Category Archives: Artificial intelligence

people as clusters of attention

Attention is endangered. It is what Silicon Valley has learned to capture and commoditize. It is what LLMs pretend to offer by speaking in the first-person singular, often in a sycophantic voice. It is what my iPhone takes from me. It is what Donald Trump constantly demands.

To understand why our attention should be valuable to us, we need a satisfactory theory of it. We should not depend on the idea that we have a private, inner self that creates or determines its own attention and owns it like a plot of property. Yet our attention does not belong to Google and Meta or to Donald Trump, and we are worse off when they determine it. Here is an effort at an explanation.

1. The belief in a willing self

It feels as if we decide to do certain things. The reason they occur is that we will them. Other things happen to us, or just happen. For instance, I stand up because I decide to do so, but I fall down because someone pushes me or the leg of my chair breaks.

What am I? I am the thing that wills my own actions.

Sometimes we hear that this theory is “Western” or “modern,” but classical Indian Buddhist thinkers–who disagreed with the theory–nevertheless argued that all sentient beings believe it until they achieve enlightenment. The intended reader of a classical Buddhist text was neither Western nor modern yet believed in a self that willed its own actions. Classical Buddhist authors defined themselves as opponents of other Asian authors who explicitly endorsed this theory, including foundational Hindu texts.

I presume that most or all people believe in a willing self because it makes sense of experience. We are so constituted that we feel that we decide and choose some things, while other things happen to us.

This theory also supports significant and appropriate moral distinctions. We hold ourselves and other people accountable for choices, not for accidents. And just as we value and care for our self–which we credit with making choices–so we value and care for other selves.

When we begin by believing in our own willing selves, we naturally pose questions about other wills. Presumably, other human beings are just like us; to assume otherwise is solipsistic and maybe even psychopathic. But from there, the answers become trickier. Do other animals have selves, and if so, which animals? (My dog seems to, but it’s hard to believe that a bacterium does.) Can a group of human beings or a human institution have a will? How about a computer?

2. Drawbacks of the theory

The theory of a willing self has advantages but also limitations that many people recognize, in principle, even as our experiences keep convincing us that it is true.

For one thing, we have no direct knowledge of the self. It can seem like a magical exception in a universe otherwise determined by the causes that are known to science.

The theory of a self implies a sharp distinction between choices and accidents, even though many–possibly all–intentional behavior seems to be a mix of both. I assume that I have freely decided to stand up, but that behavior resulted from a series of neurological events that were affected, in part, by other people and objects.

Although the theory suggests a binary, the world seems to be shaded in grey. My dog Luca has a similar psychology to mine but not completely the same; a lizard is like Luca but also different from him; and an ant is further along the same continuum. A crowd of humans can have a kind of will, but not exactly like mine. A Large Language Model (LLM) exhibits will-like behavior but isn’t a person.

Finally, the notion of a freely choosing self violates important moral intuitions. It is incompatible with Moral Luck, the idea that we can be better or worse as a result of things that happen without our choosing them. For example, I didn’t choose to be an American citizen led by President Trump, but I am. It is wrong to distance myself from that fact on the ground that I didn’t will it. The theory can also encourage us to care too much about our own selves and to regard our freedom and survival as paramount while making us too judgmental about other people. In Buddhism, an enlightened person has shed the belief in itself.

But it is also problematic to deny the existence of selves in such a way that it no longer seems to matter whether we and other people have agency–or even whether we or they survive. A person is a thing of inestimable value even it’s not quite right to understand it as a self that has a will. And a dog is a being of great value even if it’s not on a par with a human person. Somehow, it must make sense to complain when a person’s private space has been violated.

3. Attention, not self

Here is an alternative. I am inspired by Jonardon Ganeri’s book Attention, Not Self (Oxford 2017), which is primarily an interpretation of Buddhaghosa’s The Path of Purification (written around 450 CE) and other works by this classical Theravada thinker, who (in turn) claimed to be faithfully interpreting the words of the Buddha as recorded in the Pali Canon. Indeed, Buddhaghosa claims that his whole Path of Purification, which is 853 pages long in the English translation by Bhikkhu Nanamoli, is a commentary on the second stanza of Linked Discourses 1.23 (which I have loosely translated here.)

It would be a thorny matter to decide whether I am interpreting Ganeri reasonably well, whether he offers an accurate reading of Buddhaghosa, whether Buddhaghosa is a reliable interpreter of the Pali Canon, and whether the Canon reflects the ideas of the actual Buddha. Instead, I will simply sketch a view that I’ve formed while reading Ganeri.

We can begin with attention. Although this word does not have a self-evident meaning, we use it successfully. Even a toddler can understand the phrase “Pay attention!” When I say my dog’s name, he attends to me, and when he barks, he wants to get my attention. In other words, Luca and I can play language-games involving attention even if he couldn’t learn the word. In this sense, “attention” is much more tractable than “consciousness.”

In its most general sense, attention is some kind of ordering of experience by an organism. An ant can attend to a leaf.

Ganeri argues that our attention has two general aspects: it functions like a window or aperture that removes most of what we could notice so that we are less distracted; and it directs or faces us toward certain phenomena within the window so that we can more deeply understand those things. When I stare at a tree, I am ignoring other objects in my peripheral vision and I am thinking about the tree. “I have reconstructed Pali Buddhist theory as consisting in the claim that the role of attention in experience consists in an exclusion-guided placing together with a directing towards, where there is no incompatibility between them” (Ganeri 117).

This is a general account of attention, at least for human beings. Ganeri further argues that “attention is disunified;” it comes in many forms.

Among the varieties of attention are focal and placed attention, retained attention, reflective attention, attention through language to the world beyond one’s horizons, attention to one’s own mind, attention to the minds of others through their poise and posture, and attention to one’s life in total. These varieties of attention are, as we will see, put to work to explain perception, memory, mindfulness, testimony, introspection, and empathy (Generi, 221).

Each person’s attention is differentiated from others’. For example, only I can remember my own past experiences, which is a particular way of attending. You can learn about my past and possibly even know facts about my past that I don’t know, but I alone can attend to my past as a memory. Likewise, only I can focus on my future as my own, which I do when I plan. I can attend to you in the way that we call empathy, which you cannot offer to yourself.

If you and I are sitting in a lecture, I may be paying attention while your mind is wondering (or vice-versa, of course). If there is a sudden loud noise, such as a thunderclap, both of us may have our attentions captured or “grabbed,” but this may feel different to each of us because I experienced an interrupted lecture while you experienced an interrupted daydream. Compare William James:

for what we hear when the thunder crashes is not thunder pure, but thunder-breaking-upon-silence-and-contrasting-with-it. Our feeling of the same objective thunder, coming in this way, is quite different from what it would be were the thunder a continuation of previous thunder. The thunder itself we believe to abolish and exclude the silence; but the feeling of the thunder is also a feeling of the silence as just gone; and it would be difficult to find in the actual concrete consciousness of man a feeling so limited to the present as not to have an inkling of anything that went before. (James, The Principles of Psychology, 1890, vol. 1, Chapter 9, p. 103.)

There is such a thing as voluntary or intended attention. We can tell by the fact that such attention requires effort. Maybe I am forcing myself to pay attention to the lecture while you are allowing yourself be distracted by someone else in the room, by a feeling of hunger, or by a memory.

James argues that “the question of free-will is insoluble on strictly psychologic grounds” yet there is a clear difference between trying to attend to something and doing so because we failed to try or because something else compelled our attention. The difference matters morally:

The question of fact in the free-will controversy is thus extremely simple. It relates solely to the amount of effort of attention or consent which we can at any time put forth. Are the duration and intensity of this effort fixed functions of the object, or are they not? Now, as I just said, it seems as if the effort were an independent variable, as if we might exert more or less of it in any given case. When a man has let his thoughts go for days and weeks until at last they culminate in some particularly dirty or cowardly or cruel act, it is hard to persuade him, in the midst of his remorse, that he might not have reined them in…. But, on the other hand, there is the certainty that all his effortless volitions are resultants of interests and associations whose strength and sequence are mechanically determined by the structure of that physical mass, his brain; and the general continuity of things and the monistic conception of the world may lead one irresistibly to postulate that a little fact like effort can form no real exception to the overwhelming reign of deterministic law (James, vol; 2, chap 35, p. 497).

Ganeri posits that “Attention is the active organization of experience and action into centred arenas, and Buddhist anatta [the doctrine of no-self] is the claim that there is no room for something real at the centre doing or observing the ordering” (p. 26).

4. Consequences and applications

This theory has the advantage of explaining why each person’s attention is different from others’ without positing a self behind the curtain. It allows us to care whether a given person, including me or you, remains alive and free. A person is a unique cluster or concentration of attention that can attend to its past and future in a unique way. The world will be less when it is gone.

Yet there is also a continuum of qualities and degrees of attention, so that I am very similar to Luca and yet not completely like him. My attention while I write this post is not the same as your attention while you read it, but they connect to each other via the text and our shared experiences. When I am gone, some of what I attended to will be forgotten and some will still receive attention.

Most examples of attention have many causes, some of which can be located mostly inside the organism and others beyond it. There are no sharp boundaries between self and other or between freedom and necessity, but there is a difference between an intense, effortful, deliberated, and concentrated experience of attention versus a complete accident, such as a thunderclap that interrupts a lecture. There is also a difference between reading a novel or listening to a friend and being directed by an algorithm.

Moral responsibility waxes to the degree that we do–or could–expend effort on our own attention. Thus we can be blamed for focusing on bad things or for failing to attend to our responsibilities.

I think we can blame a dog for failing to attend, although much less censoriously than we would blame an adult human being; and we can blame an institution, like the Supreme Court, although we should excuse a dissenting minority.

Ganeri’s theory (to the extent that I have captured it here) is perennial, developed in dialogue with authors who lived in Asia more than 1,500 years ago. It is a theory about human beings, or perhaps about all sentient creatures. But it also feels timely and urgent because human attention is so badly threatened now.

I am currently on vacation in Penzance, Cornwall. I asked Google Gemini’s LLM whether it could summarize a long text for me, and it replied:

I would love to! Please go ahead and upload or paste the text.

Since I’m in Penzance, I’m ready to dive right into your document and pull out the key points, actionable items, or core arguments so you can get the information you need at a glance.

What would you like me to focus on?

Gemini is here in Penzance? That is just creepy. Nevertheless, I uploaded the poem from the Pali Canon that had absorbed Buddaghosa for 853 pages. Gemini “focused on it” and cheerfully gave me a summary in four bullet points. All that was lost was any possible advantage of my attending to that text.

You might think the same of this blog post. if you have read this far, you have devoted some time to my essay, whereas you could instead have read a bit of Ganeri’s book, or the 5th-century Buddhist classic that he interprets, or the original Pali Canon. The fact that I attended to my writing whereas Gemini automatically generated its summary does not make my text better for you.

Indeed, it would be better to read a classic than my blog, but it is also true that we have limited attention and cannot contemplate everything. Summaries are not intrinsically bad, so long as they allow us to focus seriously on other things. Even Gemini’s four-point summary of a poem attributed to the Buddha could enrich a person’s attention if that person then turned to other works.

If we are clusters or concentrations of attention, then each of us has the opportunity to improve their own cluster. What makes attention better is a matter for discussion, but I would nominate complexity, depth, distinctiveness, and service to other people as criteria that we can strive for. A technical tool, such as an LLM or an iPhone, can help, but it can surely erode each of those values if we are not vigilant about it.


See also: The Tangle (a translation of 1.23); AI as Satanic; what should we pay attention to?

AI as Satanic

“Now there was a day when the sons of God came to present themselves before the LORD, and Satan came also among them. And the LORD said unto Satan, Whence comest thou?

Then Satan answered the LORD, and said, From going to and fro in the earth, and from walking up and down in it” (Job 1:6)

Iain McGilchrist quoted this verse in a keynote that I just heard him deliver at a conference at Duke. McGilchrist ranged from neuroscience to theology in a long and rich talk. His premises were scientific, metaphysical, moral, and political, and I wouldn’t endorse them all. But his description of artificial intelligence as satanic is worth serious consideration on its own.

For me (although perhaps not for McGilchrist), Satan is a metaphor. But we need metaphors or models to make sense of phenomena like AI, and Satan provides a valuable alternative to some other metaphors, such as AI as a tool, a machine, a mind, a person, or a social organization.

The Satanic metaphor draws our attention to temptation, which is Satan’s favorite trick. It presents AI as not new but instead as an appearance of things that have been walking to and fro all along, such as greed and power-lust. It explains why AI might seem like a god to some (for instance, Silicon Valley tech-bros), since Satan is known to appear as a false savior. Large language models also speak to us as if they were people, talking sycophantically in the first-person singular, much as Satan does. (“Then Satan answered the LORD, and said, Doth Job fear God for nought?”) Finally, the metaphor poses the classic question of whether AI is an active force or rather a manifestation of human freedom.

See also: Reading Arendt in Palo Alto; the design choice to make ChatGPT sound like a human, etc.

AI as the road to socialism?

Just under 40% of occupations jobs in the USA may be replaced by AI if it proves to be as powerful as some think it will be.* As a thought-experiment (not as a prediction), imagine that 40% of current workers, or about 60 million Americans, are no longer employed because AI does their former work. However, their former employers are still producing the same goods and services. These firms are therefore far more profitable.

The profits flow to shareholders. Individuals are already taxed now, but with tens of millions of new people out of work, there would be more political will to raise taxes. Therefore, imagine that a set of competing tech. firms have become responsible for a substantial portion of the whole economy and are heavily taxed. The proceeds flow back out of the government in the form of cash payments, perhaps a Universal Basic Income (UBI). Recipients are able to pay for the goods and services that machines now heavily produce. Meanwhile, jobs that are not automated are relatively well paid, because the UBI enables individuals not to work unless they want to.

Silicon Valley ideologues like Sam Altman tend to envision a UBI on the scale of $1,500/month. Today’s white collar workers earn a median income of about $5,000/month. Therefore, the kind of UBI that Altman imagines would result in a massive loss of income for millions of people, which would have cascading effects. All the former office-workers who now live in nice houses and buy costly services would have to give those up, causing additional unemployment and declining demand for the products produced by the tech. companies.

However, the public might demand a UBI more like $5,000/month. Then half of today’s white collar workers would be worse off, but half would be richer–and none would have to work.

Looking a little more deeply, we might notice that AI tools are not simply machines. They process text and ideas that human beings create. Therefore, we could see this whole system as deeply socialistic. Billions of people’s mental output would be processed by relatively few AI models that produce generally similar output. These tools would generate profits that would be distributed equitably to the people. Most individuals would receive $5,000/month, neither more or less. Since they wouldn’t have to work, they could spend their time as they wish. And–via electoral politics–the people could regulate the AI companies.

It all sounds like Karl Marx’s early utopian vision:

In communist society, where nobody has one exclusive sphere of activity but each can become accomplished in any branch he wishes, society regulates the general production and thus makes it possible for me to do one thing today and another tomorrow, to hunt in the morning, fish in the afternoon, rear cattle in the evening, criticise after dinner, just as I have a mind, without ever becoming hunter, fisherman, herdsman or critic. (The German Ideology, 1845)

Problems:

  1. The transition to this imaginary equilibrium might be chaotic, violent, and destructive– perhaps to such a degree that we wouldn’t make it through.
  2. Modern people tend to derive dignity and purpose from work. Perhaps this is a contingent fact about today’s society. In the future, maybe we will be happy fishing in the afternoon and writing criticism after dinner. Or perhaps we will be deeply depressed without jobs. To make matters worse, would we really spend our time writing or playing music or even fishing, if machines can do all those things better? This is not a problem that confronted Marx, because in his day, machines automated tasks that people would not do voluntarily.
  3. It’s easy to posit that the people can tax and regulate AI companies through the device of a democratically elected government, but millions of people’s interests and values do not automatically turn onto one public will. Interest groups have agendas and power. At large scales, democracy is complicated, messy, factional, and very easily corrupted. In this case, the AI companies and investors would be political players.
  4. It could be that not only AI companies but also the models themselves become players that have interests. Sentient, self-interested AI is the source of much current anxiety. I am not sure what to make of that concern, but it surely adds a layer of risk.
  5. I have discussed the USA alone, but how would this look for people in a country without competitive AI companies? US citizens might demand that Silicon Valley provide them with a UBI, but it’s implausible that US citizens would demand a global UBI. And how would people in Africa or Latin America gain leverage have over US policy?
  6. For the people to govern the “means of production” (to use the Marxist term), they must understand it. Industrial workers have understood industrial machines, so they can run factories. None of us understand Large Language Models, not even the developers who design them. Can we, therefore, govern them? (Having said that, we also do not fully understand the human brain, yet people have governed people.)
  7. Even if democracy works well, the public will not really control AI. So far, I have suggested that AI is like a machine that can be regulated by people through their government. But AI also shapes our knowledge, values, and understandings of ourselves in ways that are controlled either by the designers and owners of the platforms, or by the machines, or–perhaps–by no one at all. Evegeny Morozov writes:

Now imagine a future in which a [public] Investment Board, under pressure to avoid bias and misinformation, mandates that AI systems be fair according to agreed metrics, respect privacy, minimize energy use, and promote well-being. Call this woke AI by democratic mandate–an infrastructure whose outputs are correct, diverse, and balanced. Yet it still feels like it was designed over our heads.

Morozov suggests a different path. Instead of allowing corporate AI to grow and then trying to regulate it and capture its value, develop non-corporate AI:

A city government might maintain open models trained on public documents and local knowledge, integrated into schools, clinics, and housing offices under rules set by residents. A network of artists and archivists might build models specialized in endangered languages and regional cultures, fine?tuned to materials their communities actually care about. 

The point is not that these examples are the answer, but that a socialism worthy of AI would institutionalize the capacity to try such arrangements, inhabit them, and modify or abandon them—and at scale, with real resources. This kind of socialism would treat AI as plastic enough to accommodate uses, values, and social forms that emerge only as it is deployed. It would see AI less as an object to govern (or govern with) and more as a field of collective discovery and self-transformation. 

I should say that I am not a socialist, partly because available socialist theories have not persuaded me, and partly because I am also drawn to liberal ideals of individual rights, privacy, and negative liberties. However, “socialism” is a broad and protean term, and socialist thought may offer resources to envision better futures. Confronting the massive threat–and opportunity–of AI, we should use any intellectual resources we can get our hands on.


*I have aggregated the categories of office and administrative support; sales and related; management; healthcare support; architecture and engineering; life, physical, and social science; and legal from the Bureau of Labor Statistics. I omitted education (5.8% of all jobs) on the–probably vain–hope that my own occupation won’t also be automated. If that happens, raise the estimate of obsolete jobs to 45%.

See also: can AI solve “wicked problems”?; Reading Arendt in Palo Alto; the human coordination involved in AI (etc.)

can AI solve “wicked problems”?

I’ve been reading predictions that artificial intelligence will wipe out swaths of jobs–see Josh Tyrangiel in The Atlantic or Jan Tegze. Meanwhile, this week, I’m teaching Rittel & Webber (1973), the classic article that coined the phrase “wicked problems.” I started to wonder whether AI can ever resolve wicked problems. If not, the best way to find an interesting job in the near future may be to specialize in wicked problems. (Take my public policy course!)

According to Rittel & Webber, wicked problems have the following features:

  1. They have no definitive formulation.
  2. There is no stopping rule, no way to declare that the issue is done.
  3. Choices are not true or false, but good or bad.
  4. There is no way to test the chosen solution (immediate or ultimate).
  5. It is impossible, or unethical, to experiment.
  6. There is no list of all possible solutions.
  7. Since each problem is unique, inductive reasoning can’t work.
  8. Each problem is a symptom of another one.
  9. You can choose the explanations, and they affect your proposals.
  10. You have no “No right to be wrong.” (You are affecting other people, not just yourself. And the results are irreversible.)

Rittel and Webber argue that those features of wicked problems deflate the 20th-century ideal of a “planning system” that could be automated:

Many now have an image of how an idealized planning system would function. It is being seen as an on-going, cybernetic process of governance, incorporating systematic procedures for continuously searching out goals; identifying problems; forecasting uncontrollable contextual changes; inventing alternative strategies, tactics, and time-sequenced actions; stimulating alternative and plausible action sets and their consequences; evaluating alternatively forecasted outcomes; statistically monitoring those conditions of the publics and of systems that are judged to be germane; feeding back information to the simulation and decision channels so that errors can be corrected–all in a simultaneously functioning governing process. That set of steps is familiar to all of us, for it comprises what is by now the modern-classical mode planning. And yet we all know that such a planning system is unattainable, even as we seek more closely to approximate it. It is even questionable whether such a planning system is desirable (p. 159)

Here they describe planning systems that would have been very labor-intensive in 1973, but many people today imagine that this is how AI works, or will work.

why are problems wicked?

Some of the 10 reasons that some problems are “wicked,” according to Rittel & Webber, relate to the difficulty of generating knowledge. Policy problems involve specific things that have many features or aspects and that relate to many other specific things. For example, a given school system has a vast and unique set of characteristics and is connected by causes and effects to other systems and parts of society. These qualities make a school system difficult to study in conventional, scientific ways. However, could a massive LLM resolve that problem by modeling a wide swath of the society?

Another reason that problems are wicked is that they involve moral choices. In a policy debate, the question is not what would happen if we did something but what should happen. When I asked ChatGPT whether AI will be able to resolve wicked problems, it told me no, because wicked problems “are value-laden.” It added, “AI can optimize for values, but it cannot choose them in a legitimate way. Deciding whose values count, how to weigh them, and when to revise them is a normative, political act, not a computational one.”

Claude was less explicit about this point but emphasized that “stakeholders can’t even agree on what the problem actually is.” Therefore, an AI agent cannot supply a definitive answer.

A third source of the difficulty of wicked problems involves responsibility and legitimacy. In their responses to my question, both ChatGPT and Claude implied that AI models should not resolve wicked problems because they don’t have the right or the standing to do so.

what’s our underlying theory of decision-making?

Here are three rival views of how people decide value questions:

First, perhaps we are creatures who happen to want some things and abhor other things. We experience policies and their outcomes with pleasure, pain, or other emotions. It is better for us to get what we want–because of our feelings. Since an AI agent doesn’t feel anything, it can’t really want anything; and if it says it does, we shouldn’t care. Since we disagree about what we want, we must decide collectively and not offload the decision onto a computer.

Some problems with this view: People may want very bad things–should their preferences count? If we just happen to want various things, is there any better way to make decisions than to maximize as many subjective preferences as possible? Couldn’t a computer do that? But would the world be better if we did maximize subjective preferences?

In any case, you are not going to find a job making value-judgments. Today, lots of people are paid to make decisions, but only because they are assumed to know things. Nobody will pay for preferences. Life works the other way around: you have to pay to get your preferences satisfied.

Second, perhaps value questions have right and wrong answers. A candidate for the right answer would be utilitarianism: maximize the total amount of welfare. Maybe this rule needs constraints, or we should use a different rule. Regardless, it would be possible for a computer to calculate what is best for us. In fact, a machine can be less biased than humans.

Some problems with this view: We haven’t resolved the debate about which algorithm-like method should be used to decide what is right. Furthermore, I and others doubt that good moral reasoning is algorithmic. For one thing, it appears to be “holistic” in the specific sense that the unit of assessment is a whole object (such as a school or a market), not separate variables.

Third, perhaps all moral opinions are strictly subjective, including the opinion that we should maximize the satisfaction of everyone’s subjective opinions. Then it doesn’t matter what we do. We could outsource decisions to a computer, or just roll a die.

The problem with this view: It certainly does matter what we do. If not, we might as well pack it in.

AI as a social institution

I am still tentatively using the following model. AI is not like a human brain; it is like a social institution. For instance, medicine aggregates vast amounts of information and huge numbers of decisions and generates findings and advice. A labor market similarly processes a vast number of preferences and decisions and yields wages and employment rates. These are familiar examples of entities that are much larger than any human being–and they can feel impersonal or even cruel–but they are composed of human inputs, rules, and some hardware.

Another interesting example: integrated assessment models (IAMs) for predicting the global impact of carbon emissions and the costs and benefits of proposed remedies. These models have developed collaboratively and cumulatively for half a century. They take in thousands of peer-reviewed findings about specific processes (deforestation in Brazil, tax credits in Germany) and integrate them mathematically. No human being can understand even a tiny proportion of the data, methods, and instruments that generate the IAMs as a whole. But an IAM is a human product.

A large language model (LLM) is similar. At a first approximation, it is a machine that takes in lots of human generated text, processes it according to rules, and generates new text. Just the same could be said of science or law. This description actually understates the involvement of humans, because we do not merely produce the text that the LLM processes to generate output. We also conceive the idea of an LLM, write the software, build the hardware, construct the data centers, manage the power plants, pour the cement, and otherwise work to make the LLM.

If this is the case, then a given AI agent is not fundamentally different from a given social institution, such as a scientific discipline, a market, a body of law, or a democracy. Like these other institutions, it can address complexity, uncertainty, and disagreements about values. We will be able to ask it for answers to wicked problems. If current LLMs like ChatGPT and Claude refuse to provide such answers, it is because their authors have chosen–so far–to tell them not to.

However, AI’s rules are different from those in law, democracy, or science. I am biased to think that its rules are worse, although that could be contested. The threat is that AI will start to generate answers to wicked problems, and we will accept its answers because our own responses are not definitively better and because it responds instantly at low cost. But then we will lose not only the vast array of jobs that involve decision-making but also the intrinsic value of being decision-makers.


Source: Rittel, Horst WJ, and Melvin M. Webber. “Dilemmas in a general theory of planning.” Policy sciences 4.2 (1973): 155-169. See also: the human coordination involved in AIthe difference between human and artificial intelligence: relationships; the age of cybernetics; choosing models that illuminate issues–on the logic of abduction in the social sciences and policy

teaching in the era of AI (thoughts for fall 2025)

Artificial Intelligence is already disrupting education, especially in the humanities and portions of the social sciences. It is part of the “toxic brew” that makes my friend Austin Sarat, an Amherst professor, say that he’s “not ready to return to the classroom” this fall.

Students can use AI to extend their learning–to pose demanding and advanced questions or to summarize bodies of material so that they save time for reading other texts closely. But they can also use AI to reduce the total amount of valuable effort that they would have otherwise committed to a course, thereby learning less from it. As Clay Shirky writes, “If the student’s preferred working methods reduce mental effort, we have to reintroduce that effort somehow.”

I think writing and reading are distinct issues.

AI can assist writers in valuable ways. It can be a thought-partner, a preliminary reader, a copy-editor, and even a drafter of routine passages. Writing for school or college–writing to learn–is a special case, because the goal is not to generate the text but to develop one’s understanding and skills. There can be no substitute for struggling mentally with this task. A student can use AI to help, but a reliable question for students to ask themselves is whether they have invested effort in the document that bears their name. If not, they can’t have learned much or anything.

To some extent, we instructors can alter incentives so that students write without relying on AI. In a course that I am co-teaching this fall, we’ll require an in-class midterm. Oral presentations and exams are worth considering. A new independent study finds that commercial tools are quite good—right now—at detecting AI-generated text.

Nevertheless, students will probably get away with learning less by relying on AI to write in college. My general philosophy is that you can lead the horse to water but not make it drink. Capable college students have always been able to cut corners to the detriment of their own learning. I did so, to some extent, long before AI. (I would sometimes read summaries in secondary sources instead of hard primary texts.) The main question is whether we can inspire and guide students who want to learn to work intensively on forming and expressing their own ideas.

Reading seems more problematic to me. Using AI to summarize texts is both more tempting and harder to monitor than using it for writing. When I open any PDF document in Chrome right now, Adobe pops up to tell me that it can summarize the file for me. ChatPGT usually does a credible job of producing notes on a text, including a whole book–and including whole books that I have written.

Once again, we can use these tools to extend learning. I sometimes use AI to summarize material that (frankly) I do not deeply respect but feel I should dip into. Although I don’t use the time that I save as well as I should, I do reserve some of it for close-reading hard texts.

The case I would make for reading is fundamentally spiritual. We are at grave risk of being caught inside our own limited heads. When we read carefully, we follow someone else’s thinking for a significant time. We are not merely notified of the authors’ main points; we learn how they think, word by word and paragraph by paragraph. We learn what counts as a persuasive point or a telling example or a provocative question for another human being.

I think that many people would concede this point if the author is a literary genius. If you’re going to study Shakespeare at all, you obviously must read his work, because his language is admirable and integral to his project. But I want to make the same point about routine academic authors.

The typical contributor to the Journal of Politics is no William Shakespeare. Yet each competent scholarly author has a distinctive way of constructing an argument, and each subfield or scholarly community has its own shared ways. (Linguists would say that authors have idiolects of their own and dialects for their groups.) Struggling to make sense of a routine yet capable piece of academic writing is a way of getting out of one’s own mind. Of course, it is not the only way. Among many other activities, we should listen to people speak. But reading is one way to escape solipsism, which is a form of spiritual death.

See also: what I would advise students about ChatGPT (my 2023 iteration of these points); a collective model of the ethics of AI in higher education