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Feedback in creativity support tools can help crowdworkers to improve theirideations. However, current feedback methods require human assessment fromfacilitators or peers. This is not scalable to large crowds. We proposeInterpretable Directed Diversity to automatically predict ideation quality anddiversity scores, and provide AI explanations - Attribution, ContrastiveAttribution, and Counterfactual Suggestions - to feedback on why ideations werescored (low), and how to get higher scores. These explanations providemulti-faceted feedback as users iteratively improve their ideations. Weconducted formative and controlled user studies to understand the usage andusefulness of explanations to improve ideation diversity and quality. Usersappreciated that explanation feedback helped focus their efforts and provideddirections for improvement. This resulted in explanations improving diversitycompared to no feedback or feedback with scores only. Hence, our approach opensopportunities for explainable AI towards scalable and rich feedback foriterative crowd ideation and creativity support tools.

Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation