arXiv:2606.24890v1 Announce Type: new Abstract: Can a small group of volunteers shape how AI systems discuss animal welfare, just by editing Wikipedia? We show that they can. Wikipedia appears in nearly every major language model training dataset and is weighted more heavily than web-crawled text. The Pro-Animal Wikipedians (PAW), a group of advocates who add sourced animal welfare content to relevant articles, have made 125 edits across 115 pages. Using gradient-based data attribution (Bergson; MAGIC), we traced how these edits influence language model behavior. TrackStar retrieval attributio
The increasing reliance of large language models on internet data, particularly Wikipedia, makes pinpointing specific influence vectors critical as AI's societal impact grows.
This study demonstrates how targeted, small-scale editorial efforts on foundational data sources can disproportionately influence AI value alignment and narrative generation at scale.
The understanding of how LLM values are shaped shifts from broad data ingestion to acknowledging the significant leverage of curated content sources and expert communities within them.
- · Advocacy groups
- · Specialized content creators
- · AI ethicists
- · Data attribution technology
- · AI developers ignoring data provenance
- · Organizations relying on uncurated LLM outputs
AI model developers will need to implement more sophisticated data provenance tracking and bias mitigation strategies for training data.
There will be increased strategic competition among various interest groups to proactively 'seed' foundational knowledge bases like Wikipedia to influence future AI outputs.
The concept of 'digital sovereignty' might expand to include control and curation of global foundational data sources to ensure national or ideological alignment of AI systems.
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