Background: This is a general overview of AI safety applied to astronomy, created as a part of the AI Safety North Alignment course 2023. It is intended as a simple overview for people without intermediate AI knowledge. The contents of this text should be seen as a suggestion for how AI safety could be more clearly defined in research, from the perspective of a student with minimal experience in academia or AI research.

Introduction

My reason for publishing this on the LessWrong forum is to get some feedback from others if my reasoning makes sense. I wanted to explore the idea of a better standard in research in relation to AI safety. Personally I feel like finding how the researchers mitigated risk from using AI shouldn't be so complicated. I am a physics student and do not have intermediate knowledge about AI, but learned a bit during the AI Safety North Alignment course. Reading articles in the field of astronomy where AI was used, I found that the explanation of how the risks were mitigated were confusing. One could argue that for advanced research you need to have knowledge from the field the research is from to understand it. In my opinion, since more fields are starting to use AI, the most rational approach would be for the explanation to be more straightforward, so that even a person who doesn't know astronomy so well can understand it. My goal is to try an argue that researchers when using AI for research in astronomy should be more straightforward about how they mitigated risks/errors, and I will do this by applying AI safety concepts. I am not sure if my reasoning makes sense or if this already is common in other fields, but an attempt was made.

Some applications of AI in astronomy

Astronomers have been using AI for decades, such as in 1990 when astronomers from the University of Arizona were among the first to use a neural network, a type of machine learning algorithm model taking inspiration from the brain, to study the shapes of galaxies. Since then, AI has spread into every field of astronomy, and for example generative AI was used to make the first image of a black hole two times sharper. As telescopes improve the amount of data collected increases, making AI algorithms the most viable solution to ever hope going through the data (Impey, 2023).

It is also useful in cosmology, a sub-field of astro...

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