Summary of Ai Can Enhance Creativity in Social Networks, by Raiyan Abdul Baten et al.
AI Can Enhance Creativity in Social Networks
by Raiyan Abdul Baten, Ali Sarosh Bangash, Krish Veera, Gourab Ghoshal, Ehsan Hoque
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores whether peer recommendation engines can improve creative performances in self-organizing social networks. To achieve this, it addresses data collection and intervention design challenges. A trained model predicting ideation performance is used to build SocialMuse, which maximizes predicted performances for peer recommendations. The study finds that treatment networks leveraging SocialMuse outperform AI-agnostic control networks in creativity measures. This decentralization of inspiration sources helps inspired ideas stand out better. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at if online tools can help people get creative ideas from each other. It solves some tricky problems to collect the right data and design the tool. The tool, called SocialMuse, uses information about how people work together to give them suggestions for new ideas. The study shows that when people use this tool, they do better at being creative than when they don’t. This is because the tool helps spread around who gets inspired by whom. |
