Read the recent Vox article about effective altruism ("EA") and longtermism and I'm once again struck by how *obvious* it is that these folks are utterly failing at ceding any power & how completely mismatched "optimization" is from the goals of doing actual good in the world.
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Just a few random excerpts, because it was so painful to read...
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"Oh noes! We have too much money, and not enough actual need in today's world."
First: This is such an obvious way in which insisting on only funding the MOST effective things is going to fail. (Assuming that is even knowable.)
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Second: Your favorite charity is now fully funded? Good. Find another one. Or stop looking for tax loopholes.
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Third: Given everything that's known about the individual and societal harms of income inequality, how does that not seem to come up?
My guess: These folks feel like they somehow earned their position & the burden of having to give their $$ away.
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Another consequence of taking "optimization" in this space to its absurd conclusion: Don't bother helping people closer to home (AND BUILDING COMMUNITY) because there are needier people we have to go be saviors for.
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Poor in the US/UK/Europe? Directly harmed by the systems making our homegrown billionaires so wealthy? You're SOL, because they have a "moral obligation" to use the money they amassed exploiting you to go help someone else.
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"Oh noes! The movement is now dominated by a few wealthy individuals, and so the amount of 'good' we can do is depending on what the stock market does to their fortunes.
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And yet *still* they don't seem to notice that massive income inequality/the fact that our system gives rise to billionaires is a fundamental problem worth any attention.
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Once again: If the do-gooders aren't interested in shifting power, no matter how sincere their desire to go good, it's not going to work out well.
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And that's before we even get into the absolute absurdity that is "longtermism". This intro nicely captures the way in which it is self-congratulatory and self-absorbed:
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"Figuring out which charitable donations addressing actual real-world current problems are "most" effective is just too easy. Look at us, we're "solving" the "hard" problem of maximizing utility into the far future!! We are surely the smartest, bestest people."
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And then of course there's the gambit of spending lots of money on AI development to ... wait for it ... prevent the development of malevolent AI.
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To his credit, the journalist does point out that this is kinda sus, but then he also hops right in with some #AIhype:
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Yes, we are seeing lots of applications of pattern matching of big data, and yes we are seeing lots of flashy demos, and yes the "AI" conferences are buried under deluges of submissions and yes arXiv is amassing ever greater piles of preprints.
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But none of that credibly indicates any actual progress towards the feared? coveted? early anticipated? "AGI". One thing is does clearly indicate is massive over-investment in this area.
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If folks with $$ they feel obligated to give to others to mitigate harm in the world were actually concerned with what the journalist aptly calls "the damage that even dumb AI systems can do", there are lots of great orgs doing that work who could use the funding:
Step 1: Lead off with AI hype. AI is "profound"!! It helps people "unlock their potential"!!
There is some useful tech that meets the description in these paragraphs. But I don't think anything is clarified by calling machine translation or information extraction "AI".
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And then another instance of "standing in awe of scale". The subtext here is it's getting bigger so fast --- look at all of that progress! But progress towards what and measured how?
I suggest you read the whole thing, but some pull quotes:
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@danmcquillan "ChatGPT is a part of a reality distortion field that obscures the underlying extractivism and diverts us into asking the wrong questions and worrying about the wrong things." -- @danmcquillan
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"The compulsion to show 'balance' by always referring to AI's alleged potential for good should be dropped by acknowledging that the social benefits are still speculative while the harms have been empirically demonstrated."
@mathbabedotorg I do think there's a positive role for shame in this case --- shame here is reinforcing community values against "experimenting" with vulnerable populations without doing due diligence re research ethics.
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It seems that part of the #BigData#mathymath#ML paradigm is that people feel entitled to run experiments involving human subjects who haven't had relevant training in research ethics—y'know computer scientists bumbling around thinking they have the solutions to everything. >>
There's a certain kind of techbro who thinks it's a knock-down argument to say "Well, you haven't built anything". As if the only people whose expertise counts are those close to the machine. I'm reminded (again) of @timnitGebru 's wise comments on "the hierarchy of knowledge".>>
I've been pondering some recently about where that hierarchy comes from. It's surely reinforced by the way that $$ (both commercial and, sadly, federal research funds) tends to flow --- and people mistaking VCs, for example, as wise decision makers.
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But I also think that some of it has roots in the way different subjects are taught. Math & CS are both (frequently) taught in very gate-keepy ways (think weeder classes) and also students are evaluated with very cut & dried exams.
Trying out You.com because people are excited about their chat bot. First observation: Their disclaimer. Here's this thing we're putting up for everyone to use while also knowing (and saying) that it actually doesn't work.
Second observation: The footnotes, allegedly giving the source of the information provided in chatbot style, are difficult to interpret. How much of that paragraph is actually sourced from the relevant page? Where does the other "info" come from?
A few of the queries I tried returned paragraphs with no footnotes at all.
Chatbots-as-search is an idea based on optimizing for convenience. But convenience is often at odds with what we need to be doing as we access and assess in formation.