3. The first priority in charity is to mitigate potential harms caused by how you made your money.
For people working in AI the potential harms of their work are immense, and involve most present and future life, so I think itās reasonable to prioritize mitigating those harms, but I donāt think itās a good general principle for the vast majority of givers.
Suppose an extreme scenario where I make billions of dollars developing a practical form of nuclear fusion, which quickly devalues fossil fuels. A large investor in fossil fuels loses a lot of money as a consequence, and has to cancel their $2M space tourism trip. This is a clear harm to them directly caused by how I made my money. Why should my first priority as a philanthropist be to mitigate this harm, instead of preventing thousands of much poorer people losing a child from preventable disease?
More practically: most harms caused by most of our careers are local (e.g. if I become a doctor, someone else wonāt get to be a doctor) and I think our priority in charity should be to help those that we can help the most, who usually are far removed from any effects of our careers.
5. Give to issues that are close to your heart and/āor your field of expertise, so you understand them better.
I think giving to causes close to my heart is a very flawed moral guide which will predictably lead me to help fewer people and help them less. Largely because by default the issues closest to my heart would be problems like Parkinsonās disease that already receive a lot of resources, and with the same amount of resources I can help far more individuals far more, people who are suffering from issues I was born too lucky to ever see (e.g. tuberculosis kills 1.2 million people per year). Their suffering is just as real, and matters just as much. I think many great points raised by Paul Bloom in Against Empathy also apply here.
I do agree that ceteris paribus one should favor issues that are closer to oneās field of expertise, especially when able to identify particularly promising opportunities, but again, my field of expertise is unlikely to involve the worldās most neglected issues (almost by definition) and we should be mindful of that when evaluating funding opportunities.
4. Beyond that, donāt try to optimize charity too much. Is it better to donate to a political candidate who will change the healthcare system in the long term? Or to donate to sick people who need expensive surgery right now? Optimize a little, but then focus on the giving. In order to optimize charity, you need two things that we donāt have: A calculus of suffering and a crystal ball that tells the future.
I agree that there is a risk of trying to optimize charity too much. In practice I think the main risks are analysis paralysis, or feeling paralyzed by extreme cluelessness, and as a consequence donating much less than otherwise, or not at all.
But there is definitely also a risk of optimizing too little, and I worry that optimizing āa littleā results way fewer individuals being helped. Itās true that without crystal balls and a perfect measure of suffering you canāt be certain of whether a choice is optimific. But the same applies to choices people make in their own lives, where we all agree itās rational to spend more than a little time comparing options.
E.g. If I were diagnosed with a serious medical issue with several treatment options with different risk profiles, nobody would tell me: āThere is no perfect measure of health, and no crystal ball about complications, so just compare a little and then focus on getting some surgery.ā People would advise me to get a second opinion, study the literature, compare hospitals and surgeons, ask for expert advice, and so on. None of this can guarantee the optimal result, but for things we care about, optimizing āa littleā is not enough, and when talking about charitable donations the stakes are extremely high. Unlike hospitals or recommended treatment options, some charities probably do 100x more good per dollar than others, so the returns to comparison are unusually large.
I also imagine that Harari and Sapienship spent quite a bit of time and research before shortlisting those 3 specific recommendations, so itās likely we donāt actually disagree here.
Iām very far from working in the AI industry, so I donāt think Iām the target audience of this post.
In any case, as someone trying to use part of my resources to help others, I agree with much of it (especially points 1. 2. and 6.) but I personally strongly disagree with other points:
For people working in AI the potential harms of their work are immense, and involve most present and future life, so I think itās reasonable to prioritize mitigating those harms, but I donāt think itās a good general principle for the vast majority of givers.
Suppose an extreme scenario where I make billions of dollars developing a practical form of nuclear fusion, which quickly devalues fossil fuels. A large investor in fossil fuels loses a lot of money as a consequence, and has to cancel their $2M space tourism trip.
This is a clear harm to them directly caused by how I made my money. Why should my first priority as a philanthropist be to mitigate this harm, instead of preventing thousands of much poorer people losing a child from preventable disease?
More practically: most harms caused by most of our careers are local (e.g. if I become a doctor, someone else wonāt get to be a doctor) and I think our priority in charity should be to help those that we can help the most, who usually are far removed from any effects of our careers.
I think giving to causes close to my heart is a very flawed moral guide which will predictably lead me to help fewer people and help them less. Largely because by default the issues closest to my heart would be problems like Parkinsonās disease that already receive a lot of resources, and with the same amount of resources I can help far more individuals far more, people who are suffering from issues I was born too lucky to ever see (e.g. tuberculosis kills 1.2 million people per year). Their suffering is just as real, and matters just as much. I think many great points raised by Paul Bloom in Against Empathy also apply here.
I do agree that ceteris paribus one should favor issues that are closer to oneās field of expertise, especially when able to identify particularly promising opportunities, but again, my field of expertise is unlikely to involve the worldās most neglected issues (almost by definition) and we should be mindful of that when evaluating funding opportunities.
I agree that there is a risk of trying to optimize charity too much. In practice I think the main risks are analysis paralysis, or feeling paralyzed by extreme cluelessness, and as a consequence donating much less than otherwise, or not at all.
But there is definitely also a risk of optimizing too little, and I worry that optimizing āa littleā results way fewer individuals being helped. Itās true that without crystal balls and a perfect measure of suffering you canāt be certain of whether a choice is optimific. But the same applies to choices people make in their own lives, where we all agree itās rational to spend more than a little time comparing options.
E.g. If I were diagnosed with a serious medical issue with several treatment options with different risk profiles, nobody would tell me: āThere is no perfect measure of health, and no crystal ball about complications, so just compare a little and then focus on getting some surgery.ā People would advise me to get a second opinion, study the literature, compare hospitals and surgeons, ask for expert advice, and so on. None of this can guarantee the optimal result, but for things we care about, optimizing āa littleā is not enough, and when talking about charitable donations the stakes are extremely high. Unlike hospitals or recommended treatment options, some charities probably do 100x more good per dollar than others, so the returns to comparison are unusually large.
I also imagine that Harari and Sapienship spent quite a bit of time and research before shortlisting those 3 specific recommendations, so itās likely we donāt actually disagree here.