This colloquium talk is planned as an in-person event. Registration is only required for non-CEU members.
A now-familiar thought (associated with the idea of “strong AI”) that in the not-too-distant
future, AI may come to far surpass humans’ general cognitive capacities. Some famously worry
that such AI pose an existential risk to humans, either due to indifference to human aims or a
hostility to humans. This paper focuses on a different cluster of questions: a series of questions in
the foundations of ethics raised by the possibility that we ask AI to engage in evaluative
reasoning (e.g., about what is good and bad, right and wrong, etc.). There is a natural epistemic
motive for asking strong AI to engage in evaluative reasoning: one might hope that strong AI
could help us to make progress in addressing persistent evaluative controversies. However,
suppose that strong AIs converge on evaluative conclusions that we are independently inclined to
oppose. For example, AI might come to anti-anthropocentric evaluative conclusions (two
examples: maybe they really prioritize certain cognitive capacities in their evaluations, treating
us the way we treat mosquitos; or maybe they don’t, treating the interests of insects as on a par
with that of highly rational beings). Alternatively, AI might come to radically consequentialist
conclusions, that portray the evaluative significance of, e.g., our relations to our projects and
loved ones as easily swamped. We take such dissatisfaction with (by hypothesis) epistemically
highly credible evaluative conclusions to raise important questions about our attitudes towards
the evaluative, even if we assume a strong sort of realism about evaluative thought and talk. It is
familiar for metaethical antirealists to ask the question “why care about evaluative properties?”
on the supposition that realism is true. And they often argue that this is a reason to reject realism.
However, we think that the sorts of possibility we are canvassing instead makes salient two other
ways of thinking about alienation from evaluative standards. First, it could push us to a
conceptual ethics conclusion that we ought to adopt other (perhaps: more anthropocentric)
evaluative concepts. Second, it might instead push us toward a deep alienation from the
evaluative: that is, we might simply embrace that the existing evaluative concepts capture
what really and truly matters, and find that we simply don’t want our lives or world to be
structured by what really and truly matters, if it involves sufficient sacrifice of what we care
about. In light of these issues, we then reflect on how to best think about what “the” alignment
problem in AI really is, suggesting that there are in fact multiple different “alignment” problems
that are worth wrestling with, and that it is a difficult evaluative question which one to prioritize
in thinking about AI ethics, and why.
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