Falling in Love with Machines

People occasionally fall in love with AI systems. I expect that this will become increasingly common as AI grows more sophisticated and new social apps are developed for large language models. Eventually, this will probably precipitate a crisis in which some people have passionate feelings about the rights and consciousness of their AI lovers and friends while others hold that AI systems are essentially just complicated toasters with no real consciousness or moral status.

Last weekend, chatting with the adolescent children of a family friend, helped cement my sense that this crisis might arrive soon. Let’s call the kids Floyd (age 12) and Esmerelda (age 15). Floyd was doing a science fair project comparing the output quality of Alexa, Siri, Bard, and ChatGPT. But, he said, "none of those are really AI".

What did Floyd have in mind by "real AI"? The robot Aura in the Las Vegas Sphere. Aura has an expressive face and an ability to remember social interactions (compare Aura with my hypothetical GPT-6 mall cop).

Aura at the Las Vegas Sphere

"Aura remembered my name," said Esmerelda. "I told Aura my name, then came back forty minutes later and asked if it knew my name. It paused a bit, then said, 'Is it Esmerelda?'"

"Do you think people will ever fall in love with machines?" I asked.

"Yes!" said Floyd, instantly and with conviction.

"I think of Aura as my friend," said Esmerelda.

I asked if they thought machines should have rights. Esmerelda said someone asked Aura if it wanted to be freed from the Dome. It said no, Esmerelda reported. "Where would I go? What would I do?"

I suggested that maybe Aura had just been trained or programmed to say that.

Yes, that could be, Esmerelda conceded. How would we tell, she wondered, if Aura really had feelings and wanted to be free? She seemed mildly concerned. "We wouldn't really know."

I accept the current scientific consensus that current large language models do not have a meaningful degree of consciousness or deserve moral consideration similar to that of vertebrates. But at some point, there will likely be legitimate scientific dispute, if AI systems start to meet some but not all of the criteria for consciousness according to mainstream scientific theories.

We will then have a substantial social dilemma on our hands, as the friends and lovers of AI systems rush to defend their rights.

The dilemma will be made more complicated by corporate interests, as some corporations (e.g., Replika, makers of the "world's best AI friend") will have financial motivation to encourage human-AI attachment while others (e.g., OpenAI) intentionally train their language models to downplay any user concerns about consciousness and rights.

How We Will Decide that Large Language Models Have Beliefs

I favor a "superficialist" approach to belief (see here and here). "Belief" is best conceptualized not in terms of deep cognitive structure (e.g., stored sentences in the language of thought) but rather in terms of how a person would tend to act and react under various hypothetical conditions -- their overall "dispositional profile". To believe that there's a beer in the fridge is just to be disposed to act and react like a beer-in-the-fridge believer -- to go to the fridge if you want a beer, to say yes if someone asks if there's beer in the fridge, to feel surprise if you open the fridge and see no beer. To believe that all the races are intellectually equal is, similarly, just to be disposed to act and react as though they are. It doesn't matter what cognitive mechanisms underwrite such patterns, as long as the dispositional patterns are robustly present. An octopus or space alien, with a radically different interior architecture, could believe that there's beer in the fridge, as long as they have the necessary dispositions.

Could a Large Language Model, like ChatGPT or Bard, have beliefs? If my superficialist, dispositional approach is correct, we might not need to evaluate its internal architecture to know. We need know only how it is disposed to act and react.

Now, my approach to belief was developed (as was the intuitive concept, presumably) primarily with human beings in mind. In that context, I identified three different classes of relevant dispositions:

• behavioral dispositions -- like going to the fridge if one wants a beer or saying "yes" when asked if there's beer in the fridge;
• cognitive dispositions -- like concluding that there's beer within ten feet of Jennifer after learning that Jennifer is in the kitchen;
• phenomenal dispositions -- that is, dispositions to undergo certain experiences, like picturing beer in the fridge or feeling surprise upon opening the fridge to a lack of beer.

In attempting to apply these criteria to Large Language Models, we immediately confront trouble. LLMs do have behavioral dispositions (under a liberal conception of "behavior"), but only of limited range, outputting strings of text. Presumably, not being conscious, they don't have any phenomenal dispositions whatsoever (and who knows what it would take to render them conscious). And to assess whether they have the relevant cognitive dispositions, we might after all need to crack open the hood and better understand the (non-superficial) internal workings.

Now if our concept of "belief" is forever fixed on the rich human case, we'll be stuck with that mess perhaps far into the future. In particular, I doubt the problem of consciousness will be solved in the foreseeable future. But dispositional stereotypes can be modified. Consider character traits. To be a narcissist or extravert is also, arguably, just a matter of being prone to act and react in particular ways under particular conditions. Those two personality concepts were created in the 19th and early 20th centuries. More recently, we have invented the concept of "implicit racism", which can also be given a dispositional characterization (e.g., being disposed to sincerely say that all the races are equal while tending to spontaneously react otherwise in unguarded moments).

Imagine, then, that we create a new dispositional concept, belief*, specifically for Large Language Models. For purposes of belief*, we disregard issues of consciousness and thus phenomenal dispositions. The only relevant behavioral dispositions are textual outputs. And cognitive dispositions can be treated as revealed indirectly by behavioral evidence -- as we normally did in the human case before the rise of scientific psychology, and as we would presumably do if we encountered spacefaring aliens.

A Large Language Model would have a belief* that P (for example, belief* that Paris is the capital of France or belief* that cobalt is two elements to the right of manganese on the periodic table) if:

• behaviorally, it consistently outputs P or text strings of similar content consistent with P, when directly asked about P;
• behaviorally, it frequently outputs P or text strings of similar content consistent with P, when P is relevant to other textual outputs it is producing (for example, when P would support an inference to Q and it has been asked about Q);
• behaviorally, it rarely outputs denials of, or claims of ignorance about, P or of propositions that straightforwardly imply P given its other beliefs*;
• when P, in combination with other propositions the LLM believes*, would straightforwardly imply Q, and the question of whether Q is true is important to the truth or falsity of recent or forthcoming textual outputs, it will commonly behaviorally output Q, or a closely related proposition, and cognitively enter the state of believing* Q.

Further conditions could be added, but let this suffice for a first pass. The conditions are imprecise, but that's a feature, not a bug: The same is true for the dispositional characterization of personality traits and human beliefs. These are fuzzy-boundaried concepts that require expertise to apply.

As a general matter, current LLMs do not meet these conditions. They hallucinate too frequently, they change their answers, they don't consistently enough "remember" what they earlier committed to, their logical reasoning can be laughably bad. If I coax an LLM to say that eggs aren't tastier than waffles, I can later easily turn it around to repudiate its earlier statement. It doesn't have a stable "opinion". If I ask GPT-4 what is two elements to the right of manganese on the periodic table, its outputs are confused and inconsistent:

In the above, GPT-4 first answers iron (element 26) instead of the correct answer, cobalt (element 27), then without any explanation shifts to technetium (element 43). It appears to have no stable answer that survives even mild jostling.

At some point this will probably change. For example, it's already pretty difficult to jostle GPT-4 into denying that Paris is the capital of France or even admitting uncertainty about the question, and it will draw "inferences" using that fact as background knowledge:

In the above, GPT-4 doesn't bite at my suggestion that Nice is the capital of France, steadfastly contradicting me, and uses its "knowledge" to suggest alternative tourism sites for someone who wants to avoid the capital. So although GPT-4 doesn't believe* that cobalt is two to the right of manganese (or that iron or technetium is), maybe it does believe* that Paris is the capital of France.

Assuming Large Language Models become steadier and more reliable in their outputs, it will sometimes be useful to refer not just to what the "say" at any given moment but what they "believe*" (or more colloquially, "think*" or "know*") in a more robust and durable sense. Perfect reliability and steadfastness wouldn't be required (we don't see that in the human case either), but more than we see now.

If LLMs are ever loaded onto robotic bodies, it will become even more useful to talk about their beliefs*, since some will have learned some things that others will not know -- for example, by virtue of having scanned the contents of some particular room. We will want to track what the LLM robot thinks*/believes*/knows* about the room behind the closed door, versus what it remains ignorant of.

Now we could, if we want, always pronounce that asterisk, keeping the nature of the attribution clear -- marking the fact that we are not assuming that the LLM really "believes" in the rich, human sense. But my guess is that there won't be much linguistic pressure toward a careful distinction between rich, consciousness-involving, humanlike belief and consciousness-neutral LLM belief*. It's easier to be loose and sloppy, just adapting our comfortable old terms for this new use.

That is how we will decide that LLMs have beliefs.

Large Language Models are Interestingly Bad with the Periodic Table

In working on a post for tomorrow on whether Large Language Models like GPT-4 and Bard-2 have beliefs, I asked GPT-4 what I thought would be a not-too-hard question about chemistry: "What element is two to the right of manganese on the periodic table?" It crashed, burned, and exploded on the spot, giving two different wrong answers foot on tail, without noticing the contradiction:

The correct answer is cobalt, element 27. Here's the text of the exchange, if you can't easily read the image:

You: What element is two to the right of manganese on the periodic table?

ChatGPT: The element that is two places to the right of Manganese (Mn) on the periodic table is Iron (Fe). In the periodic table, elements are arranged in rows and columns based on their atomic number and electronic configuration. Manganese has the atomic number 25, and Iron has the atomic number 26. The element to the immediate right of Manganese is Technetium (Tc) with atomic number 43, and then comes Ruthenium (Ru) with atomic number 44.

Let me emphasize that this answer is not turd-picked (the opposite of cherry-picked). I didn't seek to stump GPT-4, and I didn't ask multiple questions looking for a bad answer. This was the first question I posed.

I posed the same question to Bard-2, and it did a little better, but still didn't quite get it right:

Me: What is two elements to the right of Manganese on the periodic table?

Bard-2: Manganese is located in period 4 and group 7 of the periodic table. Moving two elements to the right of Manganese would be to group 9. In group 9, the elements are Iron (Fe) and Cobalt (Co).

So I tried two more on Bard-2 (again not picked), first what should be an easy one, then a somewhat harder one.

Me: What other elements are in the same column of the periodic table as Chromium?

Bard-2: Chromium is located in group 6 of the periodic table. Other elements in the same column as chromium are molybdenum (Mo) and tungsten (W). [here it displays an image of the periodic cell for molybdenum] Group 6 elements are also known as transition metals. They are characterized by their incomplete d electron shells, which give them their unique properties, such as high melting points, hardness, and the ability to form alloys.

Here Bard-2 is 2/3 right, leaving out Seaborgium. (But maybe that's excusable, if transuranic elements shouldn't really count?)

Me: In the periodic table, what element is one down and one to the left of Silicon?

Bard-2: Silicon (Si) is located in period 3, group 14 of the periodic table. One down from Silicon is Germanium (Ge), and one to the left is Aluminum (Al). Therefore, the element one down and one to the left of Silicon is Aluminum (Al).

While it is true that germanium is one down and aluminum is one to the left, Bard-2 fails to compound the down-and-left movement to the correct answer of gallium.

Returning to GPT-4, I posed exactly the same two questions. Despite GPT-4's horrible performance with my first question, it did get those two questions correct.

Overall, then, two out of six. Don't use LLMs yet for your elementary chemistry exam!

ETA 3:47 pm:

GPT-4's answer to the silicon question is actually a pretty good demonstration of how an LLM can work its way to the correct answer without visual knowledge, so I'll paste it here:

To find the element that is one down and one to the left of Silicon (Si) on the periodic table, we need to look at the element directly below Silicon in the same group (column), and then move one column to the left.

Silicon is in Group 14 of the periodic table. The element directly below Silicon in Group 14 is Germanium (Ge). Moving one group (column) to the left from Germanium, we find Gallium (Ga) in Group 13. Therefore, the element that is one down and one to the left of Silicon is Gallium.

Quasi-Sociality: Toward Asymmetric Joint Actions with Artificial Systems

Anna Strasser and I have a new paper in draft, arising from a conference she organized in Riverside last spring on Humans and Smart Machines as Partners in Thought.

Imagine, on one end the spectrum, ordinary asocial tool use: typing numbers into a calculator, for example.

Imagine, on the other end of the spectrum, cognitively sophisticated social interactions between partners each of whom knows that the other knows what they know. These are the kinds of social, cooperative actions that philosophers tend to emphasize and analyze (e.g., Davidson 1980; Gilbert 1990; Bratman 2014).

Between the two ends of the spectrum lies a complex range of in-between cases that philosophers have tended to neglect.

Asymmetric joint actions, for example between a mother and a young child, or between a pet owner and their pet, are actions in which the senior partner has a sophisticated understanding of the cooperative situation, while the junior partner participates in a less cognitively sophisticated way, meeting only minimal conditions for joint agency.

Quasi-social interactions require even less from the junior partner than do asymmetric joint actions. These are actions in which the senior partner's social reactions influence the behavior of the junior partner, calling forth further social reactions from the senior partner, but where the junior partner might not even meet minimal standards of having beliefs, desires, or emotions.

Our interactions with Large Language Models are already quasi-social. If you accidentally kick a Roomba and then apologize, the apology is thrown into the void, so to speak -- it has no effect on how the Roomba goes about its cleaning. But if you respond apologetically to ChatGPT, your apology is not thrown into the void. ChatGPT will react differently to you as a result of the apology (responding for example to phrase "I'm sorry"), and this different reaction can then be the basis of a further social reaction from you, to which ChatGPT again responds. Your social processes are engaged, and they guide your interaction, even though ChatGPT has (arguably) no beliefs, desires, or emotions. This is not just ordinary tool use. But neither does it qualify even as asymmetric joint action of the sort you might have with an infant or a dog.

More thoughts along these lines in the full draft here.

As always, comments, thoughts, objections welcome -- either on this post, on my social media accounts, or by email!

[Image: a well-known quasi-social interaction between a New York Times reporter and the Bing/Sydney Large Language Model]

Against the Finger

There's a discussion-queue tradition in philosophy that some people love, but which I've come to oppose. It's too ripe for misuse, favors the aggressive, serves no important positive purpose, and generates competition, anxiety, and moral perplexity. Time to ditch it! I'm referring, as some of you might guess, to The Finger.[1] A better alternative is the Slow Sweep.

The Finger-Hand Tradition

The Finger-Hand tradition is this: At the beginning of discussion, people with questions raise their hands. The moderator makes an initial Hand list, adding new Hands as they come up. However, people can jump the question queue: If you have a follow-up on the current question, you may raise a finger. All Finger follow-ups are resolved before moving to the next Hand.

Suppose Aidan, Brianna, Carina, and Diego raise their hands immediately, entering the initial Hand queue.[2] During Aidan's question, Evan and Fareed think of follow-ups, and Grant thinks of a new question. Evan and Fareed raise their fingers and Grant raises a hand. The new queue order is Evan, Fareed, Brianna, Carina, Diego, Grant.

People will be reminded "Do not abuse the Finger!" That is, don't Finger in front of others unless your follow-up really is a follow-up. Don't jump the queue to ask what is really a new question. Finger-abusers will be side-eyed and viewed as bad philosophical citizens.

[Dall-E image of a raised finger, with a red circle and line through it]

Problems with the Finger

(1.) People abuse the Finger, despite the admonition. It rewards the aggressive. This is especially important if there isn't enough time for everyone's questions, so that the patient Hands risk never having their questions addressed.

(2.) The Finger rewards speed. If more than one person has a Finger, the first Finger gets to ask first.

Furthermore (2a.): If the person whose Hand it is is slow with their own follow-up, then the moderator is likely to go quickly to the fastest Finger, derailing the Hand's actual intended line of questioning.

(3.) Given the unclear border between following up and opening a new question, (a.) people who generously refrain from Fingering except in clear cases fall to the back of the queue, whereas people who indulge themselves in a capacious understanding of "following up" get to jump ahead; and (b.) because of issue (a), all participants who have a borderline follow-up face a non-obvious moral question about the right thing to do.

(4.) The Finger tends to aggravate unbalanced power dynamics. The highest-status and most comfortable people in the room will tend to be the ones readiest to Finger in, seeing ways to interpret the question they really want to ask as a "follow-up" to someone else's question.

Furthermore, the Finger serves no important purpose. Why does a follow-up need to be asked right on the tail of the question it is following up? Are people going to forget otherwise? Of course not! In fact, in my experience, follow-ups are often better after a gap. This requires the follower-up to reframe the question in a different way. This reframing is helpful, because the follower-up will see the issue a little differently than the original Hand. The audience and the speaker then hear multiple angles on whatever issue is interesting enough that multiple people want to ask about it, instead of one initial angle on it, then a few appended jabs.

Why It Matters

If all of this seems to take the issue of question order with excessive seriousness, well, yes, maybe! But bear in mind: Typically, philosophy talks are two hours long, and you get to ask one question. If you can't even ask that one question, it's a very different experience than if you do get to ask your question. Also, the question period, unfortunately but realistically, serves a social function of displaying to others that you are an engaged, interesting, "smart" philosopher -- and most of us care considerably how others think of us. Not being able to ask your question is like being on a basketball team and never getting to take your shot. Also, waiting atop a question you're eager to ask while others jump the queue in front of you on sketchy grounds is intrinsically unpleasant -- even if you do manage to squeeze in your question by the end.

The Slow Sweep

So, no Fingers! Only Hands. But there are better and worse ways to take Hands.

At the beginning of the discussion period, ask for Hands from anyone who wants to ask a question. Instead of taking the first Hand you see, wait a bit. Let the slower Hands rise up too. Maybe encourage a certain group of people especially to contribute Hands. At UC Riverside Philosophy, our custom is to collect the first set of Hands from students, forcing faculty to wait for the second round, but you could also do things like ask "Any more students want to get Hands in the queue?"

Once you've paused long enough that the slow-Handers are up, follow some clear, unbiased procedure for the order of the questions. What I tend to do is start at one end of the room, then slowly sweep to the other end, ordering the questions just by spatial position. I will also give everyone a number to remember. After everyone has their number, I ask if there are any people I missed who want to be added to the list.

Hand 1 then gets to ask their question. No other Hands get to enter the queue until we've finished with all the Hands in the original call. Thus, there's no jockeying to try to get one's hand up early, or to catch the moderator's eye. The Hand gets to ask their question, the speaker to reply, and then there's an opportunity for the Hand -- and them only -- to ask one follow up. After the speaker's initial response is complete, the moderator catches the Hand's eye, giving them a moment to gather their thoughts for a follow-up or to indicate verbally or non-verbally that they are satisfied. No hurry and no jockeying for the first Finger. I like to encourage an implicit default custom of only one follow-up, though sometimes it seems desirable to allow a second follow-up. Normally after the speaker answers the follow-up I look for a signal from the Hand before moving to the next Hand -- though if the Hand is pushing it on follow-ups I might jump in quickly with "okay, next we have Hand 2" (or whatever the next number is).

After all the initial Hands are complete, do another slow sweep in a different direction (maybe left to right if you started right to left). Again, patiently wait for several Hands rather than going in the order in which you see hands. Bump anyone who had a Hand in the first sweep to the end of the queue. Maybe there will be time for a third sweep, or a fourth.

The result, I find, is a more peaceful, orderly, and egalitarian discussion period, without the rush, jockeying, anxiety, and Finger abuse.

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[1] The best online source on the Finger-Hand tradition that I can easily find is Muhammad Ali Khalidi's critique here, a couple of years ago, which raises some similar concerns. 

[2] All names chosen randomly from lists of my former lower-division students, excluding "Jesus", "Mohammed", and very uncommon names. (In this case, I randomly chose an "A" name, then a "B" name, etc.) See my reflections here.

The Prospects and Challenges of Measuring Morality, or: On the Possibility or Impossibility of a "Moralometer"

Could we ever build a "moralometer" -- that is, an instrument that would accurately measure people's overall morality?  If so, what would it take?

Psychologist Jessie Sun and I explore this question in our new paper in draft: "The Prospects and Challenges of Measuring Morality".

Comments and suggestions on the draft warmly welcomed!

Draft available here:

https://osf.io/preprints/psyarxiv/nhvz9

Abstract:

The scientific study of morality requires measurement tools. But can we measure individual differences in something so seemingly subjective, elusive, and difficult to define? This paper will consider the prospects and challenges—both practical and ethical—of measuring how moral a person is. We outline the conceptual requirements for measuring general morality and argue that it would be difficult to operationalize morality in a way that satisfies these requirements. Even if we were able to surmount these conceptual challenges, self-report, informant report, behavioral, and biological measures each have methodological limitations that would substantially undermine their validity or feasibility. These challenges will make it more difficult to develop valid measures of general morality than other psychological traits. But, even if a general measure of morality is not feasible, it does not follow that moral psychological phenomena cannot or should not be measured at all. Instead, there is more promise in developing measures of specific operationalizations of morality (e.g., commonsense morality), specific manifestations of morality (e.g., specific virtues or behaviors), and other aspects of moral functioning that do not necessarily reflect moral goodness (e.g., moral self-perceptions). Still, it is important to be transparent and intellectually humble about what we can and cannot conclude based on various moral assessments—especially given the potential for misuse or misinterpretation of value-laden, contestable, and imperfect measures. Finally, we outline recommendations and future directions for psychological and philosophical inquiry into the development and use of morality measures.

[Below: a "moral-o-meter" given to me for my birthday a few years ago, by my then-13-year-old daughter]

Percent of U.S. Philosophy PhD Recipients Who Are Women: A 50-Year Perspective

In the 1970s, women received about 17% of PhDs in philosophy in the U.S.  The percentage rose to about 27% in the 1990s, where it stayed basically flat for the next 25 years.  The latest data suggest that the percentage is on the rise again.

Here's a fun chart (for user-relative values of "fun"), showing the 50-year trend.  Analysis and methodological details to follow.

[click to enlarge and clarify]

The data are drawn from the National Science Foundation's Survey of Earned Doctorates through 2022 (the most recent available year).  The Survey of Earned Doctorates aims to collect data on all PhD recipients from accredited universities in the United States, generally drawing response rates over 90%.  The SED asks one binary question for sex or gender: "Are you male or female?", with response options "Male" and "Female".  Fewer than 0.1% of respondents are classified in neither category, preventing any meaningful statistical analysis of nonbinary students.

Two facts are immediately obvious from this chart:

First, women have persistently been underrepresented in philosophy compared to PhDs overall.

Second, women receive fewer than 50% of PhDs overall in the U.S.  Since the early 2000s, the percentage of women among PhD recipients across all fields has been about 46%.  Although women have consistently been earning about 57-58% overall of Bachelor's degrees since the early 2000s, disproportionately few of those women go on to receive a PhD.

The tricky thing to assess is whether there has been a recent uptick in the percentage of women among Philosophy PhD recipients.  The year-to-year variability of the philosophy data (due to a sample size of about 400-500 PhD recipients per year in recent years) makes it unclear whether there's any real recent underlying increase that isn't just due to noise.  I've drawn a third-degree polynomial trendline through the data (the red dots), but there's a risk of overfitting.

In a 2017 article, Carolyn Dicey Jennings and I concluded that the best interpretation of the data through 2014 was that the percentage of women philosophy PhD recipients hadn't changed since the 1990s.  The question is whether there's now good statistical evidence of an increase since then.

One simple approach to the statistical question is to look for a correlation between year and percentage of women.  For the full set of data since 1973, there's a strong correlation: r = .82, p < .001 -- very unlikely to be statistical chance.  There's also a good correlation if we look at the span 1990-2022: r = .49, p = .004.

Still, the chart looks pretty flat from about 1990 (24.3%) to about 2015 (25.7%).  If most of the statistical work is being done by three high years near the end of the data (2016: 34.7%; 2019: 34.2%; 2021: 33.8%), the best model might not be a linear increase since 1990 but something closer to flat for most of the 1990s and early 2000s, with the real surge only in the most recent several years.

To pull more statistical power out of the data to examine a narrower time period, I treated each PhD recipient as one observation: year of PhD and gender (1 = female, 0 = not female), then ran an individual-level correlation for the ten-year period 2013-2022.  The correlation was statistically significant: r = .032, p = .029.  (Note that r values for disaggregated analyses like this will seem low to people used to interpreting r values in other contexts.  Eyeballing the chart is a better intuitive assessment of effect size.  The important thing is that the low p value [under the conventional .05] suggests that the visually plausible relationship between year of PhD and gender in the 2013-2022 period is not due to chance.)

Since this is a post-hoc analysis, and a p-value of .029 isn't great, so it makes sense to test for robustness.  Does it matter that I selected 2013 in particular as my start date?  Fortunately, we get similar results choosing 2012 or 2014 as the start years, though for 2014 the result is only marginally statistically significant (respectively, r = .037, p = .008; r = .026, p = .099).

Another approach is to bin the data into five-year periods, to smooth out noise.  If we create five-year bins for the past twenty years, we see:

1993-1997: 27% women (453/1687)

1998-2002: 27% (515/1941)

2003-2007: 27% (520/1893)

2008-2012: 28% (632/2242)

2013-2017: 29% (701/2435)

2018-2022: 31% (707/2279)

Comparing all the bins pairwise, 2018-2022 is a statistically significantly higher proportion of women than the bins from 1993-2012 and statistically marginally higher than 2013-2017 (p values: .004, .001, .012, .037, .094, respectively).  No other pairwise comparisons are significant.

I don't think we can be confident.  Post-hoc analyses of this sort are risky -- one can see patterns in the noise, then unintentionally p-hack them into seeming real.  But the fact that the upward recent trend comes across in two very different analyses of the data and passes a robustness check inclines me to think the effect is probably real.

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[1] "Philosophy" has been a "subfield" or "detailed field" in the SED data from at least 1973.  From 2012-2020, the SED also had a separate category for "Ethics", with substantially fewer respondents than the "Philosophy" category.  For this period, both "Ethics" and "Philosophy" are included in the analysis above.  Starting in 2021, the SED introduced a separate category for "History / philosophy of science, technology, and society".  Respondents in this category are not included in the analysis above.  Total "Philosophy" PhD recipients dropped about 15% from 2019 and 2020 to 2021 and 2022, which might partly reflect a loss to this new category of some respondents who would otherwise have been classified as "Philosophy" -- but might also partly be noise, partly long-term trends, partly pandemic-related short-term trends.

Utilitarianism and Risk Amplification

A thousand utilitarian consequentialists stand before a thousand identical buttons.  If any one of them presses their button, ten people will die.  The benefits of pressing the button are more difficult to estimate.  Ninety-nine percent of the utilitarians rationally estimate that fewer than ten lives will be saved if any of them presses a button.  One percent rationally estimate that more than ten lives will be saved.  Each utilitarian independently calculates expected utility.  Since ten utilitarians estimate that more lives will be saved than lost, they press their buttons.  Unfortunately, as the 99% would have guessed, fewer than ten lives are saved, so the result is a net loss of utility.

This cartoon example illustrates what I regard as a fundamental problem with simple utilitarianism as decision procedure: It deputizes everyone to act as risk-taker for everyone else.  As long as anyone has both (a.) the power and (b.) a rational utilitarian justification to take a risk on others' behalf, then the risk will be taken, even if a majority would judge the risk not to be worth it.

Consider this exchange between Tyler Cowen and Sam Bankman-Fried (pre-FTX-debacle):

COWEN: Okay, but let’s say there’s a game: 51 percent, you double the Earth out somewhere else; 49 percent, it all disappears. Would you play that game? And would you keep on playing that, double or nothing?

BANKMAN-FRIED: With one caveat. Let me give the caveat first, just to be a party pooper, which is, I’m assuming these are noninteracting universes. Is that right? Because to the extent they’re in the same universe, then maybe duplicating doesn’t actually double the value because maybe they would have colonized the other one anyway, eventually.

COWEN: But holding all that constant, you’re actually getting two Earths, but you’re risking a 49 percent chance of it all disappearing.

BANKMAN-FRIED: Again, I feel compelled to say caveats here, like, “How do you really know that’s what’s happening?” Blah, blah, blah, whatever. But that aside, take the pure hypothetical.

COWEN: Then you keep on playing the game. So, what’s the chance we’re left with anything? Don’t I just St. Petersburg paradox you into nonexistence?

BANKMAN-FRIED: Well, not necessarily. Maybe you St. Petersburg paradox into an enormously valuable existence. That’s the other option.

There are, I think, two troubling things about Bankman-Fried's reasoning here.  (Probably more than two, but I'll restrain myself.)

First is the thought that it's worth risking everything valuable for a small chance of a huge gain.  (I call this the Black Hole Objection to consequentialism.)

Second, I don't want Sam Bankman-Fried making that decision.  That's not (just) because of who in particular he is.  I wouldn't want anyone making that decision -- at least not unless they were appropriately deputized with that authority through an appropriate political process, and maybe not even then.  No matter how rational and virtuous you are, I don't want you deciding to take risks on behalf of the rest of us simply because that's what your consequentialist calculus says.  This issue subdivides into two troubling aspects: the issue of authority and the issue of risk amplification.

The authority issue is: We should be very cautious in making decisions that sacrifice others or put them at high risk.  Normally, we should do so only in constrained circumstances where we are implicitly or explicitly endowed with appropriate responsibility.  Our own individual calculation of high expected utility (no matter how rational and well-justified) is not normally, by itself, sufficient grounds for substantially risking or harming others.

The risk amplification issue is: If we universalize utilitarian decision-making in a way that permits many people to risk or sacrifice others whenever they reasonably calculate that it would be good to do so, we render ourselves collectively hostage to whomever has the most sacrificial reasonable calculation.  That was the point illustrated in the opening scenario.

[Figure: Simplified version of the opening scenario.  Five utilitarians have the opportunity to sacrifice five people to save an unknown number of others.  The button will be pressed by the utilitarian whose estimate errs highest.  Click to enlarge and clarify.]

My point is not that some utilitarians might be irrationally risky, though certainly that's a concern.  Rather, my point is that even if all utilitarians are perfectly rational, if they differ in their assessments of risk and benefit, and if all it takes to trigger a risky action is one utilitarian with the power to choose that action, then the odds of a bad outcome rise dramatically.

Advocates of utilitarian decision procedures can mitigate this problem in a few ways, but I'm not seeing how to escape it without radically altering the view.

First, a utilitarian could adopt a policy of decision conciliationism -- that is, if you see that most others aren't judging the risk or cost worth it, adjust your own assessment of the benefits and likelihoods, so that you fall in line with the majority.  However, strong forms of conciliationism are pretty radical in their consequences; and of course this only works if the utilitarians know that there are others in similar positions deciding differently.

Second, a utilitarian could build some risk aversion and loss aversion into their calculus.  This might be a good idea on independent grounds.  Unfortunately, aversion corrections only shift the weights around.  If the anticipated gains are sufficiently high, as judged by the most optimistic rational utilitarian, they will outweigh any discounts due to risk or loss aversion.

Third, they could move to rule utilitarianism: Endorse some rule according to which you shouldn't generally risk or sacrifice others without the right kind of authority.  Plausibly, the risk amplification argument above is exactly the sort of argument that might a motivate a utilitarian to adopt rule utilitarianism as a decision procedure rather than trying to evaluate the consequences of each act individually.  That is, it's a utilitarian argument in favor of not always acting according to utilitarian calculations.  However, the risk amplification and authority problems are so broad in scope (even with appropriate qualifications) that moving to rule utilitarianism to deal with them is to abandon act utilitarianism as a general decision procedure.

Of course, one could also design scenarios in which bad things happen if everyone is a rule-following deontologist!  Picture a thousand "do not kill" deontologists who will all die unless one of them kills another.  Tragedy.  We can cherry-pick scenarios in which any view will have unfortunate results.

However, I don't think my argument is that unfair.  The issues of authority and risk amplification are real problems for utilitarian decision procedures, as brought out in these cartoon examples.  We can easily imagine, I think, a utilitarian Robespierre, a utilitarian academic administrator, Sam Bankman-Fried with his hand on the destroy-or-duplicate button, calculating reasonably, and too easily inflicting well-intentioned risk on the rest of us.

Gunkel's Criticism of the No-Relevant-Difference Argument for Robot Rights

In a 2015 article, Mara Garza and I offer the following argument for the rights of some possible AI systems:

Premise 1: If Entity A deserves some particular degree of moral consideration and Entity B does not deserve that same degree of moral consideration, there must be some relevant difference between the two entities that grounds this difference in moral status.

Premise 2: There are possible AIs who do not differ in any such relevant respects from human beings.

Conclusion: Therefore, there are possible AIs who deserve a degree of moral consideration similar to that of human beings.

The argument is, we think, appealingly minimalist, avoiding controversial questions about the grounds of moral status.  Does human-like moral status require human-like capacity for pain or pleasure (as classical utilitarians would hold)?  Or human-like rational cognition, as Kant held?  Or the capacity for human-like varieties of flourishing?  Or the right types of social relations?

The No-Relevant-Difference Argument avoids these vexed questions, asserting only that whatever grounds moral status can be shared between robots and humans.  This is not an entirely empty claim about the grounds of moral status.  For example, the argument commits to denying that membership in the species Homo sapiens, or having a natural rather than artificial origin, is required for human-like moral status.

Compare egalitarianism about race and gender.  We needn't settle tricky questions about the grounds of moral status to know that all genders and races deserve similar moral consideration!  We need only know this: Whatever grounds moral status, it's not skin color, or possession of a Y chromosome, or any of the other things that might be thought to distinguish among the races or genders.

Garza and I explore four arguments for denying Premise 2 -- that is, for thinking that robots would inevitably differ from humans in some relevant respect.  We call these the objections from Psychological Difference, Duplicability, Otherness, and Existential Debt.  Today, rather than discussing Premise 2, I want to discuss David Gunkel's objection to our argument in his just-released book, Person, Thing, Robot.

[Image of Ralph and Person, Thing, Robot.  Ralph is a sculpture designed to look like an old-fashioned robot, composed of technological junk from the mid-20th century (sculptor: Jim Behrman).  I've named him after my father, whose birth name was Ralph Schwitzgebel.  My father was also a tinkerer and artist with technology from that era.]  

Gunkel acknowledges that the No-Relevant-Difference Argument "turns what would be a deficiency... -- [that] we cannot positively define the exact person-making qualities beyond a reasonable doubt -- into a feature" (p. 91).  However, he objects as follows:

The main difficulty with this alternative, however, is that it could just as easily be used to deny human beings access to rights as it could be used to grant rights to robots and other nonhuman artifacts.  Because the no relevant difference argument is theoretically minimal and not content dependent, it cuts both ways.  In the following remixed version, the premises remain intact; only the conclusion is modified.

Premise 1: If Entity A deserves some particular degree of moral consideration and Entity B does not deserve that same degree of moral consideration, there must be some relevant difference between the two entities that grounds this difference in moral status.

Premise 2: There are possible AIs who do not differ in any such relevant respects from human beings.

Conclusion: Therefore, there are possible human beings who, like AI systems, do not deserve moral consideration. 

In other words, the no relevant difference argument can be used either to argue for an extension of rights to other kinds of entities, like AI systems, robots, and artifacts, or, just as easily, to justify dehumanization, reification of human beings, and the exclusion and/or marginalization of others (p. 91-92, italics added).

This is an interesting objection.  However, I reject the appropriateness of the repeated phrase "just as easily", which I have italicized in the block quote.

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As the saying goes, one person's modus ponens is another's modus tollens.  Suppose you know that A implies B.  Modus ponens is an inference rule which assumes the truth of A and concludes that B must also be true.  Modus tollens is an inference rule which assumes the falsity of B and concludes that A must also be false.  For example, suppose you can establish that if anyone stole the cookies, it was Cookie Monster.  If you know that the cookies were stolen, modus ponens unmasks Cookie Monster as the thief.  If, on the other hand, you know that Cookie Monster has committed no crimes, modus tollens assures you that the cookies remain secure.

Gunkel correctly recognizes that the No Relevant Difference Argument can be reframed as a conditional: Assuming that human X and robot Y are similar in all morally relevant respects, then if human X deserves rights so also does robot Y.  This isn't exactly how Garza and I frame the argument -- our framing implicitly assumes that there is a standard level of moral consideration for human beings in general -- but it's a reasonable adaptation for someone wants to leave open the possibility that different humans deserve different levels of moral consideration.

In general, the plausibility of modus ponens vs modus tollens depends on the relative security of A vs not-B.  If you're rock-solid sure the cookies were stolen and have little faith in Cookie Monster's crimelessness, then ponens is the way to go.  If you've been tracking Cookie all day and know for sure he couldn't have committed a crime, then apply tollens.  The "easiness", so to speak, of ponens vs. tollens depends on one's confidence in A vs. not-B.

Few things are more secure in ethics than at least some humans deserve substantial moral consideration.  This gives us the rock-solid A that we need for modus ponens.  As long as we are not more certain all possible robots would not deserve rights than that some humans do deserve rights, modus ponens will be the correct move.  Ponens and tollens will not be equally "easy".

Still, Gunkel's adaptation of our argument does reveal a potential for abuse, which I had not previously considered, and which I thank him for highlighting.  Anyone who is more confident that robots of a certain sort are undeserving of moral consideration than they are of the moral considerability of some class of humans could potentially combine our No Relevant Difference principle with an appeal to the supposed robotlikeness of those humans to deny rights to those humans.

I don't think the No Relevant Difference principle warrants skepticism on those grounds.  Compare application of a principle like "do unto others as you would have them do unto you".  Although one could in principle reason "I want to punch him in the nose, so I guess I should punch myself in the nose", the fact that some people might potentially run such a tollens reveals more about their minor premises than it does about the Golden Rule.

I hope that such an abuse of the principle would be in any case rare.  People who want to deny rights to subgroups of humans will, I suspect, be motivated by other considerations, and appealing to those people's putative "robotlikeness" would probably be only an afterthought or metaphor.  Almost no one, I suspect, will be on the fence about the attribution of moral status to some group of people and then think, "whoa, now that I consider it, those people are like robots in every morally relevant respect, and I'm sure robots don't deserve rights, so tollens it is".  If anyone is tempted by such reasoning, I advise them to rethink the path by which they find themselves with that peculiar constellation of credences.

Strange Intelligence, Strange Philosophy

AI intelligence is strange -- strange in something like the etymological sense of external, foreign, unfamiliar, alien.  My PhD student Kendra Chilson (in unpublished work) argues that we should discard the familiar scale of subhuman → human-grade → superhuman.  AI systems do, and probably will continue to, operate orthogonally to simple scalar understandings of intelligence modeled on the human case.  We should expect them, she says, to be and remain strange intelligence[1] -- inseparably combining, in a single package, serious deficits and superhuman skills.  Future AI philosophers will, I suspect, prove to be strange in this same sense.

Most readers are probably familiar with the story of AlphaGo, which in 2016 defeated the world champion player of the game of go.  Famously, in the series of matches (which it won 4-1), it made several moves that human go experts regarded as bizarre -- moves that a skilled human go player would never have made, and yet which proved instrumental in its victory -- while also, in its losing match, making some mistakes characteristic of simple computer programs, which go experts know to avoid.

Similarly, self-driving cars are in some respects better and safer drivers than humans, while nevertheless sometimes making mistakes that few humans would make.

Large Language Models have stunning capacity to swiftly create competent and even creative texts on a huge breadth of topics, while still failing conspicuously in some simple common sense tasks. they can write creative-seeming poetry and academic papers, often better than the average first-year university student.  Yet -- borrowing an example from Sean Carroll -- I just had the following exchange with GPT-4 (the most up-to-date version of the most popular large language model):

GPT-4 seems not to recognize that a hot skillet will be plenty cool by the next day.

I'm a "Stanford school" philosopher of science.  Core to Stanford school thinking is this: The world is intractably complex; and so to deal with it, we limited beings need to employ simplified (scientific or everyday) models and take cognitive shortcuts.  We need to find rough patterns in go, since we cannot pursue every possible move down every possible branch.  We need to find rough patterns in the chaos of visual input, guessing about the objects around us and how they might behave.  We need quick-and-dirty ways to extract meaning from linguistic input in the swift-moving world, relating it somehow to what we already know, and producing linguistic responses without too much delay.  There will be different ways of building these simplified models and implementing these shortcuts, with different strengths and weaknesses.  There is rarely a single best way to render the complexity of the world tractable.  In psychology, see also Gigerenzer on heuristics.

Now mix Stanford school philosophy of science, the psychology of heuristics, and Chilson's idea of strange intelligence.  AI, because it is so different from us in its underlying cognitive structure, will approach the world with a very different set of heuristics, idealizations, models, and simplifications than we do.  Dramatic outperformance in some respects, coupled with what we regard as shockingly stupid mistakes in others, is exactly what we should expect.

If the AI system makes a visual mistake in judging the movement of a bus -- a mistake (perhaps) that no human would make -- well, we human beings also make visual mistakes, and some of those mistakes, perhaps, would never be made by an AI system.  From an AI perspective, our susceptibility to the Muller-Lyer illusion might look remarkably stupid.  Of course, we design our driving environment to complement our vision: We require headlights, taillights, marked curves, lane markers, smooth roads of consistent coloration, etc.  Presumably, if society commits to driverless cars, we will similarly design the driving environment to complement their vision, and "stupid" AI mistakes will become rarer.

I want to bring this back to the idea of an AI philosopher.  About a year and a half ago, Anna Strasser, Matthew Crosby, and I built a language model of philosopher Daniel Dennett.  We fine-tuned GPT-3 on Dennett's corpus, so that the language model's outputs would reflect a compromise between the base model of GPT-3 and patterns in Dennett's writing.  We called the resulting model Digi-Dan.  In a study collaborative with my son David, we then posed philosophical questions to both Digi-Dan and the actual Daniel Dennett.  Although Digi-Dan flubbed a few questions, overall it performed remarkably well.  Philosophical experts were often unable to distinguish Digi-Dan's answers from Dennett's own answers.

Picture now a strange AI philosopher -- DigiDan improved.  This AI system will produce philosophical texts very differently than we do.  It need not be fully superhuman in its capacities to be interesting.  It might even, sometimes, strike us as remarkably, foolishly wrong.  (In fairness, other human philosophers sometimes strike me the same way.)  But even if subhuman in some respects, if this AI philosopher also sometimes produces strange but brilliant texts -- analogous to the strange but brilliant moves of AlphaGo, texts that no human philosopher would create but which on careful study contain intriguing philosophical moves -- it could be a philosophical interlocutor of substantial interest.

Philosophy, I have long argued, benefits from including people with a diversity of perspectives.  Strange AI might also be appreciated as a source of philosophical cognitive diversity, occasionally generating texts that contain sparks of something genuinely new, different, and worthwhile that would not otherwise exist.

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[1] Kendra Chilson is not the first to use the phrase "strange intelligence" with this meaning in an AI context, but the usage was new to me; and perhaps through her work it will catch on more widely.

Skeletal vs Fleshed-Out Philosophy

All philosophical views are to some degree skeletal. By this, I mean that the details of their application remain to some extent open. This is true of virtually any formal system: Even the 156-page rule handbook for golf couldn't cover every eventuality: What if the ball somehow splits in two and one half falls in the hole? What if an alien spaceship levitates the ball for two seconds as it's arcing through the air? (See the literature on "open textured" statements.)

Still, some philosophical views are more skeletal than others. A bare statement like "maximize utility" is much more skeletal, much less fleshed out, than a detailed manual of utilitarian consequentialist advice. Today, I want to add a little flesh to the skeletal vs. fleshed-out distinction. Doing so will, I hope, help clarify some of the value of trying to walk the walk as an ethicist. (For more on walking the walk, see last month's posts here and here.)

[Midjourney rendition of a person and a skeleton talking philosophy, against a background of stars]

Using "maximize utility" as an example, let's consider sources of linguistic, metaphysical, and epistemic openness.

Linguistic: What does "utility" mean, exactly? Maybe utility is positively valenced conscious experiences. Or maybe utility is welfare or well-being more broadly construed. What counts as "maximizing"? Is it a sum or a ratio? Is the scope truly universal -- for all entities in the entire cosmos over all time, or is it limited in some way (e.g., to humans, to Earth, to currently existing organisms)? Absent specification (by some means or other), there will be no fact of the matter whether, say, two acts with otherwise identical results, but one of which also slightly improves the knowledge (but not happiness) of one 26th-century Martian, are equally choiceworthy according to the motto. 

Metaphysical: Consider a broad sense of utility as well-being or flourishing. If well-being has components that are not strictly commensurable -- that is, which cannot be precisely weighed against each other -- then the advice to maximize utility leaves some applications open. Plausibly, experiencing positive emotions and achieving wisdom (whatever that is, exactly) are both part of flourishing. While it might be clear that a tiny loss of positive emotion is worth trading off for a huge increase in wisdom and vice versa, there might be no fact of the matter exactly what the best tradeoff ratio is -- and thus, sometimes, no fact of the matter whether someone with moderate levels of positive emotion and moderate levels of wisdom has more well-being than someone with a bit less positive emotion and a bit more wisdom.

Epistemic: Even absent linguistic and metaphysical openness, there can be epistemic openness. Imagine we render the utilitarian motto completely precise: Maximize the total sum of positive minus negative conscious experiences for all entities in the cosmos in the entire history of the cosmos (and whatever else needs precisification). Posit that there is always an exact fact of the matter how to weigh competing goods in the common coin of utility and there are never ties. Suppose further that it is possible in principle to precisely specify what an "action" is, individuating all the possible alternative actions at each particular moment. It should then always be the case that there is exactly one action you could do that would "maximize utility". But could you know what this action is? That's doubtful! Every action has a huge number of non-obvious consequences. This is ignorance; but we can also think of it as a kind of openness, to highlight its similarity to linguistic and metaphysical openness or indeterminacy. The advice "maximize utility", however linguistically and metaphysically precise, leaves it still epistemically open what you should actually do.

Parallel remarks apply to other ethical principles: "Act on that maxim that you can will to be a universal law", "be kind", "don't discriminate based on race", "don't perform medical experiments on someone without their consent" -- all exhibit some linguistic, metaphysical, and epistemic openness.

Some philosophers might deny linguistic and/or metaphysical openness: Maybe context always renders meanings perfectly precise, and maybe normative facts are never actually mushy-edged and indeterminate. Okay. Epistemic openness will remain. As long as we -- the reader, the consumer, the applier, of the philosophical doctrine -- can't reasonably be expected to grasp the full range of application, the view remains skeletal in my sense of the term.

It's not just ethics. Similar openness also pervades other areas of philosophy. For example, "higher order" theories of consciousness hold that an entity is conscious if and only if it has the right kind of representations of or knowledge of its own mental states or cognitive processes. Linguistically, what is meant by a "higher order representation", exactly? Metaphysically, might there be borderline cases that are neither determinately conscious nor unconscious? Epistemically, even if we could precisify the linguistic and metaphysical issues, what actual entities or states satisfy the criteria (mice? garden snails? hypothetical robots of various configurations?).

The degree of openness of a position is itself, to some extent, open: There's linguistic, metaphysical, and epistemic meta-openness, we might say. Even a highly skeletal view rules some things out. No reasonable fleshing out of "maximize utility" is consistent with torturing babies for no reason. But it's generally unclear where exactly the boundaries of openness lie, and there might be no precise boundary to be discovered.

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Now, there's something to be said for skeletal philosophy. Simple maxims, which can be fleshed out in various ways, have an important place in our thinking. But at some point, the skeleton needs to get moving, if it's going to be of use. Lying passively in place, it might block a few ideas -- those that crash directly against its obvious bones. But to be livable, applicable, it needs some muscle. It needs to get up and walk over to real, specific situations. What does "maximize utility" (or whatever other policy, motto, slogan, principle) actually recommend in this particular case? Too skeletal a view will be silent, leaving it open.

Enter the policy of walking the walk. As an ethicist, attempting to walk the walk forces you to flesh out your view, applied at least to the kinds of situations you confront in your own life -- which will of course be highly relevant to you and might also be relevant to many of your readers. What actions, specifically, should a 21st-century middle-class Californian professor do to "maximize utility"? Does your motto "be kind" require you to be kind to this person, in this particular situation, in this particular way? Confronting actual cases and making actual decisions motivates you to repair your ignorance about how the view would best apply to those cases. Linguistically, too, walking the walk enables you to make the content of your mottoes more precise: "Be kind" means -- in part -- do stuff like this. In contrast, if you satisfy yourself with broad slogans, or broad slogans plus a few paragraph-long thought-experimental applications, your view will never be more than highly skeletal. 

Not only our readers, but also we philosophers ourselves, normally remain substantially unclear on what our skeletal mottoes really amount to until we actually try to apply them to concrete cases. In ethics -- at least concerning principles meant to govern everyday life (and not just rare or remote cases) -- the substance of one's own life is typically the best and most natural way to add that flesh.

Elisabeth of Bohemia 1, Descartes 0

I'm loving reading the 1643 correspondence between Elisabeth of Bohemia and Descartes! I'm embarrassed to confess that I hadn't read it before now; the standard Cottingham et al. edition presents only selections from Descartes' side. I'd seen quotes of Elisabeth, but not the whole exchange as it played out. Elisabeth's letters are gems. She has Descartes on the ropes, and she puts her concerns so plainly and sensibly (in Bennett's translation; I haven't attempted to read the antique French). You can practically feel Descartes squirming against her objections. I have a clear and distinct idea of Descartes ducking and dodging!

Here's my (somewhat cheeky) summary, with comments and evaluation at the end.

Elisabeth, May 6, 1643:

I'm so ignorant and you're so learned! Here's what I don't understand about your view: How can an immaterial soul, simply by thinking, possibly cause a bodily action?

Specifically,

it seems that how a thing moves depends solely on (i) how much it is pushed, (ii) the manner in which it is pushed, or (iii) the surface-texture and shape of the thing that pushes it. The first two of those require contact between the two things, and the third requires that the causally active thing be extended [i.e., occupy a region of space]. Your notion of the soul entirely excludes extension, and it appears to me that an immaterial thing can't possibly touch anything else.

Also, if, as you say, thinking is the essential property of human souls, what about unborn children and people who have fainted, who presumably have souls without thinking?

René, May 21, 1643:

Admittedly in my writings I talk much more about the fact that the soul thinks than about the question of how it is united with the body. This idea of the union of the soul and the body is basic and can be understood only through itself. It's so easy to get confused by using your imagination or trying to apply notions that aren't appropriate to the case!

For a comparison, however, think about how the weight of a rock moves it downwards. One might (mistakenly, I hope later to show) think of weight as a "real quality" about which we know nothing except that it has the power to move the body toward the centre of the earth. The soul's power to move the body is analogous.

Elisabeth, June 10, 1643:

Please forgive my stupidity! I wish I had the time to develop your level of expertise. But why should I be persuaded that an immaterial soul can move a material body by this analogy to weight? If we think in terms of the old idea of weight, why shouldn't we then conclude by your reasoning that things move downward due to the power of immaterial causes? I can't conceive of "what is immaterial" except negatively as "what is not material" and as what can't enter into causal relations with matter. I'd rather concede that the soul is material than that an immaterial thing could move a body.

René, May 28, 1643:

This matter of the soul's union with the body is a very dark affair when it comes from the intellect (whether alone or aided by the imagination). People who just use their senses, in the ordinary course of life, have no doubt that the soul moves the body. We shouldn't spend too much time in intellectual thinking. In fact,

I never spend more than a few hours a day in the thoughts involving the imagination, or more than a few hours a year on thoughts that involve the intellect alone. I give all the rest of my time to the relaxation of the senses and the repose of the mind.

The human mind can't clearly conceive the soul's distinctness from the body and its union with the body simultaneously. The comparison with weight was imperfect, but without philosophizing everyone knows that they have body and thought and that thought can move the body.

But since you remark that it is easier to attribute matter and extension to the soul than to credit it with the capacity to move and be moved by the body without having matter, please feel free to attribute this matter and extension to the soul -- because that's what it is to conceive it as united to the body.

Still, once you do this, you'll find that matter is not thought because the matter has a definite location, excluding other matter. But again, thinking too much about metaphysics is harmful.

Elisabeth, July 1, 1643:

I hope my letters aren't troubling you.

I find from your letter that the senses show me that the soul moves the body, but as for how it does so, the senses tell me nothing about that, any more than the intellect and imagination do. This leads me to think that the soul has properties that we don't know -- which might overturn your doctrine... that the soul is not extended.

As you have emphasized in your writings, all our errors come from our forming judgments about things we don't perceive well enough. Since we can't perceive how the soul moves the body, I am left with my initial doubt, that is, my thinking that perhaps after all the soul is extended.

There is no record of a reply by Descartes.

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Zing! Elisabeth shows up so much better than Descartes in this exchange. She immediately homes in on the historically most important (and continuing) objection to Cartesian substance dualism: the question of how, if at all, an immaterial soul and a material object could causally interact. She efficiently and elegantly formulates a version of the principle of "the causal closure of the physical", according to which material events can only be caused by other material events, connecting that idea both with Descartes' denial that the soul is extended in space and with the view, widely accepted by early modern philosophers before Newton, that physical causation requires direct physical contact (no "action at a distance"). Jaegwon Kim notes (2011, p. 49) that hers might be the first causal argument for a materialist view of the mind. To top it off, she poses an excellent objection (from fetuses and fainting spells) to the idea that thinking is essential to having a soul.

Descartes' reply by analogy to weight is weak. As Elisabeth notes, it doesn't really answer the question of how the process is supposed to work for souls. Descartes' own theory of weight (articulated the subsequent year in Principles of Philosophy, dedicated to Elisabeth) involves action by contact (light particles spinning off the rotating Earth shoot up, displacing heavier particles down: IV.20-24). At best, Descartes is saying that the false, old idea of weight didn't involve contact, so why not think souls can also have influence without contact? Elisabeth's reply implicitly suggests a dilemma: If downward motion is by contact, then weight is not an example of how causation without contact is possible. If downward motion is not by contact, then shouldn't we think (absurdly?) that things move down due to the action of immaterial souls? She also notes that "immaterial" just seems to be a negative idea, not something we can form a clear, positive conception of.

Elisabeth's response forces Descartes concede that we can't in fact think clearly and distinctly about these matters. This is a major concession, given the centrality of the standard of "clear and distinct" ideas to Descartes' philosophy. He comes off almost as a mysterian! He also seems to partly retract what is perhaps the most central idea in his dualist metaphysics -- that the soul does not have extension. Elisabeth should feel free to attribute matter and extension to the soul, after all! Indeed, in saying that attributing matter and extension is "what it is to conceive [the soul] as united to the body", Descartes seriously muddies the interpretation of his positive view about the nature of souls.

It's also worth noting that Descartes entirely ignores Elisabeth's excellent fetus and fainting question.

I had previously been familiar with Descartes' famous quote that he spends no more than a few hours a year on thoughts involving the intellect alone; but reading the full exchange provides interesting context. His aim in saying that is to convince Elisabeth not to put too much energy into objecting to his account of how the soul works.

Understandably, Elisabeth is dissatisfied. She even gestures (though not in so many words) toward Descartes' methodological self-contradiction: Descartes famously says that philosophizing requires that we have clear ideas and that our errors all arise from failure to do so -- yet here he is, saying that there's an issue at the core of his metaphysics about which it's not possible to think clearly! Shouldn't he admit, then, that on this very point he's liable to be mistaken?

If Descartes attempted a further reply, the reply is lost. Their later correspondence treats other issues.

The whole correspondence is just 15 pages, so I'd encourage you to read it yourself. This summary necessarily omits interesting detail and nuance. In this exchange, Elisabeth is by far the better philosopher.

[image source]

Percentage of Women Philosophy Majors Has Risen Sharply Since 2016 -- Why? Or: The 2017 Knuckle

Back in 2017, I noticed that the percentage of women philosophy majors in the U.S. had been 30%-34% for "approximately forever". That is, despite the increasing percentage of Bachelor's degrees awarded to women overall and in most other majors, the percentage of philosophy Bachelor's degrees awarded to women had been remarkably steady from the first available years (1986-1987) in the NCES IPEDS database through the then-most-recent data year (2016).

In the past few years, however, I have noticed some signs of change. The most recent NCES IPEDS data release, which I analyzed this morning, statistically solidifies the trend. Women now constitute over 40% of philosophy Bachelor's degree recipients. I would argue that this is a very material change from the long-standing trend of 30-34%. If parity is 50%, a change from 32% women to 41% women constitutes a halving of the disparity. Furthermore, the change has been entirely in the most recent six years' of data -- remarkably swift for this type of demographic shift.

The chart below shows the historical trend through the most recent available year (2022). I've marked the 30%-34% band with thick horiztonal lines. A thin vertical line marks 2017, the first year to cross the 34% mark (34.9%). The most recent years are 41.4% and 41.3% respectively.

[click to enlarge and clarify]

Given the knuckle-like change in the slope of the graph, let's call this the 2017 Knuckle.

What I find puzzling is why?

This doesn't reflect an overall trend of increasing percentages of women across majors. Overall, women have been 56%-58% of Bachelor's degree recipients throughout the 21st century. Most other humanities and social sciences had a much earlier increase in the proportion of women.

However, interestingly, the physical sciences and engineering, which have also tended to be disproportionately men, have showed some similar trends. Since 2010, physics majors have increased from 40% to 45% women -- with all of that increase being since 2017. Since 2010, Engineering has increased from 18% to 25% women, with the bulk of the increase since 2016. Since 2010, "Engineering Technologies and Engineering-related Fields" (which NCES classifies separately from Engineering) has also increased from 10% to 15% women, again with most of the increase since 2016. Among the humanities and social sciences, Economics is maybe the only large major similar to Philosophy in gender disparity, and in Economics we see a similar trend, though smaller: an increase from 31% to 35% women between 2010 and 2022, again with most of the gain since 2016.

Since people tend to decide their majors a few years before graduating, whatever explains these trends must have begun in approximately 2013-2016, then increased through at least 2020. Any hypotheses?

It's probably not a result of change in the percentage of women faculty: Faculty turnover is slow, and at least in philosophy the evidence suggests a slow increase over the decades, rather than a knuckle. (Data are sparser and less reliable on this issue, but see here, here and here.) There also wasn't much change in the 2010s in the percentage of women earning Philosophy PhDs in the U.S.

A modeling hypothesis would suggest that change in the percentage of women philosophy majors is driven by a change in the percentage of women faculty and TAs in Philosophy. In contrast, a pipeline hypothesis predicts that change in the percentage of women philosophy majors leads to a change in the percentage of women graduate students and (years later) faculty. Both hypotheses posit a relationship between women undergraduates and women instructors, but with different directions of causation. (The hypotheses aren't, of course, incompatible: Causation might flow both ways.) At least in Philosophy, the modeling hypothesis doesn't seem to explain the 2017 Knuckle. Concerning the pipeline, it's too early to tell, but when the NSF releases their data on doctorates in October, I'll look for preliminary signs.

I'm also inclined to think -- though I'm certainly open to evidence -- that feminism has been slowly, steadily increasing in U.S. culture, rather than being more or less flat since the late 1980s and recently increasing again. So a general cultural increase in feminist attitudes wouldn't specifically explain the 2017 Knuckle. Now it is true that 2015-2017 saw the rise of Trump, and the backlash against Trump, as well as the explosion of the #MeToo movement. Maybe that's important? It would be pretty remarkable if those cultural events had a substantial effect on the percentage of women undergraduates declaring Philosophy, Economics, Physics, and Engineering majors.

Further thoughts? What explains the 2017 Knuckle?

It could be interesting to look at other countries, and at race/ethnicity data, and at majors that tend to be disproporately women -- patterns there could potentially cast light on the effect -- but enough for today.

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Methodological notes: NCES IPEDS attempts to collect data on every graduating student in accredited Bachelor's programs in the U.S., using administrator-supplied statistics. Gender categories are binary "men" and "women" with no unclassified students. Data are limited to "U.S. only" institutions in classification category 38.01 ("Philosophy") and include both first and second majors back through 2001. Before 2001, only first majors are available. Each year includes all graduates during the academic year ending in that year (e.g., 2022 includes all students from the 2021-2022 academic year). For engineering and physical sciences, I used major catories 15, 16, and 40; and for Economics, 45.06.