that favor some over others. This paper details how design anthropology was used to imagine new possibilities and co-design a patent-pending recommendation algorithm for the art industry.