The Nonlinear Library LW - Key takeaways from our EA and alignment research surveys by Cameron Berg
May 3, 2024
47:42
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Key takeaways from our EA and alignment research surveys, published by Cameron Berg on May 3, 2024 on LessWrong.
Many thanks to Spencer Greenberg, Lucius Caviola, Josh Lewis, John Bargh, Ben Pace, Diogo de Lucena, and Philip Gubbins for their valuable ideas and feedback at each stage of this project - as well as the ~375 EAs + alignment researchers who provided the data that made this project possible.
Background
Last month,
AE Studio launched two surveys:
one for alignment researchers, and
another for the broader EA community.
We got some surprisingly interesting results, and we're excited to share them here.
We set out to better explore and compare various population-level dynamics within and across both groups. We examined everything from demographics and personality traits to community views on specific EA/alignment-related topics. We took on this project because it seemed to be largely unexplored and rife with potentially-very-high-value insights. In this post, we'll present what we think are the most important findings from this project.
Meanwhile, we're also sharing and publicly releasing a
tool we built for analyzing both datasets. The tool has some handy features, including customizable filtering of the datasets, distribution comparisons within and across the datasets, automatic classification/regression experiments, LLM-powered custom queries, and more. We're excited for the wider community to use the tool to explore these questions further in whatever manner they desire.
There are many open questions we haven't tackled here related to the current psychological and intellectual make-up of both communities that we hope others will leverage the dataset to explore further.
(Note: if you want to see all results, navigate to the
tool, select the analysis type of interest, and click 'Select All.' If you have additional questions not covered by the existing analyses, the GPT-4 integration at the bottom of the page should ideally help answer them. The code running the tool and the raw anonymized data are both also publicly available.)
We incentivized participation by offering to donate $40 per eligible[1] respondent - strong participation in both surveys enabled us to donate over $10,000 to both AI safety orgs as well as a number of
different high impact organizations (see here[2] for the exact breakdown across the two surveys). Thanks again to all of those who participated in both surveys!
Three miscellaneous points on the goals and structure of this post before diving in:
1. Our goal here is to share the most impactful takeaways rather than simply regurgitating every conceivable result. This is largely why we are also releasing the data analysis tool, where anyone interested can explore the dataset and the results at whatever level of detail they please.
2. This post collectively represents what we at AE found to be the most relevant and interesting findings from these experiments. We sorted the TL;DR below by perceived importance of findings. We are personally excited about pursuing
neglected approaches to alignment, but we have attempted to be as deliberate as possible throughout this write-up in striking the balance between presenting the results as straightforwardly as possible and sharing our views about implications of certain results where we thought it was appropriate.
3. This project was descriptive and exploratory in nature. Our goal was to cast a wide psychometric net in order to get a broad sense of the psychological and intellectual make-up of both communities. We used standard frequentist statistical analyses to probe for significance where appropriate, but we definitely still think it is important for ourselves and others to perform follow-up experiments to those presented here with a more
tightly controlled scope to replicate and further sharpen t...
Many thanks to Spencer Greenberg, Lucius Caviola, Josh Lewis, John Bargh, Ben Pace, Diogo de Lucena, and Philip Gubbins for their valuable ideas and feedback at each stage of this project - as well as the ~375 EAs + alignment researchers who provided the data that made this project possible.
Background
Last month,
AE Studio launched two surveys:
one for alignment researchers, and
another for the broader EA community.
We got some surprisingly interesting results, and we're excited to share them here.
We set out to better explore and compare various population-level dynamics within and across both groups. We examined everything from demographics and personality traits to community views on specific EA/alignment-related topics. We took on this project because it seemed to be largely unexplored and rife with potentially-very-high-value insights. In this post, we'll present what we think are the most important findings from this project.
Meanwhile, we're also sharing and publicly releasing a
tool we built for analyzing both datasets. The tool has some handy features, including customizable filtering of the datasets, distribution comparisons within and across the datasets, automatic classification/regression experiments, LLM-powered custom queries, and more. We're excited for the wider community to use the tool to explore these questions further in whatever manner they desire.
There are many open questions we haven't tackled here related to the current psychological and intellectual make-up of both communities that we hope others will leverage the dataset to explore further.
(Note: if you want to see all results, navigate to the
tool, select the analysis type of interest, and click 'Select All.' If you have additional questions not covered by the existing analyses, the GPT-4 integration at the bottom of the page should ideally help answer them. The code running the tool and the raw anonymized data are both also publicly available.)
We incentivized participation by offering to donate $40 per eligible[1] respondent - strong participation in both surveys enabled us to donate over $10,000 to both AI safety orgs as well as a number of
different high impact organizations (see here[2] for the exact breakdown across the two surveys). Thanks again to all of those who participated in both surveys!
Three miscellaneous points on the goals and structure of this post before diving in:
1. Our goal here is to share the most impactful takeaways rather than simply regurgitating every conceivable result. This is largely why we are also releasing the data analysis tool, where anyone interested can explore the dataset and the results at whatever level of detail they please.
2. This post collectively represents what we at AE found to be the most relevant and interesting findings from these experiments. We sorted the TL;DR below by perceived importance of findings. We are personally excited about pursuing
neglected approaches to alignment, but we have attempted to be as deliberate as possible throughout this write-up in striking the balance between presenting the results as straightforwardly as possible and sharing our views about implications of certain results where we thought it was appropriate.
3. This project was descriptive and exploratory in nature. Our goal was to cast a wide psychometric net in order to get a broad sense of the psychological and intellectual make-up of both communities. We used standard frequentist statistical analyses to probe for significance where appropriate, but we definitely still think it is important for ourselves and others to perform follow-up experiments to those presented here with a more
tightly controlled scope to replicate and further sharpen t...