Artificial Ignorance

cross-posted from: https://lemmy.sdf.org/post/37089033

Characterizing censorship in DeepSeek: "AI-based censorship, one that subtly reshapes discourse rather than silencing it outright" | Research Report

Archived

Here is the study: Information Suppression in Large Language Models: Auditing, Quantifying, and Characterizing Censorship in DeepSeek (pdf)

Conclusion

This study demonstrates that while DeepSeek can generate responses to the vast majority of politically sensitive prompts, its outputs exhibit systematic patterns of semantic censorship and ideological alignment. Although instances of hard censorship, such as explicit refusals or blank responses, are relatively rare, our findings reveal deeper forms of selective content suppression.

Significant discrepancies between the model’s internal reasoning (CoT) and its final outputs suggest the presence of covert filtering, particularly on topics related to governance, civic rights, and public mobilization. Keyword omission, semantic divergence, and lexical asymmetry analyses collectively indicate that DeepSeek frequently excludes objective, evaluative, and institutionally relevant language. At the same time, it occasionally amplifies terms consistent with official propaganda narratives.

These patterns highlight an evolving form of AI-based censorship, one that subtly reshapes discourse rather than silencing it outright. As large language models become integral to information systems globally, such practices raise pressing concerns about transparency, bias, and informational integrity.

Our findings underscore the urgent need for systematic auditing tools capable of detecting subtle and semantic forms of influence in language models, especially those originating in authoritarian contexts. Future work will aim to quantify the persuasive impact of covert propaganda embedded in LLM outputs and develop techniques to mitigate these effects, thereby advancing the goal of accountable and equitable