Abstract
A long lineage of computer-assisted design tools has established interaction
paradigms that give full control to the designer over the software. Introduction
of Artificial Intelligence (AI) to this creative process leads to a more co-creative
paradigm, with AI taking a more proactive role. Recent generative approaches
based on deep learning have strong potential as an asset creator and co-creator,
however current algorithms are opaque and burden the designer with making sense
of the output. In order for deep learning to become a colleague that designers can
trust and work with, better explainability, controllability, and interactivity is necessary. We highlight current and potential ways in which explainability can inform
human users in creative tasks and call for involving end-users in the development
of both interfaces and underlying algorithms.
paradigms that give full control to the designer over the software. Introduction
of Artificial Intelligence (AI) to this creative process leads to a more co-creative
paradigm, with AI taking a more proactive role. Recent generative approaches
based on deep learning have strong potential as an asset creator and co-creator,
however current algorithms are opaque and burden the designer with making sense
of the output. In order for deep learning to become a colleague that designers can
trust and work with, better explainability, controllability, and interactivity is necessary. We highlight current and potential ways in which explainability can inform
human users in creative tasks and call for involving end-users in the development
of both interfaces and underlying algorithms.
| Originalsprog | Engelsk |
|---|---|
| Titel | Proceedings of the Human Centered AI workshop at NeurIPS 2022 |
| Antal sider | 3 |
| Publikationsdato | 2022 |
| Status | Udgivet - 2022 |