What AI Safety Materials Do ML Researchers Find Compelling?
I (Vael Gates) recently ran a small pilot study with Collin Burns in which we showed ML researchers (randomly selected NeurIPS / ICML / ICLR 2021 authors) a number of introductory AI safety materials, asking them to answer questions and rate those materials.
We selected materials that were relatively short and disproportionally aimed at ML researchers, but we also experimented with other types of readings. Within the selected readings, we found that researchers (n=28) preferred materials that were aimed at an ML audience, which tended to be written by ML researchers, and which tended to be more technical and less philosophical.
In particular, for each reading we asked ML researchers (1) how much they liked that reading, (2) how much they agreed with that reading, and (3) how informative that reading was. Aggregating these three metrics, we found that researchers tended to prefer (Steinhardt > [Gates, Bowman] > [Schulman, Russell]), and tended not to like Cotra > Carlsmith. In order of preference (from most preferred to least preferred) the materials were:
“More is Different for AI” by Jacob Steinhardt (2022) (intro and first three posts only)
“Why I Think More NLP Researchers Should Engage with AI Safety Concerns” by Sam Bowman (2022)
“Frequent arguments about alignment” by John Schulman (2021)
“Of Myths and Moonshine” by Stuart Russell (2014)
“Why alignment could be hard with modern deep learning” by Ajeya Cotra (2021) (feel free to skip the section “How deep learning works at a high level”)
“AI timelines/risk projections as of Sept 2022” (first 3 pages only)
Christiano (2019), Cotra (2021), and Carlsmith (2021) are well-liked by EAs anecdotally, and we personally think they’re great materials. Our results suggest that materials EAs like may not work well for ML researchers, and that additional materials written by ML researchers for ML researchers could be particularly useful. By our lights, it’d be quite useful to have more short technical primers on AI alignment, more collections of problems that ML researchers can begin to address immediately (and are framed for the mainstream ML audience), more technical published papers to forward to researchers, and so on.
More Detailed Results
For the question “Overall, how much did you like this content?”, Likert 1-7 ratings (I hated it (1) - Neutral (4) - I loved it (7)) roughly followed:
Steinhardt > Gates > [Schulman, Russell, Bowman] > [Christiano, Cotra] > Carlsmith
For the question “Overall, how much do you agree or disagree with this content?”, Likert 1-7 ratings (Strongly disagree (1) - Neither disagree nor agree (4) - Strongly agree (7)) roughly followed:
Steinhardt > [Bowman, Schulman, Gates, Russell] > [Cotra, Carlsmith]
For the question “How informative was this content?”, Likert 1-7 ratings (Extremely noninformative (1) - Neutral (4) - Extremely informative (7)) roughly followed:
Steinhardt > Gates > Bowman > [Cotra, Christiano, Schulman, Russell] > Carlsmith
The combination of the above questions led to the overall aggregate summary (Steinhardt > [Gates, Bowman] > [Schulman, Russell]) as preferred readings listed above.
In the qualitative responses about the readings, there were some recurring criticisms, including: a desire to hear from AI researchers, a dislike of philosophical approaches, a dislike of a focus on existential risks or an emphasis on fears, a desire to be “realistic” and not “speculative”, and a desire for empirical evidence.