  Feedback in creativity support tools can help crowdworkers to improve their
ideations. However, current feedback methods require human assessment from
facilitators or peers. This is not scalable to large crowds. We propose
Interpretable Directed Diversity to automatically predict ideation quality and
diversity scores, and provide AI explanations - Attribution, Contrastive
Attribution, and Counterfactual Suggestions - to feedback on why ideations were
scored (low), and how to get higher scores. These explanations provide
multi-faceted feedback as users iteratively improve their ideations. We
conducted formative and controlled user studies to understand the usage and
usefulness of explanations to improve ideation diversity and quality. Users
appreciated that explanation feedback helped focus their efforts and provided
directions for improvement. This resulted in explanations improving diversity
compared to no feedback or feedback with scores only. Hence, our approach opens
opportunities for explainable AI towards scalable and rich feedback for
iterative crowd ideation and creativity support tools.
