Artificial intelligence, superefficiency and the end of work: a humanistic perspective on meaning in life

Knell, S., & Rüther, M. (2023). 

AI and Ethics.

https://doi.org/10.1007/s43681-023-00273-w

Abstract

How would it be assessed from an ethical point of view if human wage work were replaced by artificially intelligent systems (AI) in the course of an automation process? An answer to this question has been discussed above all under the aspects of individual well-being and social justice. Although these perspectives are important, in this article, we approach the question from a different perspective: that of leading a meaningful life, as understood in analytical ethics on the basis of the so-called meaning-in-life debate. Our thesis here is that a life without wage work loses specific sources of meaning, but can still be sufficiently meaningful in certain other ways. Our starting point is John Danaher’s claim that ubiquitous automation inevitably leads to an achievement gap. Although we share this diagnosis, we reject his provocative solution according to which game-like virtual realities could be an adequate substitute source of meaning. Subsequently, we outline our own systematic alternative which we regard as a decidedly humanistic perspective. It focuses both on different kinds of social work and on rather passive forms of being related to meaningful contents. Finally, we go into the limits and unresolved points of our argumentation as part of an outlook, but we also try to defend its fundamental persuasiveness against a potential objection.

Concluding remarks

In this article, we explored the question of how we can find meaning in a post-work world. Our answer relies on a critique of John Danaher’s utopia of games and tries to stick to the humanistic idea, namely to the idea that we do not have to alter our human lifeform in an extensive way and also can keep up our orientation towards common ideals, such as working towards the good, the true and the beautiful.

Our proposal still has some shortcomings, which include the following two that we cannot deal with extensively but at least want to briefly comment on. First, we assumed that certain professional fields, especially in the meaning conferring area of the good, cannot be automated, so that the possibility of mini-jobs in these areas can be considered.  This assumption is based on a substantial thesis from the

philosophy of mind, namely that AI systems cannot develop consciousness and consequently also no genuine empathy.  This assumption needs to be further elaborated, especially in view of some forecasts that even the altruistic and philanthropic professions are not immune to the automation of superefficient systems. Second, we have adopted without further critical discussion the premise of the hybrid standard model of a meaningful life according to which meaning conferring objective value is to be found in the three spheres of the true, the good, and the beautiful. We take this premise to be intuitively appealing, but a further elaboration of our argumentation would have to try to figure out, whether this trias is really exhaustive, and if so, due to which underlying more general principle.

A pdf is here.

Full transparency, big John Danaher fan.  Regardless, here is my summary:

Humans are meaning makers. We find meaning in our work, our relationships, and our engagement with the world. The article discusses the potential impact of AI on the meaning of work, and I agree that the authors make some good points. However, I think their solution is somewhat idealistic. It is true that social relationships and engagement with the world can provide us with meaning, but these activities will be difficult to achieve, especially in a world where AI is doing most of the work.  We will need ways to cooperate, achieve, and interact to engage in behaviors that are geared toward super-ordinate goals.  Humans need to align their lives with core human principles, such as meaning-making, pattern repetition, cooperation, and values-based behaviors.

• The authors focus on the potential impact of AI on the meaning of work, but they also acknowledge that other factors, such as automation and globalization, are also having an impact.

• The authors' solution is based on the idea that meaning comes from relationships and engagement with the world. However, there are other theories about the meaning of life, such as the idea that meaning comes from self-actualization or from religious faith.

• The authors acknowledge that their solution is not perfect, but they argue that it is a better alternative than Danaher's solution. However, I think it is important to consider all of the options before deciding which one is best.  Ultimately, it will come down to a values-based decision, as there seems to be no one right or correct solution.

Automation, work and the achievement gap

Danaher, J., Nyholm, S. 

AI Ethics (2020). 

https://doi.org/10.1007/s43681-020-00028-x

Abstract

Rapid advances in AI-based automation have led to a number of existential and economic concerns. In particular, as automating technologies develop enhanced competency, they seem to threaten the values associated with meaningful work. In this article, we focus on one such value: the value of achievement. We argue that achievement is a key part of what makes work meaningful and that advances in AI and automation give rise to a number achievement gaps in the workplace. This could limit people’s ability to participate in meaningful forms of work. Achievement gaps are interesting, in part, because they are the inverse of the (negative) responsibility gaps already widely discussed in the literature on AI ethics. Having described and explained the problem of achievement gaps, the article concludes by identifying four possible policy responses to the problem.

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Conclusion

Achievement is an important part of the well-lived life. It is the positive side of responsibility. Where we blame ourselves and others for doing bad things, we also praise ourselves for achieving positive (or value neutral) things. Achievement is particularly important when it comes to meaningful work. One of the problems with widespread automation is that it threatens to undermine at least three of the four main conditions for achievement in the workplace: it can reduce the value of work tasks; reduce the cost of committing to those work tasks; and sever the causal connection between human effort and workplace outcome. This opens up ‘achievement gaps’ in the workplace. There are, however, some potential ways to manage the threat of achievement gaps: we can focus on other aspects of meaningful work; we can find some ways to retain the human touch in the production of workplace outputs; we can emphasise the importance of teamwork in producing valuable outputs; and we can find outlets for achievement outside of the workplace.