Epistemic status: High uncertainty; This is exploratory work; Our goal is to provide possible research directions rather than offering solutions.

This is shared work. Most of the part on neural correlates is by Jan. Most of the parts on behavior and high-level considerations are by Marius.

Background

I (Marius) was listening to the 80k podcast with Chris Olah on his work on interpretability. When he talked about neural circuits, he said they found many circuits similar to structures in the human brain (some are also quite different). The question that I can’t unthink since then is “What evidence would we need to see in Neural Networks that would convince us of suffering in a morally relevant way?”. So I teamed up with Jan to explore the question.

To clarify, we don’t care about whether they suffer in a similar way that humans do or to the same extent. The question that interests us is “Do neural networks suffer?” and, more importantly, “How would we tell?”.

Broadly, we think there are three different approaches to this problem: a) Neural correlates, b) Behavioural data, and c) high-level considerations.

In this post, we want to find out if there are any avenues that give us reasonable answers or if you just run into a wall, like with most other questions about suffering, patienthood, etc.

It’s not super clear what we are looking for exactly. In general, we are looking for all kinds of evidence that can potentially update our probability of NN suffering.

Summary of the results:

Neural Correlates: Neuroscience helps to de-confuse some terminology, but has not (yet) unambiguously identified a unique neural correlate for suffering. Pain is much better characterized as a “homeostatic behavioral drive” with associated cognitive routines. Since pain usually leads to suffering, finding the homeostatic behavioral drive might also be indicative of suffering in NNs. Finally, the PANAS measure from psychology might provide comparability to humans.
Behavior: Looking at the response of NNs to different stimuli might yield some evidence about their ability to suffer. In language models, it is unclear how to test for the existence of suffering. But testing if GPT-3, for example, has a consistent concept of suffering or observing its answers to the PANAS questions might be a start. For RL agents we can create tests comparable to animal studies. For example, one could look at the react

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Suffering