(Disclaimer: Post written in a personal capacity. These are personal hot takes and do not in any way represent my employer's views.)

TL;DR: I do not think we will produce high reliability methods to evaluate or monitor the safety of superintelligent systems via current research paradigms, with interpretability or otherwise. Interpretability seems a valuable tool here and remains worth investing in, as it will hopefully increase the reliability we can achieve. However, interpretability should be viewed as part of an overall portfolio of defences: a layer in a defence-in-depth strategy. It is not the one thing that will save us, and it still won’t be enough for high reliability.

Introduction

There's a common, often implicit, argument made in AI safety discussions: interpretability is presented as the only reliable path forward for detecting deception in advanced AI - among many other sources it was argued for in [...]

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Outline:

(00:55) Introduction

(02:57) High Reliability Seems Unattainable

(05:12) Why Won't Interpretability be Reliable?

(07:47) The Potential of Black-Box Methods

(08:48) The Role of Interpretability

(12:02) Conclusion

The original text contained 5 footnotes which were omitted from this narration.

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Source:

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Narrated by TYPE III AUDIO.