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The Alignment Journal publishes research on the scientific foundations of understanding, predicting, and steering artificial intelligent systems. As of today, submissions are open to everyone.
What we do differently:
- We pay reviewers, with bonuses for speed and insight.
- Review discussion is confidential, but every accepted paper is published with a “review summary” to distill the insights generated.
- Each paper is judged against our acceptance bar, not against other submissions.
- Papers under review are listed publicly, and researchers can nominate themselves to review, so each paper can reach the reviewers who know the topic best.
We seek work that builds theoretical or otherwise principled analyses of agency, robustness, incentives, interpretability, generalization, and long-term behavior, and whose significance survives the deletion of any particular model, dataset or benchmark. Empirical investigations of such ideas are welcome, and negative results are encouraged.
We are especially interested in approaches that could apply to future superintelligent systems that interact and self-improve.
You can submit your preprint in any format. Submissions are rolling and open now on the website.
If you are instead interested in reviewing or serving as an action editor (basically: an area chair), create an account on our website.
Links to submit, our scope and the reasoning behind the Journal are in the reply ⬇️
What we do differently:
- We pay reviewers, with bonuses for speed and insight.
- Review discussion is confidential, but every accepted paper is published with a “review summary” to distill the insights generated.
- Each paper is judged against our acceptance bar, not against other submissions.
- Papers under review are listed publicly, and researchers can nominate themselves to review, so each paper can reach the reviewers who know the topic best.
We seek work that builds theoretical or otherwise principled analyses of agency, robustness, incentives, interpretability, generalization, and long-term behavior, and whose significance survives the deletion of any particular model, dataset or benchmark. Empirical investigations of such ideas are welcome, and negative results are encouraged.
We are especially interested in approaches that could apply to future superintelligent systems that interact and self-improve.
You can submit your preprint in any format. Submissions are rolling and open now on the website.
If you are instead interested in reviewing or serving as an action editor (basically: an area chair), create an account on our website.
Links to submit, our scope and the reasoning behind the Journal are in the reply ⬇️
