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AI Safety & other: 2021 through 2026

This question is part of the Maximum Likelihood Round of the Forecasting AI Progress Tournament. You can view all other questions in this round here.

arXiv is a repository of electronic preprints approved for posting after moderation, but not full peer review. It consists of scientific papers in the fields of mathematics, physics, astronomy, electrical engineering, computer science, quantitative biology, statistics, mathematical finance and economics, which can be accessed online.

Many machine learning articles will be posted on arXiv before publication. In theoretical computer science and machine learning, over 60% of published papers have arXiv e-prints (Sutton et al. 2017).

AI Safety refers to a field aimed at developing techniques for designing AI systems that do not display unintended and harmful behaviour (Amodei et al., 2016). A related problem is that of (the lack of) transparency and interpretability of complicated ML systems. Transparency and interpretability techniques aim to generate insights about what ML systems are doing. Such techniques may enable meaningful human oversight and in building fair, safe, and aligned AI systems (Olah, 2018).

How many e-prints on AI Safety, interpretability or explainability will be published on arXiv over the 2021-01-01 to 2026-12-31 period?

This question resolves as the total number of Natural Language Processing e-prints published on arXiv over the 2021-01-01 to 2026-12-31 period (inclusive), as per the e-print's "original submission date".

Details of the search query

For the purpose of this question e-prints published under Computer Science that contain the following keywords in "all fields" (i.e. the abstract and title):

"ai safety", "ai alignment", "aligned ai", "value alignment problem", "reward hacking", "reward tampering", "tampering problem", "safe exploration", "robust to distributional shift", "scalable oversight", "explainable AI", "interpretable AI", "explainable model", "verification for machine learning", "verifiable machine learning", "interpretable model", "interpretable machine learning", "cooperative inverse reinforcement learning", "value learning", "iterated amplification", "preference learning", "AI safety via debate", "reward modeling", "logical induction"

The query should include cross-listed papers (papers listed on other subjects besides Computer Science). You can execute the query here.

Running this query for previous years gives:

  • 80 for the calendar year 2017
  • 127 for the calendar year 2018
  • 275 for the calendar year 2019


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