Hi all. This post outlines my concerns over the effectiveness of alignment-based solutions to AI safety problems. My basic worry is that any rearticulation of human values, whether by a human or an AI, necessarily reshapes them. If this is the case, alignment is not sufficient for AI safety, since an aligned AI still challenge humanity’s autonomy over its normative conceptions of ethics. Additionally, AI’s ability to represent institutions and its association with “science,” “technology,” and other nebulous (but revered) concepts gives a single model an unprecedented amount of influence over the totality of human values.

At the very least, value drift should be acknowledged as a structural issue with all alignment solutions. At most, solutions like computational governance should limit the provenance of advanced models as much as possible.

I put TL; DRs after each section - feel free to take advantage!

Intro and topic overview

There’s a lot of discussion in AI alignment literature about feedback loops, bias amplification, and the like. The idea is that if a model makes misaligned predictions, the model’s application reifies this bias, making it more significantly encoded in training data and, by extension, more prevalent in future predictions.

Brian Christian’s example is crime prediction models. Statistical modeling shows that “crime” (that is, crime as recorded through arrest records), is more likely in low income neighborhoods. But, police departments might react to these models by sending more police to poorer neighborhoods. Doing so heightens arrest rates, as it would in any neighborhood with a greater police presence, which heightens the disparity of recorded crimes between affluent and low-income neighborhoods. As a result, future iterations of crime prediction models correlate income level and arrest rates more significantly; police departments increase their presence in these neighborhoods; and the correlation of crime and income deepens further as the cycle continues.

This picture glosses over two key implications of feedback loops. One is that bias amplification impacts ideology. Take an example of bias amplification in Practical Deep Learning for Coders: social media content recommendation algorithms. YouTube, and other social media websites, learn a user’s preferences from their browsing history and offer new suggestions accordingly. But, ...

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