His new book is coming out in January, and the subtitle is The Biology of Gender, Race, and Class. I will get to the details shortly, but my bottom-line review is “Not as controversial as you might think,” but do note the normalization at the end of that phrase.
Here is one bit from p.294 toward the end of the book:
Nothing we are going to learn will diminish our common humanity. Nothing we learn will justify rank-ordering human groups from superior to inferior — the bundles of qualities that make us human are far too complicated for that. Nothing we learn will lend itself to genetic determinism. We live our lives with an abundance of unpredictability, both genetic and environmental.
Most of the book defends ten key propositions, laid out on pp.7-8. The first four of those propositions concern differences between men and women (“Sex differences in personality are consistent worldwide…”) and I do not find those controversial, so I will not cover them. The chapters on those propositions provide a good survey of the evidence, and a good answer to the denialists, though I doubt if Murray is the right person to win them over. Let’s now turn to the other propositions, with my commentary along the way:
5. Human populations are genetically distinctive in ways that correspond to self-identified race and ethnicity.
True, but Murray’s analysis did not push me beyond the usual citations of lactose intolerance, sickle cell anemia, adaptation to high altitudes, and the like. That said, pp.190-195 offer a very dense discussion of target alleles for various traits, such as schizophrenia, and how those target alleles vary across different groups. I found those pages difficult to follow, and also wished that discussion had been fifty pages rather than five. Toward the end of that discussion, Murray does write (p.194): “…proof of the role of natural selection for many genetic differences will remain unobservable without methodological breakthroughs.” With that I definitely agree.
On p.195 he adds “It is implausible to expect that none of the imbalances will yield evidence of significant genetic differences related to phenotypic differences across continental populations.” That returns to my core point about this book not shifting my priors. You could agree with that sentence (noting the ambiguity in the word “significant”) and still have a quite modest vision of what those differences might mean. In any case, nothing in the book pushes me beyond that sentence in the direction of the geneticists.
And here the contrast with the chapters on men and women becomes (unintentionally?) glaring: those biological differences are relatively easy to demonstrate, so perhaps hard-to-demonstrate biological differences are not so significant. That too is just a conjecture, but there are multiple ways to play the “absence of evidence” and “how to interpret the residuals” cards, and I wish those had received a more extensive philosophy of science-like discussion.
Now let’s move to the next proposition:
6. Evolutionary selection pressure since humans left Africa has been extensive and mostly local.
That one strikes me as a miswording or misstatement, though I do not see that it corresponds to any actual mistakes in the broader text. You might think that general, non-local evolutionary selection for all humans has been quite large over the millennia, relative to local selection. I genuinely do not know the ratio here, but Murray does not seem to address the actual comparison of “across all human groups” vs. “local” as loci of selection pressures.
7. Continental population differences in variants associated with personality, abilities, and social behavior are common.
Clearly true, but note this proposition does not claim biological roots for those differences. The real question comes in the next proposition:
8. The shared environment usually plays a minor role in explaining personalities, abilities, and social behavior.
Here I have what I think is a major disagreement with Murray. If he means the term “shared environment” in the narrow sense used by say twin studies, he is probably correct. But in the more literal, Webster-derived conception of “shared environment” I very much disagree. Culture is a truly major shaper of our personalities, abilities, and social behavior, and self-evidently so. For my taste the book did not contain nearly enough discussion of culture and in fact there is virtually no discussion of the concept or its power, as a look at the index will verify. The real lesson of “twins studies plus anthropology” is that you have to control almost all of a person’s environment to have a major impact, but a major impact indeed can be had. I behave very differently than my Irish potato famine ancestors, and not because I am genetically 1/8 from the Madeira Islands. That said, within the narrower range of environmental variation measured in twins studies…well those studies seem to be fairly accurate.
9. Class structure is importantly based on differences in abilities that have a substantial genetic component.
Correct as stated, but I see those differences as much less genetic than Murray does. For instance, IQ is to some extent heritable, but how much does that shape economic outcomes? It is worth turning to Murray’s discussion on p.232 and the associated footnote 17 (pp.428-429). His main source is what is to me a flawed meta-study on IQ and job performance (Murray to his credit does also cite the best-known critique of such studies). I would opt more directly for the labor market literature on IQ and individual earnings, based on actual measured wages, which shows fairly modest correlations between IQ and earnings (read here, here and here). So, at the very least, the inherited IQ-based permanent stratification version of The Bell Curve argument is much more compelling to Murray than it is to me.
10. Outside interventions are inherently constrained in the effects they can have on personality, abilities, and social behavior.
Clearly this is literally true, if only because of the meaning of “constrained.” But mostly I would repeat my remarks on culture from #8. Cultures change, and over time they are likely to change a great deal. For instance, early in the 20th century, Korea, Japan, and China often were described as low work ethic cultures. As cultures change, in turn those cultures can shape the personalities, abilities, and social behaviors of subsequent generations, in significant ways albeit constrained. So while Murray is correct as stated, I believe I would disagree with his intended substantive point about the weight of relative forces.
Overall this is a serious and well-written book that presents a great deal of scientific evidence very effectively. Anyone reading it will learn a lot. But it didn’t change my mind on much, least of all the most controversial questions in this area. If anything, in the Bayesian sense it probably nudged me away from geneticist-based arguments, simply because it did not push me any further towards them.
Murray of course will write the book he wants to, but my personal wish list was two-fold: a) a book leaving most of the normal science behind, and focusing only on the uncertain and controversial frontier issues, in great detail, and b) much more discussion of the import of culture.
Most of all, I am happy that America’s culture of achievement is inducing Murray to continue to produce major works at the age of 76, soon to be 77.
You can pre-order here.
Here are some projects I’d like to see funded, some through my own ventures, or others through alternative mechanisms. On these issues, the right person could have an enormous impact, whether through the research side or directly coming up with actionable ideas, including of course creating and building companies.
More studies of super-effective people. Either individually or collectively. If you take the outliers in any domain, what should our intuitions be for understanding the underlying processes determining how many people could have ended up in those positions? How many people had the right genes but had the wrong upbringing? How many people had the right genes and the right upbringing but the wrong luck, or perhaps society failed them in some other manner? The answers to these questions have significant policy implications.
A comprehensive analysis and critique of the NIH and NSF. The US funds more science research than any other country — about $35 billion per year on the NIH and $8 billion per year on the NSF. How exactly do these institutions work? How have they changed over time and have these changes been for good or bad? Based on what we now know, how might we better structure the NIH and NSF? What experiments should we run or what kind of studies should we perform?
Why is life expectancy so long in Hong Kong? Life expectancy in Hong Kong is 84.23 years, more than five years longer than the US and the highest in the world. Hong Kong is not that wealthy (median household income is $38,000 USD); it’s somewhat polluted; people don’t obviously eat what seems like a healthy diet; and they don’t seem to exercise a great deal. What should we learn from this?
Bloomberg Terminal for everything. This might be a nonprofit, a company, or a government project. To state the obvious, many analyses hinge on having the right data. If you’re in finance, getting the right data is often easy: just pull it up on your Bloomberg terminal. But there is no practical way to ask “what most correlates with life expectancy in Hong Kong?” (See above on that topic.) Figure out a way to build a growing corpus of structured data across the broadest variety of domains.
A comprehensive guide to the American healthcare system. The American healthcare system is by far the world’s biggest and also by a considerable margin the world’s most influential. Yet there is no comprehensive, dispassionate, and analytical disaggregation of how it all works. Who are the actors and what are their incentives? To the degree that the relationships between different entities are in equilibrium, what are the forces ensuring they stay there? What is the Sankey diagram of fund flows within the U.S. healthcare system?
Better answers for how to quantify worker productivity. In most knowledge industries, companies have nothing better than highly subjective measures (i.e., supervisors’ assessments) of worker productivity. In theory, it seems significant improvements should be possible. In the short term, is it possible to measure the productivity or efficacy of individual managers, software engineers, educators, scientists? How about teams, and what size of team? And can we do so without creating Goodhart’s Law problems?
What should Widodo do? Indonesia is a large, populous middle-income country. It faces no major near-term security threats. It has a small manufacturing base and no major non-commodity export sectors. What is the best non-bureaucratic 10 page economic development briefing document and set of prescriptions that one could write for Indonesia’s president? For Indonesia, substitute Philippines, Chile, or Morocco.
A comparative study of foundations and their efficacy. Philanthropic foundations are behind a lot of important work. But how does a foundation decide what it wants and how the resulting grants should be structured? How effective are the programs of that foundation? In practice, how have its institutional mechanisms evolved? Imagine some kind of resource that answered these questions for the major American foundations.
Institutional critiques. More broadly, there is no discipline of institutional criticism. There is a very rich literature of policy criticism in economics, journalism, and non-fiction books. There is also a rich literature of “corporate criticism”: there are thousands of articles about how Facebook (budget: $20 billion) works and how it might be good or bad. But there is relatively little analysis of the most important institutions in our society: government departments. How is the Department of Agriculture (budget: $150 billion) organized and how effective or not is it? How about the Department of Energy (budget: $32 billion)? And why are not those questions paramount in the minds of policymakers?
Cultures of excellence. If you ask informed Filipinos why the street food is mediocre, they will tell you that Philippines lacks a “culture of excellence”. It seems that some kind of “culture of doing things really well” has very persistent and generalizable effects. South Korea and Japan have developed much more rapidly than many Asian countries, despite many others adopting relatively free “Washington Consensus”-style trade policies. Russia still has higher GDP per capita than Mexico despite Mexico’s economic policies having been much better than Russia’s for many, many decades at this point. How should we think about cultures of excellence?
Regeneration at the government layer. Herbert Kaufman (unsurprisingly) concludes in an empirical study that government organizations don’t die. While we might all agree that this is a problem, actionable solutions are in short supply. What can or should we do about this?
IQ paradox. Ron Unz points out that intergenerational variation of IQ may be much higher than is often assumed, citing Ireland and Croatia as examples. For instance, not long ago Ireland had sub-par measured IQ and now that figure is much higher, following growth and prosperity. The policy implications of IQ disparities across nations may therefore be different to what might otherwise obviously follow: perhaps environment matters much more than is assumed. If so, what should we be doing more or less of?
Credible plans for new top-tier universities. 7 of the best 25 universities in the world (Times ranking) were started in the US between 1861 and 1891 by ambitious reformers. It’s probably harder in many ways to start an impactful new university today… but it’s likely not impossible and the returns to doing so successfully might be very high. What might be a good plan? Why have so few of these plans come to fruition?
Summaries of the state of knowledge in different fields. As a general matter, a lot of oral knowledge in the world is still not readily available, and reflection on this fact might lead one in many interesting directions. One obvious application is helping people more readily understand the present state of affairs in different domains. If I want to know “how we’re doing” in, say, antiviral drug development, I could spend a few hours hunting for top researchers, email a few, and perhaps get on calls to obtain their candid assessments. Are we making good progress? What are the most important open problems? What’s holding things back? And so on. How can we make all of this knowledge publicly available across all fields?
Mechanisms for better matching. One of the single interventions that could do the most to improve global welfare would be to improve the efficiency of the partner/marriage matching ecosystem. Online dating demonstrates that significant change (and maybe even improvement?) is possible, with some figures suggesting that up to two thirds of relationships in the US may now be initiated through online dating services. Accomplished people often seem to struggle with this challenge. Good solutions would be important.
What should Durkan do? Jenny Durkan is the current mayor of Seattle. As cities become more important loci of economic activity in the world, the importance of effective city governance will increase. As with the Widodo challenge, what is the best 10 page briefing document and set of prescriptions that one could write for her? What about Baltimore and St. Louis?
Self-recommending if there ever was such a thing, here is the audio and transcript. In addition to all of the expected topics, including gender in the economics profession, we even got around to Indian classical music and Bach cantatas (she prefers the latter). Excerpt:
COWEN: Do you worry much that the RCT method — it centralizes authority in too few institutions? You need a certain amount of money. You need some managerial ability. You need connections abroad. It’s not like running regressions — everyone can do it on their PC. Is that, in some way, going to slow down science? You get more reliable results, but there’s much less competition of ideas, it seems.
DUFLO: I think it would be the case if we had not been mindful of this problem from the beginning. And it might still be the case to some extent. But I actually think that we’ve put a lot of effort in avoiding it to be the case.
When you take an organization like J-PAL, just in India we have 200 staff members. And we have, at any given time, 1,000 people running surveys. I say we, but these people are not running my project. These people are running the projects of dozens and dozens of researchers. When I started, I couldn’t have started without having the backing of my team because it was such a risky proposition that you needed to be able to easy risk capital kind of things.
But at this point, because of the infrastructure, it’s much more normal sense. People can get in with no funding of their own, in part because one of the things we are doing as a network is raising a lot of money to redistribute to other people widely. J-PAL has 400 researchers that are affiliated to it, or invited researchers, many of them quite, quite junior.
So that sort of mixture — it was very important to us, and I think we’ve been quite successful at making the tool marginally available. It’s never going to be like running a regression from your computer. But my philosophy is that if you have the drive and you’re willing to put in your own sweat equity, you can do it. And our students and many other students who are not at top institutions are doing it.
COWEN: On the internet, there’s a photo of a teenage Esther Duflo — at least it looks like you — protesting against fascism in Russia on top of a tank, is it?
DUFLO: That was a bus, and it was me. It was me. So that was in 1991. This was not when I lived for one year there. I lived one year in ’93–’94. But this was in ’91. I had gone to Russia about every year since I was a teen to learn Russian. I happened to be there the summer where there was this putsch against Gorbachev. That summer…
And someone gave me that fashizm ne poletit placard and asked me to hold it. And I’m like, “Sure, I’m going to hold it.” So I’m holding my placard. We stayed there for a long time when things were happening. Next time I saw in the evening, my parents called me, “What are you doing?” Because it turned out that that image was on all the TVs in the world. [laughs] And that’s how I very briefly became the face of this revolution.
COWEN: Does child-rearing in France strike you as more sensible than child-rearing in the United States?
DUFLO: Oh very much so, very much so.
COWEN: And why?
DUFLO: You know that book, Bringing Up Bébé?
DUFLO: I think she picked up on something which rings so true to me, which maybe is a marginal point about the US versus France. In France people are reasonably content to just go with the flow and do what everybody does. Every kid eats the same thing at 4:30, has dinner at the same time, has gone through the same experiences, learned the same songs, and everybody thinks they are totally free. But in fact, they are all on this pretty sensible railroad. And also, they don’t agonize about it.
In the US, child-rearing is one more occasion to make a statement about your identity. You’re the kind of mother that carries the baby, or you’re the kind of mother that puts the baby in a stroller. And somehow it almost can predict what you’re going to think about Donald Trump. That’s crazy. Some people are so concerned about what they do. Not only they feel that they have to invest a ton in their children, and they feel inadequate if they are not able to, but also, exactly what they do creates them as people.
In France that’s not there, and I think that makes everybody so much more laid back, children and adults.
That is the upshot of my latest Bloomberg column, as the Doing Business index, PISA scores, and the Corruption Perceptions Index have been highly influential. Here are a few of my further requests:
These successes raise a question: Which other indexes might be useful? Think of the suggestions that follow as a kind of Christmas wish list.
How about a loneliness index? David Brooks has argued that America faces a crisis of loneliness, making us unhappy and impoverishing us spiritually. I find these claims plausible, especially since the median U.S. household size has been shrinking. Still, just how bad is this problem? One recent study found that American loneliness has not been rising lately, and that loneliness increases only after people reach their early 70s…
A stress index for Americans another related idea: Just how much do our lives focus our attention on our worries rather than on our joys and hopeful expectations?
There are less emotional concerns as well. How about an infrastructure speed index? I worry about bureaucratization and the slow pace of building important public works. Construction on Manhattan’s Second Avenue subway line, for example, started in 1972, paused, resumed in 2004, and was finally completed (the first phase, anyway) in 2017. In contrast, construction of the core New York City subway system, with 28 stations, began in 1900 and finished in 1904. Similarly, construction of the Empire State Building took only 410 days.
Why do so many U.S. infrastructure projects today take so long? And if the process of improving and reshaping the environment to further human progress is now so much slower, doesn’t it make sense to try to measure this decline for the purpose of eventual improvement? Given the need for a greener energy infrastructure, this is a matter of the utmost urgency.
Speaking of energy infrastructure, how about a severity index for climate change and associated problems?
There are further noteworthy suggestions at the link. Which indices do you wish for?
From my new paper with Ben Southwood on whether the rate of scientific progress is slowing down:
Third, we shouldn’t expect mismeasured GDP simply from the fact that the internet makes many goods and services cheaper. Spotify provides access to a huge range of music, and very cheaply, such that consumers can listen in a year to albums that would have cost them tens of thousands of dollars in the CD or vinyl eras. Yet this won’t lead to mismeasured GDP. For one thing, the gdp deflator already tries to capture these effects. But even if those efforts are imperfect, consider the broader economic interrelations. To the extent consumers save money on music, they have more to spend or invest elsewhere, and those alternative choices will indeed be captured by GDP. Another alternative (which does not seem to hold for music) is that the lower prices will increase the total amount of money spent on recorded music, which would mean a boost in recorded GDP for the music sector alone. Yet another alternative, more plausible, is that many artists give away their music on Spotify and YouTube to boost the demand for their live performances, and the increase in GDP shows up there. No matter how you slice the cake, cheaper goods and services should not in general lower measured GDP in a way that will generate significant mismeasurement.
Moving to the more formal studies, the Federal Reserve’s David Byrne, with Fed & IMF colleagues, finds a productivity adjustment worth only a few basis points when attempting to account for the gains from cheaper internet age and internet-enabled products. Work by Erik Brynjolfsson and Joo Hee Oh studies the allocation of time, and finds that people are valuing free Internet services at about $106 billion a year. That’s well under one percent of GDP, and it is not nearly large enough to close the measured productivity gap. A study by Nakamura, Samuels, and Soloveichik measures the value of free media on the internet, and concludes it is a small fraction of GDP, for instance 0.005% of measured nominal GDP growth between 1998 and 2012.
Economist Chad Syverson probably has done the most to deflate the idea of major unmeasured productivity gains through internet technologies. For instance, countries with much smaller tech sectors than the United States usually have had comparably sized productivity slowdowns. That suggests the problem is quite general, and not belied by unmeasured productivity gains. Furthermore, and perhaps more importantly, the productivity slowdown is quite large in scale, compared to the size of the tech sector. Using a conservative estimate, the productivity slowdown implies a cumulative loss of $2.7 trillion in GDP since the end of 2004; in other words, output would have been that much higher had the earlier rate of productivity growth been maintained. If unmeasured gains are to make up for that difference, that would have to be very large. For instance, consumer surplus would have to be five times higher in IT-related sectors than elsewhere in the economy, which seems implausibly large.
You can find footnotes and references in the original. Here is my earlier post on the paper.
It is the same material as already released by Facebook, here is our audio and transcript, you will find our transcript easier to read. Self-recommending!
Video, audio, and transcript here, part of Mark’s personal challenge for the year, an excellent event all around. This will also end up as part of CWT.
We provide the first investigation into whether and how much genes explain having health insurance coverage or not and possible mechanisms for genetic variation. Using a twin-design that compares identical and non-identical twins from a national sample of US twins from the National Survey of Midlife Development in the United States, we find that genetic effects explain over 40% of the variation in whether a person has any health coverage versus not, and nearly 50% of the variation in whether individuals younger than 65 have private coverage versus whether they have no coverage at all. Nearly one third of the genetic variation in being uninsured versus having private coverage is explained by employment industry, self-employment status, and income, and together with education, they explain over 40% of the genetic influence. Marital status, number of children, and available measures of health status, risk preferences, and prevention effort do not appear to be important channels for genetic effects. That genes have meaningful effects on the insurance status suggests an important source of heterogeneity in insurance take up.
That is from a paper by George L. Wehby and Dan Shane. Via the excellent Kevin Lewis. We do need to know more, but one possibility is that the adverse selection model of health insurance is much overrated, and advantageous selection into health insurance is a live possibility.
By Ehud Karavani, et.al., possibly an important piece:
The increasing proportion of variance in human complex traits explained by polygenic scores, along with progress in preimplantation genetic diagnosis, suggests the possibility of screening embryos for traits such as height or cognitive ability. However, the expected outcomes of embryo screening are unclear, which undermines discussion of associated ethical concerns. Here, we use theory, simulations, and real data to evaluate the potential gain of embryo screening, defined as the difference in trait value between the top-scoring embryo and the average embryo. The gain increases very slowly with the number of embryos but more rapidly with the variance explained by the score. Given current technology, the average gain due to screening would be ≈2.5 cm for height and ≈2.5 IQ points for cognitive ability. These mean values are accompanied by wide prediction intervals, and indeed, in large nuclear families, the majority of children top-scoring for height are not the tallest.
What are the most common errors that reviewers make when reviewing health papers for you?
There are three errors that reviewers make. First, many junior reviewers write really long reviews to show that they were thorough. This doesn’t help—if the paper has 8 problems then the editor is often most interested in the top two.
Second, some reviewers can also have really high standards in a way that creates lots of Type II errors—never accepting a paper. At the Review of Economics and Statistics, we were writing to accept more papers, but reviewers made this hard by using an impossible standard for identification.
Finally, and this is rare, but a by-product of the “triple-aim” (described above): some reviewers write reports with innuendo and meanness—I never went back to them and still think very poorly of these individuals. To be mean, when protected by the veil of an anonymous review process, is a deep pathology.
My advice is: write short reviews—don’t over referee or rewrite the paper—you are the reviewer, not the author. Be kind. Be kind. Be kind. Kindness is not the same as low standards, but posing questions and raising challenges with curiosity and humility. Always remember that an editor is reading the review, sharing it with other editors, and one’s nastiness is noted and remembered especially when directed towards a new member of the profession.
That is from an interview with Amitabh Chandra of ReStat.
Many years ago I was incredulous when my wife told me she had to format a paper to meet a journal’s guidelines before it was accepted! Who could favor such a dumb policy? In economics, the rule is you make your paper look good but you don’t have to fulfill all the journal’s guidelines until after the paper is accepted.
In The high resource impact of reformatting requirements for scientific papers Jian et al. calculate the cost of reformatting–it’s $1.1 billion dollars annually! True, the authors simply surveyed 203 authors for the time it took to reformat and then multiplied that by an hourly wage and then multiplied that by all article submissions so, at best, this is a back of the envelope calculation. What is beyond doubt, however, is that reformatting typically takes several tedious hours for a high-wage professional.
Our data show that nearly 91% of authors spend greater than four hours and 65% spend over eight hours on reformatting adjustments before publication…Among the time-consuming processes involved are adjusting manuscript structure (e.g. altering abstract formats), changing figure formats, and complying with word counts that vary significantly depending on the journal. Beyond revising the manuscript itself, authors often have to adjust to specific journal and publisher online requirements (such as re-inputting data for all authors’ email, office addresses, and disclosures). Most authors reported spending “a great deal” of time on this reformatting task. Reformatting for these types of requirements reportedly caused three month or more delay in the publication of nearly one fifth of articles and one to three month delays for over a third of articles.
And for what? Most papers will be rejected so the reformatting serves no purpose.
What frustrates me about this inanity is that, as far as I can tell, almost no one benefits! We simple seem stuck in an inefficient equilibrium. What hope is there to deregulate zoning or pass a carbon tax–where benefits exceed costs but you can understand why the process is difficult because some people gain from the inefficiency–when we can’t even fix wasteful journal formatting policy? Can Elsevier or other publishing heavyweight not unilaterally move us to the Pareto frontier! Pick up those $1.1 billion bills! Come on humanity, just do it!
Addendum: Economics is good on the reformatting score but n.b. “A prior survey-based research study on biomedical journal publications times noted a median time of first submission to acceptance of five months but this seemingly included all delays in the publication process (including review time and changes to improving scientific content).” Five months would be unheard of speed in economics where you are lucky if you get referee comments in five months!
Due to a special grant, there has been a devoted tranche of Emergent Ventures to individuals, typically scholars and public intellectuals, studying the nature and causes of progress.
Here are the winners of those awards so far:
Adam Green, budding public intellectual, to study the pre-implantation genetic testing of embryos.
Ville Vesterinen, Finland, to produce podcasts and YouTube videos on the nature of progress and economic growth.
Leopold Aschenbrenner, 17 year old economics prodigy, to spend the next summer in the Bay Area and for general career development. Here is his paper on existential risk.
Byrne Hobart, to write a book on technological progress with Tobias Huber.
I’ll be announcing more winners soon, from the regular rather than the progress studies tranche of Emergent Ventures (both remain open).
A blog post by an artificial intelligence that has just been taught to write about an artificial intelligence is still far from being a good blog post by any human being.
There are many reasons why a blog post by an artificial intelligence is unlikely to be a good blog post by any human being.
First, the post is still a huge piece of written material, so it will be a big task for the AI to read it all. This is similar to reading a huge, long book, which is a huge task for you as an author.
Second, it is likely that the AI will use its knowledge about writing to create a very bad, misleading, or otherwise nonsensical blog post. In this case, the AI will be writing a blog post about its own stupidity.
Third, even if the blog post is not written by a computer but rather by a human author, the human author will not understand it and will not be able to correct it. This is because the blog post will include very basic, incorrect, or outdated knowledge about writing,
A recent study of 180 academic curricula vitae found that 56 percent that claimed to have at least one publication contained at least one unverifiable or inaccurate publication, and it suggests that CV falsification could be much more common than scholars committed to professional integrity might hope. The study is small — the 56 percent reflects only 79 CVs, of 141 that claimed to have at least one publication. The researchers behind the study make no presumption as to whether the errors were intentional.