pretraining data safety; responsible AI/ML
This seems to be linked to a classic problem in social science research of finding causal factors. For the most effective strategies, one key is to focus on individual cases - in other words, key strategies are different for each different case, even if it is the same cause area (but for example in different regions.) This do require lots of manual/field research to find nuances, in my own opinion.
Appreciate the post. https://www.pewresearch.org/social-trends/2020/01/09/trends-in-income-and-wealth-inequality/ This in-depth research article suggest the rich are getting richer faster, and suggest "Economic inequality, whether measured through the gaps in income or wealth between richer and poorer households, continues to widen." It matches with your intuition.
I wonder what could be done to really incentive the powerful/high income people to care about contributing more.
With long timeline and less than 10% probability: Hot take is these are co-dependent - prioritizing only extinction is not feasible. Additionally, does only one human exist while all others die count as non-extinction? What about only a group of humans survive? How should this be selected? It could dangerously/quickly fall back to Fascism. It would only likely benefit the group of people with current low to no suffering risks, which unfortunately correlates to the most wealthy group. When we are "dimension-reducing" the human race to one single point, we ignore the individuals. This to me goes against the intuition of altruism.
I fundamentally disagree with the winner-take-all type of cause prioritization - instead, allocate resources to each area, and unfortunately there might be multiple battles to fight.
To analyze people's responses, I can see this question being adjusted to consider prior assumptions: 1. What's your satisfaction on how we are currently doing in the world now? What are the biggest gaps to your ideal world? 2.What's your assessment of timeline + current % of extinction risk due to what?
Some example of large scale deepfakes that is pretty messed up: https://www.pbs.org/newshour/world/in-south-korea-rise-of-explicit-deepfakes-wrecks-womens-lives-and-deepens-gender-divide
Other examples on top of my head is the fake Linkedin profiles.
Not sure how to address the question otherwise; a thought is there might be deepfakes that we cannot detect/tell being deepfakes yet.
It also worries me, in the context of marginal contributions, when some people (not all) start to think of "marginal" as a "sentiment" rather than actual measurements (getting to know those areas, the actual resources, and the amount of spending, and what the actual needs/problems may be) as reasoning for cause prioritization and donations. A sentiment towards a cause area, does not always mean the cause area got the actual attention/resources it was asking for.
Thanks for the piece! Was thinking about this potential effect the other day as well, also for literature. Would think repetition could matter as well - one single exposure to one documentary may not be helpful, but multiple different ones may. Additionally, it would probably be more effective if some part of the documentary make the viewer feel connected personally. But these are conjectures and I am not sure.