Published on June 24, 2021 3:31 PM GMT

Epistemic status: Fairly uncertain. May contain errors, probabilities might not be calibrated.

Introduction

This document reviews a number of organizations in the longtermist ecosystem, and poses and answers a number of questions which would have to be answered to arrive at a numerical estimate of their impact. My aim was to see how useful a "quantified evaluation" format in the longtermist domain would be.

In the end, I did not arrive at GiveWell-style numerical estimates of the impact of each organization, which could be used to compare and rank them. To do this, one would have to resolve and quantify the remaining uncertainties for each organization, and then convert each organization's impact to a common unit [1, 2].

In the absence of fully quantified evaluations, messier kinds of reasoning have to be used and are being used to prioritize among those organizations, and among other opportunities in the longtermist space. But the hope is that reasoning and reflection built on top of quantified predictions might prove more reliable than reasoning and reflection alone.

In practice, the evaluations below are at a fairly early stage, and I would caution against taking them too seriously and using them in real-world decisions as they are. By my own estimation, of two similar past posts, 2018-2019 Long Term Future Fund Grantees: How did they do? had 2 significant mistakes, as well as half a dozen minor mistakes, out of 24 grants, whereas Relative Impact of the First 10 EA Forum Prize Winners had significant errors in at least 3 of the 10 posts it evaluated.

To make the scope of this post more manageable, I mostly did not evaluate organizations included in Lark's yearly AI Alignment Literature Review and Charity Comparison posts, nor meta-organizations [3].

Evaluated organizations

Alliance to Feed the Earth in Disasters

Epistemic status for this section: Fairly sure about the points related to ALLFED's model of its own impact. Unsure about the points related to the quality of ALLFED's work, given that I'm relying on impressions from others.

With respect to the principled case for an organization to be working on the area:

What is the probability of a (non-AI) catastrophe which makes ALLFED's work relevant (i.e., which kills 10% or more of humanity, but not all of humanity) over the next 50 to 100 years?

How much does the value of the future diminish in such a catastrophe?

How does this compare to work in other areas?

With respect to the execution details:

Is ALLFED making progress in its "feeding everyone no matter what" agenda?

Is that progress on the lobbying front, or on the research front?

Is ALLFED producing high-quality research? On a Likert scale of 1-5, how strong are their papers and public writing?

Is ALLFED cost-effective?

Given that ALLFED has a large team, is it a positive influence on its team members? How would we expect employees and volunteers to rate their experience with the organization?

Execution details about ALLFED in particular

Starting from a quick review as a non-expert, I was inclined to defer to ALLFED's own expertise in this area, i.e., to trust their own evaluation that their own work was of high value, at least compared to other possible directions which could be pursued within their cause area. Per their ALLFED 2020 Highlights, they are researching ways to quickly scale alternative food production, at the lowest cost, in the case of large catastrophes, i.e., foods which could be produced for several years if there was a nuclear war which blotted out the sun.

However, when talking with colleagues and collaborators, some had the impression that ALLFED was not particularly competent, nor its work high quality. I would thus be curious to see an assessment by independent experts about how valuable their work seems in comparison to other work in their area, or to potential work which could be done.

In 2020, ALLFED also did some work related to the COVID-19 pandemic...

Published on June 24, 2021 3:31 PM GMT

Epistemic status: Fairly uncertain. May contain errors, probabilities might not be calibrated.

Introduction

This document reviews a number of organizations in the longtermist ecosystem, and poses and answers a number of questions which would have to be answered to arrive at a numerical estimate of their impact. My aim was to see how useful a "quantified evaluation" format in the longtermist domain would be.

In the end, I did not arrive at GiveWell-style numerical estimates of the impact of each organization, which could be used to compare and rank them. To do this, one would have to resolve and quantify the remaining uncertainties for each organization, and then convert each organization's impact to a common unit [1, 2].

In the absence of fully quantified evaluations, messier kinds of reasoning have to be used and are being used to prioritize among those organizations, and among other opportunities in the longtermist space. But the hope is that reasoning and reflection built on top of quantified predictions might prove more reliable than reasoning and reflection alone.

In practice, the evaluations below are at a fairly early stage, and I would caution against taking them too seriously and using them in real-world decisions as they are. By my own estimation, of two similar past posts, 2018-2019 Long Term Future Fund Grantees: How did they do? had 2 significant mistakes, as well as half a dozen minor mistakes, out of 24 grants, whereas Relative Impact of the First 10 EA Forum Prize Winners had significant errors in at least 3 of the 10 posts it evaluated.

To make the scope of this post more manageable, I mostly did not evaluate organizations included in Lark's yearly AI Alignment Literature Review and Charity Comparison posts, nor meta-organizations [3].

Evaluated organizations

Alliance to Feed the Earth in Disasters

Epistemic status for this section: Fairly sure about the points related to ALLFED's model of its own impact. Unsure about the points related to the quality of ALLFED's work, given that I'm relying on impressions from others.

With respect to the principled case for an organization to be working on the area:

What is the probability of a (non-AI) catastrophe which makes ALLFED's work relevant (i.e., which kills 10% or more of humanity, but not all of humanity) over the next 50 to 100 years?

How much does the value of the future diminish in such a catastrophe?

How does this compare to work in other areas?

With respect to the execution details:

Is ALLFED making progress in its "feeding everyone no matter what" agenda?

Is that progress on the lobbying front, or on the research front?

Is ALLFED producing high-quality research? On a Likert scale of 1-5, how strong are their papers and public writing?

Is ALLFED cost-effective?

Given that ALLFED has a large team, is it a positive influence on its team members? How would we expect employees and volunteers to rate their experience with the organization?

Execution details about ALLFED in particular

Starting from a quick review as a non-expert, I was inclined to defer to ALLFED's own expertise in this area, i.e., to trust their own evaluation that their own work was of high value, at least compared to other possible directions which could be pursued within their cause area. Per their ALLFED 2020 Highlights, they are researching ways to quickly scale alternative food production, at the lowest cost, in the case of large catastrophes, i.e., foods which could be produced for several years if there was a nuclear war which blotted out the sun.

However, when talking with colleagues and collaborators, some had the impression that ALLFED was not particularly competent, nor its work high quality. I would thus be curious to see an assessment by independent experts about how valuable their work seems in comparison to other work in their area, or to potential work which could be done.

In 2020, ALLFED also did some work related to the COVID-19 pandemic...

Published on June 23, 2021 6:35 AM GMT

tldr: You can check for the consistent use of units to find assumptions and/or errors within calculations. I use this on GiveDirectly's Cost Effective Analysis by GiveWell to find two implicit assumptions that impact the result. I think they should probably be included explicitly within the model, and that this technique could be used for other Cost Effectiveness Analysis. If you are not interested in the specifics of how the effectiveness of charities are calculated, you will probably find this read a waste of time.

Numerical models could describe the world, or it might not describe the world. However, there's an interesting way that you can work out whether your model does describe the world without even looking at any data.

To do that, we turn to the field of Dimensional Analysis. Dimensional Analysis is based off really simple ideas. Basic Dimensional Analysis pretty much has two rules:

You can only equate, add or subtract numbers that have the same units

Multiplying/dividing numbers means multiplying/dividing their units

We use dimensional analysis all the time. If I have 4 apples per bag, and 5 bags, then we 4×5=20 apples. In this case we multiplied the units together

This is the second rule above. We however, cannot add units that are different. It doesn't make sense to say that I have 4 apples per bag + 5 bags.

We do not know what the units of the result would be. This is the first rule above. What's great about physical equations is that you can vary the units of the equation and it still means the same thing. Say instead of talking about apples, you cut each apple in half and talk about apple halves. This changes the units from apples to apple halves. So we would have 8 apple halves per bag and 5 bags. How many apple halves do you have? Well, we have 8×5=40 apple halves, which is exactly the same prediction as 20 apples. The fact that we can change our units without changing the meaning of a physical equation I'll call "scale invariance".

If you have equations that are not physical, then that law can be violated. For instance, if we assumed (wrongly) that we can add the apples per bag and bags to get apples.

Then scale invariance will be violated. Because in the first example, if you have 4 apples per bag, and 5 bags, this would predict you would have 4+5=9 apples in total. However, if we start talking about apple halves, and say we have 8 apple halves per bag, then the equation would give 8+5=13 apples halves. 13 apple halves is not the same as 9 apples. Therefore scale invariance has been violated and this equation therefore must be wrong.

If dimensional analysis is not followed, then simply changing the units of your equation would change it's results! An equation that follows these rules is deemed "physical".

In physics, this is such a powerful tool in finding incorrect equations, that it's possible to derive many equations by looking at the units of the parameters alone. I was particularly blown away by an example of deriving the range equation from the units of it's parameters on Wikipedia.

I'm from a software background, so I like to consider this as type checking math. Hence the title.

There are a lot of long calculations done in Cost Effectiveness Analysis (CEAs). As of such, I investigated as to whether these calculations were physical for GiveDirectly. That is, do these calculations abide by the rules of Dimensional Analysis.

Dimensional Analysis has two important but distinct concepts, "units" and "dimensions". Examples of dimensions include "length", "time", "money". Examples of units include "meters", "feet", "seconds", "years" etc. Each unit belongs to a dimension.

A quick overview of GiveDirectly's CEA

Before going further, if you're not familiar with GiveWell's CEAs, here's a quick overview of the way GiveDirectly's cost effectiveness is calculated. It's deliberately only the parts that are needed to understand this post. If you want the full details, you can...

Published on June 23, 2021 6:35 AM GMT

tldr: You can check for the consistent use of units to find assumptions and/or errors within calculations. I use this on GiveDirectly's Cost Effective Analysis by GiveWell to find two implicit assumptions that impact the result. I think they should probably be included explicitly within the model, and that this technique could be used for other Cost Effectiveness Analysis. If you are not interested in the specifics of how the effectiveness of charities are calculated, you will probably find this read a waste of time.

Numerical models could describe the world, or it might not describe the world. However, there's an interesting way that you can work out whether your model does describe the world without even looking at any data.

To do that, we turn to the field of Dimensional Analysis. Dimensional Analysis is based off really simple ideas. Basic Dimensional Analysis pretty much has two rules:

You can only equate, add or subtract numbers that have the same units

Multiplying/dividing numbers means multiplying/dividing their units

We use dimensional analysis all the time. If I have 4 apples per bag, and 5 bags, then we 4×5=20 apples. In this case we multiplied the units together

This is the second rule above. We however, cannot add units that are different. It doesn't make sense to say that I have 4 apples per bag + 5 bags.

We do not know what the units of the result would be. This is the first rule above. What's great about physical equations is that you can vary the units of the equation and it still means the same thing. Say instead of talking about apples, you cut each apple in half and talk about apple halves. This changes the units from apples to apple halves. So we would have 8 apple halves per bag and 5 bags. How many apple halves do you have? Well, we have 8×5=40 apple halves, which is exactly the same prediction as 20 apples. The fact that we can change our units without changing the meaning of a physical equation I'll call "scale invariance".

If you have equations that are not physical, then that law can be violated. For instance, if we assumed (wrongly) that we can add the apples per bag and bags to get apples.

Then scale invariance will be violated. Because in the first example, if you have 4 apples per bag, and 5 bags, this would predict you would have 4+5=9 apples in total. However, if we start talking about apple halves, and say we have 8 apple halves per bag, then the equation would give 8+5=13 apples halves. 13 apple halves is not the same as 9 apples. Therefore scale invariance has been violated and this equation therefore must be wrong.

If dimensional analysis is not followed, then simply changing the units of your equation would change it's results! An equation that follows these rules is deemed "physical".

In physics, this is such a powerful tool in finding incorrect equations, that it's possible to derive many equations by looking at the units of the parameters alone. I was particularly blown away by an example of deriving the range equation from the units of it's parameters on Wikipedia.

I'm from a software background, so I like to consider this as type checking math. Hence the title.

There are a lot of long calculations done in Cost Effectiveness Analysis (CEAs). As of such, I investigated as to whether these calculations were physical for GiveDirectly. That is, do these calculations abide by the rules of Dimensional Analysis.

Dimensional Analysis has two important but distinct concepts, "units" and "dimensions". Examples of dimensions include "length", "time", "money". Examples of units include "meters", "feet", "seconds", "years" etc. Each unit belongs to a dimension.

A quick overview of GiveDirectly's CEA

Before going further, if you're not familiar with GiveWell's CEAs, here's a quick overview of the way GiveDirectly's cost effectiveness is calculated. It's deliberately only the parts that are needed to understand this post. If you want the full details, you can...

Published on June 21, 2021 9:30 PM GMT

On 14th June 2021 Hilary Greaves and Will MacAskill published an update of their September 2019 paper, The case for strong longtermism. The original paper also had a video counterpart.

The original paper argued for axiological strong longtermism (AL), providing examples of interventions that avoided the “washing out hypothesis”. It claimed that AL was robust to plausible deviations from popular axiological and decision-theoretic assumptions, considered which decision situations fall within the scope of AL, and argued for deontic strong longtermism on account of very large axiological stakes.

The new paper strengthens existing points and introduces some new content. I briefly summarise (what I see as) some of the most interesting/important differences between the new and old paper below, with a focus on what is new rather than what is strengthened. In the comments section I provide some of my own thoughts on these differences.

What is new?

A new view on the best objections to strong longtermism

In the original paper, Greaves and MacAskill state that they regard the washing-out hypothesis as “the most serious objection to axiological strong longtermism”. The washing-out hypothesis states that the expected instantaneous value differences between available actions decay with time from the point of action, and decay sufficiently fast that the near-future effects tend to be the most important contributor to expected value. In other words, Greaves and MacAskill considered the intractability objection to be the strongest objection.

However, in the new paper this is no longer the case. Instead, the authors state that “the weakest points in the case for axiological strong longtermism” are:

The assessment of numbers for the cost-effectiveness of particular attempts to benefit the far future (I take this to be the “arbitrariness” issue)

The appropriate treatment of cluelessness

The question of whether an expected value approach to uncertainty is too “fanatical” in this context

They state that these issues in particular would benefit from further research.

As outlined in the comments, I think that this identification of the biggest weaknesses in the argument for ASL is the most useful contribution of this second version of the paper.

A new definition for ASL

The original paper defined axiological strong longtermism (AL) as:

In a wide class of decision situations, the option that is ex ante best is contained in a fairly small subset of options whose ex ante effects on the very long-run future are best.

The new paper abbreviates axiological strong longtermism as ‘ASL’ and defines it as:

In the most important decision situations facing agents today,

(i) every option that is near-best overall is near-best for the far future, and

(ii) every option that is near-best overall delivers much larger benefits in the far future than in the near future.

As I discuss in the comments, as far as I can tell this new definition does pretty much the same job as the old definition, but is clearer and more precise.

Addressing cluelessness

The new paper attempts to address the concern that we are clueless about the very long-run effects of our actions which, if true, would undermine both ASL and DSL (deontic strong longtermism). The authors note that there are several quite distinct possibilities in the vicinity of the “cluelessness” worry:

Simple cluelessness

Conscious unawareness

Imprecision

Arbitrariness

Ambiguity aversion

For each of these they argue that, in their view, ASL is not in fact undermined. However, they express some uncertainty with regards to arbitrariness stating that they “regard the quantitative assessment of the crucial far-future-related variables as a particularly important topic for further research”.

Addressing fanaticism

The new paper attempts to address the concern that the arguments for ASL and DSL rests problematically on tiny probabilities of enormous payoffs.

The authors deny that, on a soc...

Published on June 21, 2021 9:30 PM GMT

On 14th June 2021 Hilary Greaves and Will MacAskill published an update of their September 2019 paper, The case for strong longtermism. The original paper also had a video counterpart.

The original paper argued for axiological strong longtermism (AL), providing examples of interventions that avoided the “washing out hypothesis”. It claimed that AL was robust to plausible deviations from popular axiological and decision-theoretic assumptions, considered which decision situations fall within the scope of AL, and argued for deontic strong longtermism on account of very large axiological stakes.

The new paper strengthens existing points and introduces some new content. I briefly summarise (what I see as) some of the most interesting/important differences between the new and old paper below, with a focus on what is new rather than what is strengthened. In the comments section I provide some of my own thoughts on these differences.

What is new?

A new view on the best objections to strong longtermism

In the original paper, Greaves and MacAskill state that they regard the washing-out hypothesis as “the most serious objection to axiological strong longtermism”. The washing-out hypothesis states that the expected instantaneous value differences between available actions decay with time from the point of action, and decay sufficiently fast that the near-future effects tend to be the most important contributor to expected value. In other words, Greaves and MacAskill considered the intractability objection to be the strongest objection.

However, in the new paper this is no longer the case. Instead, the authors state that “the weakest points in the case for axiological strong longtermism” are:

The assessment of numbers for the cost-effectiveness of particular attempts to benefit the far future (I take this to be the “arbitrariness” issue)

The appropriate treatment of cluelessness

The question of whether an expected value approach to uncertainty is too “fanatical” in this context

They state that these issues in particular would benefit from further research.

As outlined in the comments, I think that this identification of the biggest weaknesses in the argument for ASL is the most useful contribution of this second version of the paper.

A new definition for ASL

The original paper defined axiological strong longtermism (AL) as:

In a wide class of decision situations, the option that is ex ante best is contained in a fairly small subset of options whose ex ante effects on the very long-run future are best.

The new paper abbreviates axiological strong longtermism as ‘ASL’ and defines it as:

In the most important decision situations facing agents today,

(i) every option that is near-best overall is near-best for the far future, and

(ii) every option that is near-best overall delivers much larger benefits in the far future than in the near future.

As I discuss in the comments, as far as I can tell this new definition does pretty much the same job as the old definition, but is clearer and more precise.

Addressing cluelessness

The new paper attempts to address the concern that we are clueless about the very long-run effects of our actions which, if true, would undermine both ASL and DSL (deontic strong longtermism). The authors note that there are several quite distinct possibilities in the vicinity of the “cluelessness” worry:

Simple cluelessness

Conscious unawareness

Imprecision

Arbitrariness

Ambiguity aversion

For each of these they argue that, in their view, ASL is not in fact undermined. However, they express some uncertainty with regards to arbitrariness stating that they “regard the quantitative assessment of the crucial far-future-related variables as a particularly important topic for further research”.

Addressing fanaticism

The new paper attempts to address the concern that the arguments for ASL and DSL rests problematically on tiny probabilities of enormous payoffs.

The authors deny that, on a soc...

Published on June 22, 2021 2:00 AM GMT

(Cross-posted from Hands and Cities)

On a popular view about meta-ethics, what you should value is determined by what an idealized version of you would value. Call this view “idealizing subjectivism.”

Idealizing subjectivism has been something like my best-guess meta-ethics. And lots of people I know take it for granted. But I also feel nagged by various problems with it — in particular, problems related to (a) circularity, (b) indeterminacy, and (c) “passivity.” This post reflects on such problems.

My current overall take is that especially absent certain strong empirical assumptions, idealizing subjectivism is ill-suited to the role some hope it can play: namely, providing a privileged and authoritative (even if subjective) standard of value. Rather, the version of the view I favor mostly reduces to the following (mundane) observations:

If you already value X, it’s possible to make instrumental mistakes relative to X.

You can choose to treat the outputs of various processes, and the attitudes of various hypothetical beings, as authoritative to different degrees.

This isn’t necessarily a problem. To me, though, it speaks against treating your “idealized values” the way a robust meta-ethical realist treats the “true values.” That is, you cannot forever aim to approximate the self you “would become”; you must actively create yourself, often in the here and now. Just as the world can’t tell you what to value, neither can your various hypothetical selves — unless you choose to let them. Ultimately, it’s on you.

I. Clarifying the view

Let’s define the view I have in mind a little more precisely:

Idealizing subjectivism: X is intrinsically valuable, relative to an agent A, if and only if, and because, A would have some set of evaluative attitudes towards X, if A had undergone some sort of idealization procedure.

By evaluative attitudes, I mean things like judgments, endorsements, commitments, cares, desires, intentions, plans, and so on. Different versions of the view can differ in which they focus on.

Example types of idealization might include: full access to all relevant information; vivid imaginative acquaintance with the relevant facts; the limiting culmination of some sort of process of reflection, argument, and/or negotiation/voting/betting between representatives of different perspectives; the elimination of “biases”; the elimination of evaluative attitudes that you don’t endorse or desire; arbitrary degrees of intelligence, will-power, dispassion, empathy, and other desired traits; consistency; coherence; and so on.

Note that the “and because” in the definition is essential. Without it, we can imagine paradigmatically non-subjectivist views that qualify. For example, it could be that the idealization procedure necessarily results in A’s recognizing X’s objective, mind-independent value, because X’s value is one of the facts that falls under “full information.” Idealizing subjectivism explicitly denies this sort of picture: the point is that A’s idealized attitudes make X valuable, relative to A. (That said, views on which all idealized agents converge in their evaluative attitudes can satisfy the definition above, provided that value is explained by the idealized attitudes in question, rather than vice versa.)

“Relative to an agent A,” here, means something like “generating (intrinsic) practical reasons for A.”

II. The appeal

Why might one be attracted to such a view? Part of the appeal, I think, comes from resonance with three philosophical impulses:

A rejection of certain types of robust realism about value, on which value is just a brute feature of the world “out there.”

A related embrace of a kind of Humeanism about means and ends. The world can tell you the means to your ends, but it cannot tell you what ends to pursue — those must in some sense be there already, in your (idealized?) heart.

An aspiration to maintain some kind of deep connection between what’s valuable, and wha...

Published on June 22, 2021 2:00 AM GMT

(Cross-posted from Hands and Cities)

On a popular view about meta-ethics, what you should value is determined by what an idealized version of you would value. Call this view “idealizing subjectivism.”

Idealizing subjectivism has been something like my best-guess meta-ethics. And lots of people I know take it for granted. But I also feel nagged by various problems with it — in particular, problems related to (a) circularity, (b) indeterminacy, and (c) “passivity.” This post reflects on such problems.

My current overall take is that especially absent certain strong empirical assumptions, idealizing subjectivism is ill-suited to the role some hope it can play: namely, providing a privileged and authoritative (even if subjective) standard of value. Rather, the version of the view I favor mostly reduces to the following (mundane) observations:

If you already value X, it’s possible to make instrumental mistakes relative to X.

You can choose to treat the outputs of various processes, and the attitudes of various hypothetical beings, as authoritative to different degrees.

This isn’t necessarily a problem. To me, though, it speaks against treating your “idealized values” the way a robust meta-ethical realist treats the “true values.” That is, you cannot forever aim to approximate the self you “would become”; you must actively create yourself, often in the here and now. Just as the world can’t tell you what to value, neither can your various hypothetical selves — unless you choose to let them. Ultimately, it’s on you.

I. Clarifying the view

Let’s define the view I have in mind a little more precisely:

Idealizing subjectivism: X is intrinsically valuable, relative to an agent A, if and only if, and because, A would have some set of evaluative attitudes towards X, if A had undergone some sort of idealization procedure.

By evaluative attitudes, I mean things like judgments, endorsements, commitments, cares, desires, intentions, plans, and so on. Different versions of the view can differ in which they focus on.

Example types of idealization might include: full access to all relevant information; vivid imaginative acquaintance with the relevant facts; the limiting culmination of some sort of process of reflection, argument, and/or negotiation/voting/betting between representatives of different perspectives; the elimination of “biases”; the elimination of evaluative attitudes that you don’t endorse or desire; arbitrary degrees of intelligence, will-power, dispassion, empathy, and other desired traits; consistency; coherence; and so on.

Note that the “and because” in the definition is essential. Without it, we can imagine paradigmatically non-subjectivist views that qualify. For example, it could be that the idealization procedure necessarily results in A’s recognizing X’s objective, mind-independent value, because X’s value is one of the facts that falls under “full information.” Idealizing subjectivism explicitly denies this sort of picture: the point is that A’s idealized attitudes make X valuable, relative to A. (That said, views on which all idealized agents converge in their evaluative attitudes can satisfy the definition above, provided that value is explained by the idealized attitudes in question, rather than vice versa.)

“Relative to an agent A,” here, means something like “generating (intrinsic) practical reasons for A.”

II. The appeal

Why might one be attracted to such a view? Part of the appeal, I think, comes from resonance with three philosophical impulses:

A rejection of certain types of robust realism about value, on which value is just a brute feature of the world “out there.”

A related embrace of a kind of Humeanism about means and ends. The world can tell you the means to your ends, but it cannot tell you what ends to pursue — those must in some sense be there already, in your (idealized?) heart.

An aspiration to maintain some kind of deep connection between what’s valuable, and wha...

Published on June 21, 2021 4:53 PM GMT

In this post I will make the case that digital marketing is under-utilized by EA orgs as well as provide some example use cases.

My hope is that this post leads to EA orgs testing the below or similar strategies.

A large part of what Effective Altruism is trying to do is to change people’s beliefs and behaviors. Digital advertising is one tool for achieving this goal. The fact that corporations, governments, and nonprofits repeatedly invest millions of dollars in digital marketing programs is evidence of their efficacy.

A couple notes:

I work at Google/YouTube helping large advertisers run Google and YouTube Ads. For that reason this post does not touch on Facebook/Instagram//Twitter/TikTok, but I am sure there are large opportunities there as well.

This post is focused on paid advertising.

Cost estimates are based on previous experience and industry benchmarks, but costs vary based on the geography, season, tactic etc.

If you plan on running any of the strategies described below, please reach out to me so we can coordinate with other charities that are planning on running similar strategies.

If your EA org would like to explore running a digital marketing campaign please don’t hesitate to reach out to me at [email protected].

Search Ads

Every year, millions of people ask Google questions related to charity, poverty, animal welfare, AI safety, etc. If we can direct those people to EA websites, they will get EA answers to their questions.

Google gives registered charities $10K/month in free advertising credits. Of a sample of ~10 EA charities, only one was fully using this Google Ads Grant.

With a little extra knowledge, spending the Google Ads Grant becomes much easier than described in previous posts (1, 2).

If you work at an EA org and would like help spending your full Google Ads Grant please fill out this survey.

Image/display Ads

If your organization has a target audience and you know of websites that audience visits, display ads could be a very cost-effective way for your charity to achieve its goals.

Example use case: Founders Pledge wants to spread the word about their pledge. They identify three websites frequented by founders and advertise on those websites. A standard benchmark for an image/display ad is $2 per 1,000 impressions (an impression is when an ad is served). This strategy would break even at one pledge per 1,000,000,000 impressions served. (Assumes advertising cost of $2 per 1,000 impressions and an average pledge size of $2M, which is based on ~$3B pledged and ~1,500 pledgers).

While the optimal number of impressions served will be much less than 1,000,000,000, it is very likely higher than 0.

In addition to sending founders to the Founders Pledge website, this tactic would also increase awareness of the pledge thereby making it easier for their outreach team to sign on new members.

Video Ads

YouTube Ads are a powerful and inexpensive way to deliver visual and audio messages to targeted audiences.

You can target users using any combination of the following:

Geography (radius targeting, zip/postal code, state)

Household income (top 10%, 11-20%, etc.)

Search history on Google and YouTube (e.g. users that searched for “best charity” or “factory farming” in the last 30 days)

Types of websites visited (e.g. users that have visited the websites of large nonprofits)

YouTube channels being watched at the time the impression serves (contextual targeting)

Demographics (age, gender)

Example use cases:

Org A wants to boost enrollment in EA University groups. They have a member student record a simple 6 second selfie video inviting students to the group. They target 18-24 year-olds within a 10 mile radius of their target universities. They could expect to pay ~$0.01 per person reached.

Org B wants their charity to be the first charity that comes to people’s minds come Giving Tuesday. They target the top 5 US zip codes by income and layer on a Household Income (top 10%) a...

Published on June 21, 2021 4:53 PM GMT

In this post I will make the case that digital marketing is under-utilized by EA orgs as well as provide some example use cases.

My hope is that this post leads to EA orgs testing the below or similar strategies.

A large part of what Effective Altruism is trying to do is to change people’s beliefs and behaviors. Digital advertising is one tool for achieving this goal. The fact that corporations, governments, and nonprofits repeatedly invest millions of dollars in digital marketing programs is evidence of their efficacy.

A couple notes:

I work at Google/YouTube helping large advertisers run Google and YouTube Ads. For that reason this post does not touch on Facebook/Instagram//Twitter/TikTok, but I am sure there are large opportunities there as well.

This post is focused on paid advertising.

Cost estimates are based on previous experience and industry benchmarks, but costs vary based on the geography, season, tactic etc.

If you plan on running any of the strategies described below, please reach out to me so we can coordinate with other charities that are planning on running similar strategies.

If your EA org would like to explore running a digital marketing campaign please don’t hesitate to reach out to me at [email protected].

Search Ads

Every year, millions of people ask Google questions related to charity, poverty, animal welfare, AI safety, etc. If we can direct those people to EA websites, they will get EA answers to their questions.

Google gives registered charities $10K/month in free advertising credits. Of a sample of ~10 EA charities, only one was fully using this Google Ads Grant.

With a little extra knowledge, spending the Google Ads Grant becomes much easier than described in previous posts (1, 2).

If you work at an EA org and would like help spending your full Google Ads Grant please fill out this survey.

Image/display Ads

If your organization has a target audience and you know of websites that audience visits, display ads could be a very cost-effective way for your charity to achieve its goals.

Example use case: Founders Pledge wants to spread the word about their pledge. They identify three websites frequented by founders and advertise on those websites. A standard benchmark for an image/display ad is $2 per 1,000 impressions (an impression is when an ad is served). This strategy would break even at one pledge per 1,000,000,000 impressions served. (Assumes advertising cost of $2 per 1,000 impressions and an average pledge size of $2M, which is based on ~$3B pledged and ~1,500 pledgers).

While the optimal number of impressions served will be much less than 1,000,000,000, it is very likely higher than 0.

In addition to sending founders to the Founders Pledge website, this tactic would also increase awareness of the pledge thereby making it easier for their outreach team to sign on new members.

Video Ads

YouTube Ads are a powerful and inexpensive way to deliver visual and audio messages to targeted audiences.

You can target users using any combination of the following:

Geography (radius targeting, zip/postal code, state)

Household income (top 10%, 11-20%, etc.)

Search history on Google and YouTube (e.g. users that searched for “best charity” or “factory farming” in the last 30 days)

Types of websites visited (e.g. users that have visited the websites of large nonprofits)

YouTube channels being watched at the time the impression serves (contextual targeting)

Demographics (age, gender)

Example use cases:

Org A wants to boost enrollment in EA University groups. They have a member student record a simple 6 second selfie video inviting students to the group. They target 18-24 year-olds within a 10 mile radius of their target universities. They could expect to pay ~$0.01 per person reached.

Org B wants their charity to be the first charity that comes to people’s minds come Giving Tuesday. They target the top 5 US zip codes by income and layer on a Household Income (top 10%) a...