Position: The Alignment Community is Unintentionally Building a Censor’s Toolkit

Sarah Ball, Phil Hackemann
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:169303-169316, 2026.

Abstract

This position paper argues that modern AI alignment methods – originally designed to prevent harmful output – are dual-use technologies that may easily be misused by malicious actors for censorship and manipulation. By mapping current alignment techniques to the possibility and actual cases of misuse, we show that the quest for a "perfectly aligned" model inadvertently also provides malicious actors with an ever-improving tool for informational dominance. We need to discuss this dual-use potential now, as its risk is exacerbated by rapid user adoption of AI as information provider, economic power asymmetries, and a political landscape that increasingly shifts towards authoritarianism. We conclude by urging the community to consider the intentional misuse of AI alignment mechanisms and propose mitigation strategies to safeguard against this dual-use potential.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-ball26c, title = {Position: The Alignment Community is Unintentionally Building a Censor’s Toolkit}, author = {Ball, Sarah and Hackemann, Phil}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {169303--169316}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/ball26c/ball26c.pdf}, url = {https://proceedings.mlr.press/v306/ball26c.html}, abstract = {This position paper argues that modern AI alignment methods – originally designed to prevent harmful output – are dual-use technologies that may easily be misused by malicious actors for censorship and manipulation. By mapping current alignment techniques to the possibility and actual cases of misuse, we show that the quest for a "perfectly aligned" model inadvertently also provides malicious actors with an ever-improving tool for informational dominance. We need to discuss this dual-use potential now, as its risk is exacerbated by rapid user adoption of AI as information provider, economic power asymmetries, and a political landscape that increasingly shifts towards authoritarianism. We conclude by urging the community to consider the intentional misuse of AI alignment mechanisms and propose mitigation strategies to safeguard against this dual-use potential.} }
Endnote
%0 Conference Paper %T Position: The Alignment Community is Unintentionally Building a Censor’s Toolkit %A Sarah Ball %A Phil Hackemann %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-ball26c %I PMLR %P 169303--169316 %U https://proceedings.mlr.press/v306/ball26c.html %V 306 %X This position paper argues that modern AI alignment methods – originally designed to prevent harmful output – are dual-use technologies that may easily be misused by malicious actors for censorship and manipulation. By mapping current alignment techniques to the possibility and actual cases of misuse, we show that the quest for a "perfectly aligned" model inadvertently also provides malicious actors with an ever-improving tool for informational dominance. We need to discuss this dual-use potential now, as its risk is exacerbated by rapid user adoption of AI as information provider, economic power asymmetries, and a political landscape that increasingly shifts towards authoritarianism. We conclude by urging the community to consider the intentional misuse of AI alignment mechanisms and propose mitigation strategies to safeguard against this dual-use potential.
APA
Ball, S. & Hackemann, P.. (2026). Position: The Alignment Community is Unintentionally Building a Censor’s Toolkit. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:169303-169316 Available from https://proceedings.mlr.press/v306/ball26c.html.

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