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Creative Culture and Machine Learning
Negar Rostamzadeh · Cheng-Zhi Anna Huang · Mark Riedl

Mon Dec 05 02:00 PM -- 04:30 PM (PST) @ Virtual

Creative domains render a big part of modern society, having a significant influence on economy and cultural life. During the last decade, fast development of ML technologies such as Generative models, led to creation of multiple creative applications. In this tutorial, we talk about co-creativity and generative art in computer vision, NLP, interactive music generation and the interplay between these modalities .

While there are opportunities for ML to empower artists to create and distribute their work, there are risks and harms when using these technologies in cultural contexts. These include harms arising from tools intended to support the creative process (e.g. biased or unsafe output, such as deepfakes) or harms incurred in creative output (e.g. individual or systemic inequity). At the same time, these systems can have broader and long-term social implications on the shape and diversity of culture more generally, which we discuss in this tutorial.

Finally in this tutorial, we discuss open questions on co-creation process, interplay between modalities, assessment of creative systems, and broader impact of these technologies and potential harms that can stem from these models.

Author Information

Negar Rostamzadeh (Google)
Cheng-Zhi Anna Huang (Google Brain)
Mark Riedl (Georgia Institute of Technology)

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