Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation
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Feedback 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 - for deeper feedback on why ideations were scored (low), and how to get higher scores. These explanations provide multi-faceted feedback as users iteratively improve their ideation. We conducted think aloud and controlled user studies to understand how various explanations are used, and evaluated whether explanations 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 predictions only. Hence, our approach opens opportunities for explainable AI towards scalable and rich feedback for iterative crowd ideation.
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