Early work on ”Givewell for AI Safety”
EA was founded on the principle of cost-effectiveness. We should fund projects that do more with less; and more generally, spend resources as efficiently as possible. And yet while much interest, funding and resources in EA have shifted towards AI safety, it’s rare to see any cost-effectiveness calculations. The focus on AI safety is based on vague philosophical arguments that the future could be very large and valuable, and thus whatever is done towards this end is worth orders of magnitude more than most short-term effects.
Even if AI safety is the most important problem, you should still want to optimize how resources are spent to achieve maximum impact since there are limited resources.
Global health organizations and animal welfare organizations work hard to measure cost-effectiveness, evaluate charities, make sure effects are counterfactual, run RCTs, estimate moral weights, scope out interventions, and more. Conversely, in AI safety/x-risk/longtermism, very few efforts are spent on measuring impact. It’s hard to find anything public that compares the results of interventions. Perhaps funders make these calculations in private, but one of the things that made Givewell so great was that everything was out in the open. From day 1, you could see Givewell’s thinking on their blog, examine their spreadsheets, and change parameters based on your own thinking.
I’m making a first attempt at a “Givewell for AI safety”. The end goal is to establish units to measure the cost effectiveness of AI safety interventions, similar to DALY/$ in global health. Scott Alexander’s survey from last year is a great example of different kinds of relevant units for AI safety work. Given units like these, a donor or funder can look at the outputs of different AI safety orgs and “buy” certain things for far cheaper than they can for others.
To begin, I’m looking into cost-effectiveness of AI safety communications. I’m starting with communications because it’s easiest to get metrics, and the outputs of comms work are publicly viewable (compared to e.g. AI policy work) and more easily assessable (compared to e.g. technical AI safety work). For this post, I’m going to be specifically focused on Youtube videos, where metrics were easiest to gather; I’m introducing my framework for evaluating different Youtube channels, along with measurements and data I’ve collected.
The end goal metric I propose for AI safety videos cost effectiveness is “quality-adjusted viewer-minute, per dollar spent”. I started by collecting data for channel viewership and costs.
To calculate time spent watching videos, I first wrote a Python script to query the Youtube API for views and view lengths for every video in specific channels. I multiplied these, and adjusted by a factor for the average percentage of a video watched: 33%, based on conversations with creators. I also messaged creators asking them for their numbers, using those directly where possible. For any creators that responded, I included their metrics (which were usually screenshotted, for authenticity).
To measure cost-effectiveness, we also need to estimate the costs of making the videos. In addition to direct costs (such as equipment and editing), I consider the value of time by people producing videos to be a major cost. This is because they could be doing other things such as earning to give. Because of this, I generally ask people to estimate their market rate salaries if they are doing the work unpaid. For example, for AI in Context, this included the salaries paid to 80000 Hours employees for the production of their video. For other creators who had lots of personal savings and weren’t getting paid, I asked for them to include the value of their time.
Some channels and podcasts also produce revenue, through ads and sponsorships. This is a very good thing and is a sign that people want to see the content. In fact, I expect the best content will be able to self-fund after some time, even be profitable. That said, for now, most aren’t profitable and subsist on donations; thus I count revenues as offsetting of costs for cost-effectiveness calculations, because this funding was produced organically, though I’m open to treating this differently.
Before we get into quality adjustments, here’s a snapshot of where different channels stand:
For quality adjustment, there are three factors I introduce: the quality adjustment for the audience, Qa, the quality adjustment for the fidelity of the message, Qf, and the alignment of the message, Qm. So the overall equation is:
Quality-adjusted viewer minute = Views × Video length × Watch % × Qa × Qf × Qm