Summary of A Roadmap to Pluralistic Alignment, by Taylor Sorensen et al.
A Roadmap to Pluralistic Alignment
by Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, Yejin Choi
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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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 This paper proposes a roadmap to aligning artificial intelligence (AI) systems with pluralistic human values. The authors identify three ways to define and operationalize pluralism: Overton models that present multiple perspectives, Steerably models that can adapt to different viewpoints, and Distributionally models that are calibrated to specific populations. They also formalize three classes of benchmarks for evaluating pluralistic AI: multi-objective, trade-off steerable, and jury-pluralistic. The authors argue that current alignment techniques may not be sufficient for achieving pluralism in AI systems, citing empirical evidence from their own experiments and other studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI systems are being designed to serve everyone, but this requires aligning them with diverse human values. This paper shows how to make language models more inclusive by presenting different perspectives or adapting to different viewpoints. The authors also introduce new ways to test these models, making sure they’re fair and accurate for different groups of people. |
Keywords
» Artificial intelligence » Alignment
