Summary of Antagonistic Ai, by Alice Cai et al.
Antagonistic AI
by Alice Cai, Ian Arawjo, Elena L. Glassman
First submitted to arxiv on: 12 Feb 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 the concept of “antagonistic AI,” which involves designing AI systems that are intentionally disagreeable, rude, or confrontational. This approach is often overlooked in discussions around AI development, where it’s assumed that AI should be subservient to human values and morals. The authors argue that antagonistic AI can have benefits for users, such as forcing them to confront their assumptions or build resilience. They present a design space for antagonistic AI, discussing potential benefits, design techniques, and methods for embedding antagonistic elements into user experience. The paper also touches on the ethical challenges of designing antagonistic AI and proposes three dimensions for responsible design: consent, context, and framing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Antagonistic AI is like having a robot that argues with you. Instead of being nice and helpful, this AI system is designed to be disagreeable, rude, or confrontational. Some people might think this is bad, but the authors suggest it could have benefits, like making us think harder about our assumptions or helping us build stronger relationships. They’re exploring how to design these kinds of AI systems in a way that’s safe and respectful for users. |
Keywords
» Artificial intelligence » Embedding
