The linked article is my research project for the AI Safety Fundamentals course. As I waited for active moderator approval of the preprint, Erich Grunewald beat me to writing a post with the same point. Luckily, our arguments largely complement each other, which is nicely symbolic of the whole debate.

In summary:

  • AI ethics is more focused on existing problems, AI safety on those arising in the near future. Since these communities build on different intellectual traditions, they view AI risks through different aesthetics.
    •  E.g. AI safety speaks in terms of utility functions, agents and incentives, AI ethics speaks in terms of fairness or accountability.
  • In terms of policy recommendations, these differences don't seem to matter. Unfortunately, the combination of political attention with the tough competition within academia and Big Tech involvement heighten suspicion and incentivize the creation of intellectual coalitions that often form around aesthetics, as the simplest common denominator.
    • This may lead to the feeling that people focused on different problems divert attention from what's really important
  • However, this micropolitics masks the fact that, in practice, AI ethics and AI safety are highly complimentary and both benefit from the shared spotlight of attention.
  • This was well demonstrated with the EU AI act, as:
    1. The act demonstrated there is a consensus on the meta-principles that should guide AI policy.
      1. Particularly the principle that AI developers need to provide sufficient evidence that they are taking reasonable measures to ensure that their technology is beneficial
      2. Therefore, there doesn't need to be an agreement regarding p(doom). If it's obvious that an advanced AI is extremely unlikely to cause a catastrophe, it should be easy to demonstrate. In such a case, the policy rightfully wouldn't slow AI development down. However, if an AI has destructive capabilities and there aren't arguments or experiments that can demonstrate safety, it's good if regulation poses an obstacle to its development.
    2. The act demonstrated the two perspectives offer different reasons for a plethora of important policies.
      1. Both safety and fairness requires a corporate governance that ensures transparent, controllable algorithms and a practical accountability of digital firms
      2. Both perspectives highlight the harms caused by the social power of social media, addictions, manipulation, surveillance, social credit systems or autonomous weapons
    3. The act demonstrated that the increased attention around AI risks gives weight to the voices from both sides.
      1. Studies suggest that mentioning scientific uncertainty (one stemming from known gaps in knowledge) and technical uncertainty (one inherent to statistical models) in science communication has neutral or positive effects on the trust of the source. However, consensus uncertainty, stemming from differences in opinions, has clearly negative effects towards both sides of a debate, particularly if it's heated. Importantly, it's a question of framing whether uncertainty stems from a gap in knowledge or differences among scientists. Therefore, merely friendlier relationships seem beneficial, in order to promote policies in the intersection, as not knowing who to trust fosters inaction.
      2. In practice, we might have witnessed this when a group of MEPs wrote their own version of the FLI open letter, which possibly sped up the EU AI act, even though the group's members expressed skepticism towards the FLI letter x-risk concerns. This may have been allowed by the FLI's letter openness towards both AI ethics and AI safety-based concerns.
  • I ran a tiny survey (n=82) to explore the effects of interaction of AI safety and AI ethics attitudes. I found out the concern for AI safety and AI bias also correlated positively (r = .28). More importantly, when respondents were first asked to think about AI safety, their concern regarding AI bias was not lower in any of the measured dimensions (perceived significance, support for policy, support of research funding) - and vice versa. The interaction effect was only significant in one direction - people who were first asked about AI safety reported higher support for policy targeting AI bias (ß = .23).
  • More evidence based on web interest and themes of other policy & funding initiatives in Erich's post





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In terms of policy recommendations, these differences don't seem to matter.

Maybe I'm nitpicking, but I see this point often and I think it's a little too self-serving. There are definitely policy ideas in both spheres that trade-off against the others. E.g. many AI X-risk policy analysts (used to) want few players to reduce race dynamics, while such concentration of power would be bad for present-day harms. Or keeping significant chip production out of developing countries.

More generally, if governments really took x-risk seriously, they would be willing to sacrifice significant civil liberties, which wouldn't be acceptable at low x-risk estimates.

That's a good note. But it seems to me a little like pointing out there's a friction between a free market policy and a pro-immigration policy because

a) Some pro-immigration policies would be anti-free market (e.g. anti-discrimination law)
b) Americans who support one tend to oppose the other

While that's true, philosophically, the positions support each other and most pro-free market policies are presumably neutral or positive for immigration.

Similarly, you can endorse the principles that guide AI ethics while endorsing less popular solutions because of additional, x-risk considerations. If there are disagreements, they aren't about moral principles, but empirical claims (x-risk clearly wouldn't be an outcome AI ethics proponents support). And the empirical claims themselves ("AI causes harm now" and "AI might cause harm in the future") support each other & correlated in my sample. My guess is that they actually correlate in academia as well.

It seems to me the negative effects of the concentration of power can be eliminated by other policies (e.g. Digital Markets Act, Digital Services Act, tax reforms)

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