ウェルビーイングに基づいたAIの前向きなビジョンが必要

Imagine yourself a decade ago, jumping directly into the present shock of conversing naturally with an encyclopedic AI that crafts images, writes code, and debates philosophy. Won’t this technology almost certainly transform society — and hasn’t AI’s impact on us so far been a mixed-bag? Thus it’s no surprise that so many conversations these days circle around an era-defining question: How do we ensure AI benefits humanity? These conversations often devolve into strident optimism or pessimism about AI, and our earnest aim is to walk a pragmatic middle path, though no doubt we will not perfectly succeed.

While it’s fashionable to handwave towards “beneficial AI,” and many of us want to contribute towards its development — it’s not easy to pin down what beneficial AI concretely means in practice. This essay represents our attempt to demystify beneficial AI, through grounding it in the wellbeing of individuals and the health of society. In doing so, we hope to promote opportunities for AI research and products to benefit our flourishing, and along the way to share ways of thinking about AI’s coming impact that motivate our conclusions.

The Big Picture

By trade, we’re closer in background to AI than to the fields where human flourishing is most-discussed, such as wellbeing economics, positive psychology, or philosophy, and in our journey to find productive connections between such fields and the technical world of AI, we found ourselves often confused (what even is human flourishing, or wellbeing, anyways?) and from that confusion, often stuck (maybe there is nothing to be done? — the problem is too multifarious and diffuse). We imagine that others aiming to create prosocial technology might share our experience, and the hope here is to shine a partial path through the confusion to a place where there’s much interesting and useful work to be done. We start with some of our main conclusions, and then dive into more detail in what follows.

One conclusion we came to is that it’s okay that we can’t conclusively define human wellbeing. It’s been debated by philosophers, economists, psychotherapists, psychologists, and religious thinkers, for many years, and there’s no consensus. At the same time, there’s agreement around many concrete factors that make our lives go well, like: supportive intimate relationships, meaningful and engaging work, a sense of growth and achievement, and positive emotional experiences. And there’s clear understanding, too, that beyond momentary wellbeing, we must consider how to secure and improve wellbeing across years and decades — through what we could call societal infrastructure: important institutions such as education, government, the market, and academia.

One benefit of this wellbeing lens is to wake us to an almost-paradoxical fact: While the deep purpose behind nearly everything our species does is wellbeing, we’ve tragically lost sight of it. Both by common measures of individual wellbeing (suicide rate, loneliness, meaningful work) and societal wellbeing (trust in our institutions, shared sense of reality, political divisiveness), we’re not doing well, and our impression is that AI is complicit in that decline. The central benefit of this wellbeing view, however, is the insight that no fundamental obstacle prevents us from synthesizing the science of wellbeing with machine learning to our collective benefit.

This leads to our second conclusion: We need plausible positive visions of a society with capable AI, grounded in wellbeing. Like other previous transformative technologies, AI will shock our societal infrastructure — dramatically altering the character of our daily lives, whether we want it to or not. For example, Facebook launched only twenty years ago, and yet social media’s shockwaves have already upended much in society — subverting news media and our informational commons, addicting us to likes, and displacing meaningful human connection with its shell. We believe capable AI’s impact will exceed that of social media. As a result, it’s vital that we strive to explore, envision, and move towards the AI-infused worlds we’d flourish within — ones perhaps in which it revitalizes our institutions, empowers us to pursue what we find most meaningful, and helps us cultivate our relationships. This is no simple task, requiring imagination, groundedness, and technical plausibility — to somehow dance through the minefields illuminated by previous critiques of technology. Yet now is the time to dream and build if we want to actively shape what is to come.

This segues into our final conclusion: Foundation models and the arc of their future deployment is critical. Even for those of us in the thick of the field, it’s hard to internalize how quickly models have improved, and how capable they might become given several more years. Recall that GPT-2 — barely functional by today’s standards — was released only in 2019. If future models are much more capable than today’s, and competently engage with more of the world with greater autonomy, we can expect their entanglement with our lives and society to rachet skywards. So, at minimum, we’d like to enable these models to understand our wellbeing and how to support it, potentially through new algorithms, wellbeing-based evaluations of models and wellbeing training data. Of course, we also want to realize human benefit in practice — the last section of this blog post highlights what we believe are strong leverage points towards that end.

The rest of this post describes in more detail (1) what we mean by AI that benefits our wellbeing, (2) the need for positive visions for AI grounded in wellbeing, and (3) concrete leverage points to aid in the development and deployment of AI in service of such positive visions. We’ve designed this essay such that the individual parts are mostly independent, so if you are interested most in concrete research directions, feel free to skip there.

Beneficial AI grounds out in human wellbeing

Discussion about AI for human benefit is often high-minded, but not particularly actionable, as in unarguable but content-free phrases like “We should make sure AI is in service of humanity.” But to meaningfully implement such ideas in AI or policy requires enough precision and clarity to translate them into code or law. So we set out to survey what science has discovered about the ground of human benefit, as a step towards being able to measure and support it through AI.

Often, when we think about beneficial impact, we focus on abstract pillars like democracy, education, fairness, or the economy. However important, none of these are valuable intrinsically. We care about them because of how they affect our collective lived experience, over the short and long-term. We care about increasing society’s GDP to the extent it aligns with actual improvement of our lives and future, but when treated as an end in itself, it becomes disconnected from what matters: improving human (and potentially all species’) experience.

In looking for fields that most directly study the root of human flourishing, we found the scientific literature on wellbeing. The literature is vast, spanning many disciplines, each with their own abstractions and theories — and, as you might expect, there’s no true consensus on what wellbeing actually is. In diving into the philosophy of flourishing, wellbeing economics, or psychological theories of human wellbeing, one encounters many interesting, compelling, but seemingly incompatible ideas.

For example, theories of hedonism in philosophy claim that pleasure and the absence of suffering is the core of wellbeing; while desire satisfaction theories instead claim that wellbeing is about the fulfillment of our desires, no matter how we feel emotionally. There’s a wealth of literature on measuring subjective wellbeing (broadly, how we experience and feel about our life), and many different frameworks of what variables characterize flourishing. For example, Martin Seligman’s PERMA framework claims that wellbeing consists of positive emotions, engagement, relationships, meaning, and achievement. There are theories that say that the core of wellbeing is satisfying psychological needs, like the need for autonomy, competence, and relatedness. Other theories claim that wellbeing comes from living by our values. In economics, frameworks rhyme with those in philosophy and psychology, but diverge enough to complicate an exact bridge. For example, the wellbeing economics movement largely focuses on subjective wellbeing and explores many different proxies of it, like income, quality of relationships, job stability, etc.

After the excitement from surveying so many interesting ideas began to fade, perhaps unsurprisingly, we remained fundamentally confused about what “the right theory” was. But, we recognized that in fact this has always been the human situation when it comes to wellbeing, and just as a lack of an incontrovertible theory of flourishing has not prevented humanity from flourishing in the past, it need not stand as a fundamental obstacle for beneficial AI. In other words, our attempts to guide AI to support human flourishing must take this lack of certainty seriously, just as all sophisticated societal efforts to support flourishing must do.

In the end, we came to a simple workable understanding, not far from the view of wellbeing economics: Human benefit ultimately must ground out in the lived experience of humans. We want to live happy, meaningful, healthy, full lives — and it’s not so difficult to imagine ways AI might assist in that aim. For example, the development of low-cost but proficient AI coaches, intelligent journals that help us to self-reflect, or apps that help us to find friends, romantic partners, or to connect with loved ones. We can ground these efforts in imperfect but workable measures of wellbeing from the literature (e.g. PERMA), taking as first-class concerns that the map (wellbeing measurement) is not the territory (actual wellbeing), and that humanity itself continues to explore and refine its vision of wellbeing.

More broadly our wellbeing relies on a healthy society, and we care not only about our own lives, but also want beautiful lives for our neighbors, community, country, and world, and for our children, and their children as well. The infrastructure of society (institutions like government, art, science, military, education, news, and markets) is what supports this broader, longer-term vision of wellbeing.

Each of these institutions have important roles to play in society, and we can also imagine ways that AI could support or improve them; for example, generative AI may catalyze education through personal tutors that help us develop a richer worldview, may help us to better hold our politicians to account through sifting through what they are actually up to, or accelerate meaningful science through helping researchers make novel connections. Thus in short, beneficial AI would meaningfully support our quest for lives worth living, in both the immediate and long-term sense.

So, from the lofty confusion of conflicting grand theories, we arrive at something sounding more like common sense. Let’s not take this for granted, however — it cuts through the cruft of abstractions to firmly recenter what is ultimately important: the psychological experience of humans. This view points us towards the ingredients of wellbeing that are both well-supported scientifically and could be made measurable and actionable through AI (e.g. there exist instruments to measure many of these ingredients). Further, wellbeing across the short and long-term provides the common currency that bridges divergent approaches to beneficial AI, whether mitigating societal harms like discrimination in the AI ethics community, to attempting to reinvigorate democracy through AI-driven deliberation, to creating a world where humans live more meaningful lives, to creating low-c