As large Vision-Language Models (VLMs) grow in prominence, ensuring their safe deployment has become critical. Recent studies have explored VLM robustness against jailbreak attacks--techniques that exploit model vulnerabilities to elicit harmful outputs. However, the limited availability of diverse multi-modal data has led current approaches to rely heavily on adversarial or manually crafted images derived from harmful text datasets, which may lack effectiveness and diversity across different contexts. In this paper, we propose a novel jailbreak method named IDEATOR, which autonomously generates malicious image-text pairs for black-box jailbreak attacks. IDEATOR is based on the insight that VLMs themselves could serve as powerful red team models for generating multimodal jailbreak prompts. Specifically, IDEATOR uses a VLM to create targeted jailbreak texts and pairs them with jailbreak images generated by a state-of-the-art diffusion model. Our extensive experiments demonstrate IDEATOR's high effectiveness and transferability. Notably, it achieves a 94% success rate in jailbreaking MiniGPT-4 with an average of only 5.34 queries, and high success rates of 82%, 88%, and 75% when transferred to LLaVA, InstructBLIP, and Meta's Chameleon, respectively. IDEATOR uncovers specific vulnerabilities in VLMs under black-box conditions, underscoring the need for improved safety mechanisms.

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.

Emerging online innovation platforms have enabled large groups of people to collaborate and generate ideas together in ways that were not possible before. However, these platforms also introduce new challenges in helping their members to generate diverse and high quality ideas. In this paper, we enumerate collaboration challenges in crowd innovation: finding inspiration for contributors from a large number of ideas, motivating crowd to contribute to improve group understanding of the problem and solution space, and coordinating collective effort to reduce redundancy and increase quality and breadth of generated ideas. We discuss possible solutions to this problem and present our recent work that addresses some of these challenges using techniques from human computation and machine learning.