Abstract
In domains as disparate as playing Go and predicting the structure of proteins, artificial intelligence (AI) technologies have begun to perform at levels beyond which any humans can achieve. Does this fact represent something lamentable? Does superhuman AI performance somehow undermine the value of human achievements in these areas? Go grandmaster Lee Sedol suggested as much when he announced his retirement from professional Go, blaming the advances of Go-playing programs like AlphaGo for sapping his will to play the game at a high level. In this paper, I attempt to make sense of Sedol’s lament. I consider a number of ways that the existence of superhuman-performing AI technologies could undermine the value of human achievements. I argue there is very little in the nature of the technology itself that warrants such despair. (Compare: does the existence of a fighter jet undermine the value of being the fastest human sprinter?) But I also argue there are several more localized domains where these technologies threaten to displace human beings from being able to achieve valuable things at all. This is a particular worry for those in unequal societies, I argue, given the difficulty of many achievements and the corresponding amount of resources needed to achieve great things.
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Notes
See Lee et al. (2016) for a contemporaneous report.
More recent work has attempted to replicate superhuman performance at multiple games without hand-coding expertise of any kind (even the rules of the game) into the algorithm (Silver et al., 2018).
Yonhap News Agency (2019). As the article mentions, there are also political reasons why Sedol might have announced his retirement from Korean Go. But whatever the true motivation for Sedol’s retirement, the sentiments he expressed latch onto a real concern about the future of human achievement in an era of superhuman AI performance. It is this concern that motivates the remainder of this paper, not Sedol’s actual motivations.
The broader question of how to “align” the values of AI technologies with human interest is a rapidly-expanding field of research (Gabriel, 2020; Peterson, 2019), but there has been little published reflection connecting these concerns to the value of achievement in particular (though see Danaher & Nyholm, 2020, discussed at length below).
Though Bradford’s account is controversial, the basic metaphysics and axiology of achievement (which she presents very clearly) are all we need to get on the table at this moment. If there are aspects of the view we need to modify in light of our reflection on AI performance, we can do so below.
In focusing on Bradford’s account, I am setting aside a dense assortment of theoretical questions concerning achievement. For one thing, Bradford (and authors who respond to her) take achievements to generate value irrespective of whether they contribute to the welfare of the achieving agent. One might, instead, understand an achievement as primarily being good for an agent’s welfare (Portmore, 2008; Scanlon, 1998). There are complicated questions as to how welfare-based and intrinsic-value-based accounts of achievement might interact. While these debates are fascinating, the agent-neutral form of achievement seems to be what is most at stake with worries like Sedol’s, so we shall focus on it here.
For Bradford, for an achievement to be competently caused just is for the agent to have a significant number of justified true beliefs about that achievement (Bradford, 2015, pp. 65–67). I ignore this condition for several reasons. First, it is only difficulty that ultimately contributes to the value of achievement for Bradford (see Hirji, 2019, and ignoring complications about the value of organic unities (see also Hurka, 2020) that would take us very far afield). The value of difficulty will be our exclusive focus in the “The value of difficulty” section. Additionally, I do not find the account of competent causation in terms of justified true belief compelling, preferring instead an account that centers the agent’s capacities and dispositions (as in Sosa, 2007).
Ignoring that, for a creature with a different cognitive makeup, the latter might be quite difficult.
One reason I think this: the underlying axiology of perfectionist value is flexible enough that many antecedent commitments can fit within it. For example, consequentialist leanings are compatible with versions of perfectionism (Hurka, 1993). One can also imagine how to adjust Hurka’s consequentialist theory to take into account the agent-centered prerogatives of nonconsequentialist theories. The important point for technology ethics is one that Rawls (1999, p. 325) makes: perfectionist goods are a kind of good that should be built into any moral theory (to be weighed against other goods).
The program is able to play millions of games in the time it would take a human being to play tens or hundreds (Silver et al., 2018). If anything, playing Go is the easiest thing in the world for AlphaGo.
This is not to take a stand on the thorny question of whether a sufficiently complicated AI technology could have cognition or agency in the right way. Contrary to classic arguments from Searle (1980), I do not see any in-principle reasons why this could not be a possibility, and there are some interesting extant accounts for how this might happen (e.g. List, 2021). Nonetheless, almost everyone agrees that machine learning algorithms as they currently exist lack most of the capacities necessary for agency, and thus for competent causation (though see Danaher, 2020).
My thanks to Josh Shepherd for pushing me on this point.
My thanks to Jake Quilty-Dunn for discussions of this line of reflection.
There are some reasons to push back here, since keeping a “well-oiled machine” running might itself be a genuine achievement.The empirical facts concerning the spread and ubiquity of “bullshit jobs” (Graeber, 2013), however, make this a rather theoretical response.
Similar arguments have also been given outside of the perfectionist account of achievement, most obviously in Experience Machine arguments (Nozick, 1974).
I raise some particular issues for these ideas below, but they are rather applied in scope. A more systematic critique of the supposed undermining of achievement by enhancement can be found in Forsberg and Skelton (2020).
Machine learning can in turn be used to evaluate different variants of the rules of chess, creating a feedback loop that pushes players towards new variants that will keep and attract interest within the broader space of “chess-like games” (Tomašev et al., 2020).
My thanks to an anonymous reviewer for pushing me to make this formulation more precise.
For more on this possibility, and its impact on science, see Buckner (2020).
As an anonymous reviewer points out, though the specific empirical facts cited here are widely discussed and (mostly) accepted, it is possible to contest them. Even so, I think the project sketched in this section is interesting regardless, if for no other reason than as a conditional claim. If the social and political facts are as this section claims, then a version of displacement represents a real threat to the value of widespread human achievement in the era of superhuman AI. How various institutional and social realities intersect with the normative theory of achievement in the era of AI is a broad research project on which I have much more to say, but can only gesture at here due to space limitations.
As theorists of the “leaky pipeline” in academia have long noted; see Cheryan et al. (2017) for a recent example in STEM fields in particular.
Attempts to mitigate the results of algorithmic bias in particular have been attempted, especially in “algorithmic auditing” (see the framework in Raji et al., 2020). But it is unclear how much these internal fixes, originating at and being implemented in companies whose interests are clearly aligned with inequality-driving forces, will be sufficient to alleviate the problem.
For instance, if one thinks the value of achievement is partially or wholly grounded in the enjoyment that we get out of the process of achieving, then the fact that we despair when contemplating the rise of superhuman AI might itself be enough to undermine the value of our achievements. This could be true even if all the arguments presented in this paper are on the right track. While I think this view represents an implausibly subjective view of the value of achievement, more work is needed to tease out the threads of these kinds of downstream issues. I am thankful to an anonymous reviewer for suggesting this line of future work.
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Acknowledgements
My deepest gratitude to Joe Moore, Thomas Lambert, Jake Quilty-Dunn, Josh Shepherd, Anncy Thresher, Jon Vandenburgh, Michael Ball-Blakely, Ting-An Lin, Diana Acosta-Navas, Henrik Kugelberg, Valerie Soon, Anne Newman, Rob Reich, Tom Kelly, Colin Allen, Tony Chemero, and Zvi Biener for comments and conversation that improved this paper immensely. Thanks are also due to audiences at Stanford University, the University of Cincinnati, the University of Pittsburgh, Florida Atlantic University, and the 2021 iteration of the Society for Philosophy and Psychology annual meeting.
Funding
The author was generously supported by a Grant from the Templeton World Charity Foundation (#0467: Practical Wisdom and Intelligent Machines) for the duration of this project.
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Karlan, B. Human achievement and artificial intelligence. Ethics Inf Technol 25, 40 (2023). https://doi.org/10.1007/s10676-023-09713-x
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DOI: https://doi.org/10.1007/s10676-023-09713-x