  Artificial intelligence (AI) is increasingly utilized in synthesizing
visuals, texts, and audio. These AI-based works, often derived from neural
networks, are entering the mainstream market, as digital paintings, songs,
books, and others. We conceptualize both existing and future human-in-the-loop
(HITL) approaches for creative applications and to develop more expressive,
nuanced, and multimodal models. Particularly, how can our expertise as curators
and collaborators be encoded in AI models in an interactive manner? We examine
and speculate on long term implications for models, interfaces, and machine
creativity. Our selection, creation, and interpretation of AI art inherently
contain our emotional responses, cultures, and contexts. Therefore, the
proposed HITL may help algorithms to learn creative processes that are much
harder to codify or quantify. We envision multimodal HITL processes, where
texts, visuals, sounds, and other information are coupled together, with
automated analysis of humans and environments. Overall, these HITL approaches
will increase interaction between human and AI, and thus help the future AI
systems to better understand our own creative and emotional processes.
