What Remains Of Eugenics: The Legacy Of “Improving” The Human Breeding Stock

Guest post by Amanda N. Weiss

People have long used selective breeding of crops and livestock to produce food and work animals with traits suited for our purposes. But ideas about good breeding stock have also seeped into the conversation about human traits throughout history, crossing the boundaries of morality and science.

The eugenics movement through the 1900s

In 1883, Sir Francis Galton first used the term “eugenics” to describe the idea of cultivating desirable traits within the human population by “improving stock”. There are two arms of eugenics: positive eugenics, which seeks to promote reproduction of the “right” people, and negative eugenics, which seeks to prevent “wrong” or “defective” people from reproducing. In the minds of eugenicists, people of color were among those cast as undesirable due to fears about race-mixing and “race suicide”.  Another major target of the eugenics movement was mentally disabled people, referred to as “feeble-minded”, who were portrayed as diseased, animalistic, and destructive. 

Some eugenicists appealed to the theories and work of scientists to support their claims. Gregor Mendel’s work cross breeding pea plants in the 1800s had demonstrated laws of genetic inheritance for certain traits, and some eugenicists incorrectly applied these laws to complex traits such as intelligence or criminality, which we now know result from a combination of many factors. Additionally, some eugenicists, including Caleb William Saleeby, focused on protecting in utero fetuses of the “right” kind of parents from exposure to toxins such as alcohol. 

Many disciplines were part of the eugenics movement, and the medical field helped to legitimize eugenics as a science in the public eye by subsuming it under the banner of its expertise. Researchers in the United States also played a major role in the eugenics movement. In the early 1900s, at the request of zoologist Charles B. Davenport, the Carnegie Institution started a research station to study evolution, including “race change”, at Cold Spring Harbor Laboratory in New York. Less than a decade later, the Eugenics Record Office was established there. This office collected information about physical and mental traits, traced through family lines. Some leaders of this office, such as educator Harry Laughlin, advocated for eugenic government policies, including restricting immigration of “antisocial, and otherwise undesirable, persons” and sterilizing “unfit” people to prevent them from reproducing. Another entity associated with the Eugenics Record Office was the Eugenic Section of the American Breeders Association, which had research committees dedicated to areas including “Heredity of Feeble-mindedness”, “Heredity of Criminality”, immigration, and sterilization. 

However, not all scientists were on board. From early on in the eugenics movement, some scientists rejected the oversimplification of gene-trait relationships and the flawed methodologies of many eugenics research studies; even some eugenicists were critical of the quality of eugenics research. One prominent opponent of eugenics was Lionel Penrose, an English scientist whose research focused on understanding mental disorders. He helped discover that environmental factors and genetics both contribute to mental disabilities. Penrose also changed the title of the journal Annals of Eugenics to Annals of Human Genetics in 1954. Likewise, in the 1960s, following Penrose’s suggestion, University College London changed the name of its Department of Eugenics, Biometry, and Genetics to the Department of Human Genetics and Biometry. 

Despite the opposition, eugenic ideals found their way into public practice, including public health. While some eugenicists believed that stopping the spread of germ-based diseases was interfering with natural elimination of the “unfit”, others believed that public health measures helped to preserve the “fit” along with the “unfit”. Both public health and eugenics sought to rid society of what they considered to be illnesses that threatened the well-being of the population.

Options mentioned for eliminating unwanted traits included euthanasia, as mentioned in a report by the Eugenic Section of the American Breeders’ Association, and execution, as discussed in the 1918 textbook Applied Eugenics. However, both documents focus more on sterilization of the “unfit”, and Applied Eugenics states that execution has “value in keeping up the standard of the race”, but “is wholly out of accord with the spirit of the times, and is not seriously considered by the eugenics movement”.

Indeed, sterilization became a major fixture in the United States’ eugenics policy. Over 30 states in the U.S. ended up with sterilization laws on the books, and the 1927 Supreme Court case Buck v. Bell upheld a state’s right to forcibly sterilize people. Notably, Carrie Buck, the young woman whose sterilization was the subject of this case, had been committed to a mental institution after giving birth from a pregnancy from rape by her foster mother’s nephew at age 17. Altogether, these laws and practices resulted in at least 60,000 sterilizations of people in institutions before World War II. Later, in the 1970s, there was also a huge wave of sterilizations of Native American women, including many procedures performed without consent. 

The eugenics programs in the United States were also influential beyond the country’s own borders. Nazi Germany took inspiration from U.S. immigration policy and sterilization laws for its own “racial hygiene” program, and U.S. eugenicists likewise supported Nazi eugenic policies.

After WWII, in 1953, the double helical structure of DNA was discovered by James Watson, Francis Crick, and Rosalind Franklin (though Franklin was not appropriately credited at the time). Despite being involved in a critical step forward for genetics, Watson later came to be known for his racist and sexist remarks that echoed eugenic thought. Watson believed intelligence was based on genetics, such that African American people were inherently less intelligent, and that the gender disparity in science may be due to women being inferior by nature. In his later years, Watson was largely shunned by the scientific community, and several articles have been written about his controversial legacy in the wake of his recent death.

Modern-day influences of eugenics

These days, it’s generally accepted that this traditional form of eugenics reflects prejudice rather than actual science. So, why is it so important for us to keep eugenics in discussion, especially within the scientific community? Ultimately, eugenics underlies the origins of some current practices and continues to haunt legal policy.

Several pieces of healthcare that we benefit from today grew from eugenic roots. For instance, Margaret Sanger, the nurse who drove forward the push for birth control education and access in the United States, allied herself with eugenicists and wrote about birth control as a tool for preventing the breeding of people considered “unfit”. Additionally, the profession of Marriage and Family Therapy was originally closely associated with eugenic ideas about promoting the “right” kind of marriages to improve future generations. 

Eugenic lines of thought have also persisted in some modern genetics research, even though most contemporary scientists would reject the idea of eugenics overall. In the first decade of the 2000s, a small sample genomic study found that a specific version of the “warrior gene” occurs frequently in the Mãori people of New Zealand, and one of the researchers made a controversial claim about the gene and predisposition for criminal behavior. The Mãori population is stereotyped as aggressive and discussion around the “warrior gene” does not always emphasize the importance of environmental effects on traits associated with this gene. Additionally, this version of the gene also appears with high frequency in several other populations. In the present day, as gene editing technologies improve, we are also faced with questions about the morality of editing genes to enhance certain traits. Some are in favor of “liberal eugenics”, wherein parents can use reproductive technology to select for desired traits in their own children. However, the line defining traits that are pathological, or reasonable to select for or against, is not always clear.

Furthermore, eugenic threats continue to loom over people with disabilities. A study published in 2022 found that 31 U.S. states and Washington, D.C. still had laws on the books that allowed for disabled people to be forcibly sterilized. Less directly, policies regulating disability benefits may affect a disabled person’s ability to have enough financial security to have and raise children. For instance, in order for a non-blind disabled person to receive Social Security disability benefits in 2025, they must not earn more than $1,620 per month. Additionally, Supplemental Security Income, which some people qualify for based on a disability, may be affected if the recipient gets married. Likewise, if a “disabled adult child” (an adult whose disability started before they turned 22 and whose parent(s) receive or were insured for Social Security benefits) gets married to someone who is not a “disabled adult child”, they will lose their Social Security Disability Insurance. If a disabled person relies on their benefits to stay alive and well, marriage may not be an option; through financial requirements, laws indirectly restrict the choice of people with disabilities to have children.

Notably, disability rights advocates and civil rights organizations expressed concern when the National Institutes of Health Director Jay Bhattacharya proposed a plan for an autism registry earlier this year. Though the Department of Health and Human Services stated soon after that the data platform would not be a registry, concerns remained about lack of communication with advocates and data privacy. There was also no guarantee that the data would be used to help people with autism rather than hurt them. 

In addition to disability-related concerns, some of the anti-immigration rhetoric prevalent in more recent times echoes the sentiment of early 1900s eugenics. According to NPR/Ipsos polling, the percentage of respondents who falsely believed that immigrants are more likely than U.S.-born people to be criminals or be in prison increased between 2018 and 2022. During President Trump’s campaigning in 2020 and 2023, he described his supporters in Minnesota as having “good genes” and undocumented immigrants as “poisoning the blood”. However, Gallup polling shows that in 2025, compared to last year, fewer Americans want lower immigration rates, and most Americans think positively of immigration, suggesting that anti-immigration sentiment may be falling out of favor.

Nevertheless, extremist views about racial inferiority of non-white people have inspired violence in recent years. Several mass shootings within the past decade have been associated with  “Great Replacement” theory, which posits that the white populations in the U.S. and Europe are being replaced by immigrant people of color. This conspiracy theory originally gained prominence in the United States after eugenicist and environmental conservationist Madison Grant published his book, The Passing of the Great Race, in 1916. 

Likewise, recent years have seen the rise of pronatalism, the promotion of reproduction, in the United States. Pronatalism arises from a fear about a decline in the population, and some pronatalists believe that some specific populations should reproduce more. At the first Natal Conference in 2023, speakers alluded to the need for “better” people to proliferate.

Conflict about “good genes” and white supremacy has also found its way into more mundane parts of the public sphere. A few months ago, American Eagle released an advertisement campaign with the tagline “Sydney Sweeney Has Great Jeans”, in which one of the ads features Sweeney saying that inherited genes determine traits, including the complex trait of personality. The campaign elicited backlash online from critics who felt that this ad coming from the mouth of a white, blue-eyed woman had eugenic undertones, but others felt that the backlash was “woke” and an overreaction. When asked in an interview about it, Sweeney replied that “[…] when I have an issue that I want to speak about, people will hear”, which sparked further backlash. Sweeney later said “I don’t support the views some people chose to connect to the campaign” and “I have come to realize that my silence regarding this issue has only widened the divide, not closed it.”

Mitigating eugenic thought through education

Given the persistence of eugenic influence in society, researchers have begun exploring genetics education strategies to minimize false beliefs about biology that align with eugenics. Namely, “humane genomics” education appears to reduce belief in genetic essentialism, which is the incorrect idea that genes determine traits in a fixed way and that race is a genetic categorization. Additionally, the Next Generation Science Standards include, at multiple grade levels, the concept that the environment affects traits. 

Critical evaluation of genetics education and the history of eugenics also arose in a 2024-2025 exchange between prominent U.K. geneticists in the pages of the American Journal of Human Genetics. Adam Rutherford, a lecturer, former editor at Nature, and science communicator, argued that genetics education focuses too much on a simple gene-determines-trait model, and that teaching single-gene inheritance as introductory genetics leads students to develop incorrect understandings of the relationship between genes and traits. Meanwhile, Walter F. Bodmer, Head of the Cancer and Immunogenetics Laboratory at the Weatherall Institute of Molecular Medicine, and Brian Charlesworth, Professor in the Institute of Ecology and Evolution at the University of Edinburgh, argued that even during the height of the eugenics movement, some scientists criticized eugenics, and that single gene major inheritance does occur for many traits and thus serves as a reasonable introductory model. This discourse highlights the importance of considering how teaching methods for basic scientific concepts have implications beyond the classroom.

So, where does that leave us now, and what can we take away from the history of eugenics and its lingering impacts on society? We can’t look at the eugenic atrocities of the past that were rationalized with pseudoscience or misapplication of scientific ideas and think that they can’t happen here and now. Active work is needed to reduce the prevalence of genetic misconceptions associated with eugenic beliefs. Developments in teaching strategies hold promise for addressing this problem in upcoming generations, but scrutinizing broader aspects of society is necessary to understand and undermine the deeply entrenched eugenic roots that remain.

For The People: The Value of Inclusivity in Scientific Research

by Clara Malekshahi

The world of scientific research is notoriously exclusionary. The use of jargon in papers, in presentations, even in interviews, impairs the public’s understanding of science and their ability to engage with research. The high barrier for participation in science breeds widespread exclusion of underrepresented minorities: twice as many underrepresented minority students switch out of a scientific field of study than students of white or Asian descent. Exclusion in scientific research is especially concerning in biology and biomedical fields, which so directly impact peoples’ lives. In particular, those in biochemistry, pharmacy, biology, and other biological fields are more likely to have graduate degrees than in almost any other field, restricting access to this knowledge to those with advanced degrees. All of this hinders engagement with the larger, non-scientific community. And yet, science is meant to serve the community at large. Such a vast disconnect makes it easier to spread misinformation and reduce the credibility of scientists. 

But like a living organism, science moves and grows. Scientists are increasingly involving the public in many ways. Public effort is being used for data collection and project development. There are more and more programs in place to engage younger students — down to the high school level —in scientific discovery and learning. The non-scientific community is being included in scientific decisions and policy. There are also newer ways in which individual scientists are reaching out to the public, such as TikTok. 

Publically-powered Databases

One way in which science is beginning to involve the public is through large databases. SmokeSense is run by the Environmental Protection Agency (EPA), and asks users to monitor their experiences with smoke and wildfires. In turn, the EPA disseminates vital information about air quality in an easily accessible app. Participants have been excited about the possibility of increased communication with organizers and responders in the event of a wildfire. It also means that individuals across multiple local communities have more power, and groups particularly sensitive to wildlife smoke may benefit from more targeted protections. 

Another database widely dependent on public input is eBird. Developed and maintained by the Cornell Ornithology Lab, eBird is a bird-watching database. Birders from around the globe upload their sightings in the form of checklists, and the eBird team turns this into interactive maps of the seasonal abundance and movement trends over time of nearly 3,000 bird species. Data from eBird has been used to study everything from the response of birds to climate change to the role of birds in disease prevalence and spread. All of this publicly-sourced data encourages involvement and interest from a wider audience.

Community Involvement in Research and Publishing

There are even more ways in which scientists are working closely with the public to carry out science. Perelman School of Medicine’s Dr. Eugenia South works on community science that directly benefits the people involved. Her research explores the effects of environmental interventions on the mental and physical wellbeing of neighborhoods in Philadelphia, especially minority neighborhoods. As part of her commitment to scientific inclusion, Dr. South works with several community coordinators on her team. With the help of the community, Dr. South has shown that interventions intended to turn vacant lots into green spaces decreased self-reported poor mental health and depression. In the 117 areas that received new planting in addition to trash removal and regular maintenance, there was a roughly 50% drop in depression among respondents. In neighborhoods below the poverty line that received new green spaces, that drop in depression was even higher. Her work also connects communities with urban designers to create more green spaces. Through the Deeply Rooted community collaborative, Dr. South’s team even encourages grant applications by the public. The creation of these new connections is vital to increase public engagement with scientists: there is a tangible, rapid effort towards change. 

This trend of public involvement extends to younger generations. Scientists are actively involving members of the public in peer-reviewed research, starting as early as high school. The Journal of Student Research, for example, specifically targets young potential scientists. In the past 13 years, they’ve published over 2,000 articles with a specific target towards high schoolers from all over the world. And what are these high schoolers writing about? Everything, from machine learning, to multi-omics of molecular pathways, to explorations into the impacts of climate change. The journal is supported by a vast editorial team of PhD-level scientists, each of whom engages with these students as fully fledged scientists. 

The Journal of Student Research is not the only avenue for high schoolers to engage in scientific publications. In early 2022, the Microbiology Spectrum journal published a paper on avian paramyxovirus in New York City. To learn as much as possible about the prevalence of disease in pigeons in New York City, this Mount Sinai-based team assembled a crew of community scientists: The New York City Virus Hunters. Five of these virus hunters, who became co-authors on the paper after sampling and analyzing the data, were high school students. The engagement with the New York City Virus Hunters and the high schoolers are huge steps towards actively including the community in scientific activities.

Social Media - the Bad and the Good

Part of the increased engagement of the public with science comes from increased accessibility of science on social media. In many ways, this is incredibly detrimental for both the public and scientists. Influencers and communicators on social media are often paid to disseminate information by industries that may want to increase sales, such as the tobacco industry. Unfortunately, misinformation and false news are often more viewed and spread than the truth. This misinformation is incredibly confusing and can erode trust in science and scientists. Although 54% of Americans get science news from general news outlets, only 28% of American adults believe that these news outlets get their facts right. This suggests a significant amount of skepticism amongst U.S. adults. 

Despite these flaws, social media is nonetheless partially responsible for increased accessibility, and is sometimes even helpful. There are scientist influencers all over the Tik Tok, as well as doctors and public health officials helping to disseminate key information. These science influencers have followers that might number in the tens of thousands, and they are increasing the scientific understanding of the general public everyday. Additionally, social media giants have successfully partnered with health agencies like the WHO to disseminate information about COVID-19 vaccinations. 

For too long, the subjects of scientific research—the general public—have not been included in active research. Citizen science databases are a great first step. But getting communities involved in making beneficial changes, and actively taking steps toward such changes, as Dr. South has done, is even better. That, finally, is the goal: to work together with the communities we serve to improve quality of life. And there is no lower limit to when to start engaging the public; high schoolers are increasingly getting involved in publishing research. All of this signals a change in the accessibility of science, and strengthens trust in scientists at a time when each new morsel of misinformation erodes credibility.

Who’s Afraid of the Big Bad AI?

by Katherine Huang

Earlier this year, during an icy spell in Boston, I attended my first American Association for the Advancement of Science meeting. On the last day of the meeting, I ran into a fellow attendee on my way back to the venue from getting lunch. We were both mildly lost, but once we found the right direction and could walk in a more relaxed manner, she remarked to me that there was a surge in the number of Artificial Intelligence (AI)-related panel sessions at the meeting this year, compared to last year. 

While an annual meeting that draws some four to five thousand attendees is only a small sample of the population, this surge reflects a broader rise in the presence of AI in our work and personal lives. It is a change that has sparked mixed reactions: according to a recent Pew Research Center study, 51% of adults in the US are more concerned than excited about increased AI usage in daily life. 

So, how wary should we be of AI technologies? And to what extent does our wariness come from media hype, as opposed to concrete problems that AI has already presented us with? 

Sensationalism in the Media

News outlets frequently dramatize their content to appeal to our emotions and curiosity, thereby boosting their readership. When it comes to AI, headlines like, “Will AI take over the world and all our jobs?” immediately grab our attention and can make us fearful and antagonistic towards AI. 

In the pursuit of sensationalist, science fiction-inspired news stories about AI, journalists can become biased in the kinds of information they want from experts. Brian K. Smith, a professor of computer science and education at Boston College, recalls a reporter asking him about the possibility of an AI apocalypse in which robots rise up against humans, à la Terminator or RoboCop. Smith challenged the reporter: “You’ve given me science fiction examples. Is there any instance so far in the history of humanity where machines have tried to rise up and destroy the human race?” The reporter ended the interview right there. 

“People are preoccupied with this one sort of model where AI is imagined as a person that will replace people,” said Ted Underwood, a professor of English and information sciences at the University of Illinois Urbana-Champaign. “Stories like that get a lot of attention.”

AI experts themselves can also be guilty of over-hyping their work. In 2016, “godfather of AI” and 2024 Physics Nobel laureate Geoffrey Hinton suggested that we should no longer train radiologists because AI would outperform humans in that field within the next five years, or “it might be ten.” Since that prediction, the radiology staff at the Mayo Clinic has increased by 55% to include more than 400 radiologists. Across the US, the American Board of Medical Specialties reported that there were 59,525 board-certified radiologists in June of 2019. By June of 2024, that number had risen to 66,962. 

MIT labor economist David Autor opined that predicting AI will steal jobs from humans underestimates how complex human jobs actually are. Indeed, AI did not replace radiologists. Rather, it has helped radiologists work better, such as by identifying images that likely show an abnormality so they know where to look first, or by taking accurate, reproducible measurements that are variable when done by hand. Human radiologists are then still needed to interpret the images and numbers, make conclusions, and communicate their findings to patients and other physicians. Admirably, Hinton has acknowledged that he overstated AI’s rendering of human radiologists obsolete, updating his view to be that AI will make them more efficient. 

“I would like to see [media] coverage that spends more time thinking about how people can expand or augment what they’re doing, rather than the people versus robots script,” said Underwood. He highlighted that we need greater technology literacy for using AI tools in the workforce, as currently, “Most people don’t have the understanding to take full advantage of them, even if they technically have access.” 

In fact, many may only understand a small slice of what constitutes AI. “At this moment in time, when people say ‘AI,’ they almost always mean ChatGPT,” said Smith. 

ChatGPT, Generative AI, and the Humanities

Since entering the chat—pun very intended—in late 2022, ChatGPT has essentially become the face of AI. Yet, AI research began much earlier in the 1950s and its results have long been in our everyday lives, from Siri to email filters and even Roombas. 

Smith credits the equivalence of ChatGPT with all of AI to the branding genius of OpenAI, the company behind ChatGPT. “OpenAI did such a phenomenal job by putting a simple interface on top of their language model and calling it ‘ChatGPT,’” he said. “It became so public-facing [while] the rest of what we talk about as AI is always buried.” 

AI is an umbrella term for computer-based technologies that perform tasks by trying to mimic human thought. ChatGPT is an application of generative AI, a newer type of AI that learns patterns from data and imitates them to produce text, images, video, code, and other content based on inputs or prompts. In contrast, traditional AI involves systems that mainly analyze and predict, rather than produce content. 

If we restrict our impressions of AI to ChatGPT and other generative AI tools, it is easy to see why AI has received such backlash in recent years. Students have used ChatGPT to write essays that were assigned to teach critical thinking and communication skills, defeating the purpose of those assignments. People can rapidly spread mis- and dis-information with AI-generated content, and while some instances might be light mischief, others can seriously harm reputations and hinder emergency responses. Additionally, scammers have used AI to write text for phishing emails and advertise fake products, cheating people out of time and money.  

While many can denounce such misuses of generative AI, a squishier issue involves using generative AI to produce art and writing for private enjoyment. Certainly, training AI systems on copyrighted material without their creators’ permission evokes ethical concerns. So does taking business away from human artists and writers when the creative industry already tends not to pay well. But what if someone who cannot afford to commission a human artist just wants to Studio Ghibli-fy a family photo with ChatGPT to put on their desk? 

If the technology is available, it is hard to persuade people not to use AI-generated work because they “should” help human creatives make a living, or because the output does not have enough human labor behind it to be “real” art. The focus then turns to what companies might do to restrict access to these technologies, or to pay creatives for using their intellectual property to train generative AI. 

Underwood, whose research involves text-mining digital libraries, has thought about fair use for a long time. He believes training AI models with copyrighted material should be fair use as long as the material is not too new—say, created within the last five years—and as long as the models are only extracting patterns from it as opposed to reproducing it, which is legally prohibited. He offers this perspective on the flip side: 

“Suppose we rule that training is not fair use. You have to pay publishers for their archives in order to train on them. The big tech companies are perfectly capable of doing that; they absolutely could buy up publishers’ back catalogs. If we determine that the right to train is something that can be bought and sold, we’d now have big intellectual monopolies where every professional in the country has to pay $500 a month to [a company] to have their [AI] model, which is the exclusive model. That’s much more terrifying to me than the idea that these models are going to compete with creatives. If we have [AI technology] open, creatives can figure out how to use it themselves and still have a job. If it’s a market that can be cornered, there is potential for concentration of power and increase of economic inequality. To me, the intellectual property argument against AI has a high chance of backfiring on those important pieces.”

Though the concept of AI versus humans looms large, generative AI competition against human creatives is not the only possible relationship between AI and the humanities. Memo Akten, Sougwen Chung, and other acclaimed artists have already integrated AI into their work. Underwood sees promise in using large language models (LLMs) to computationally analyze vast amounts of text for drawing conclusions about literary history, a field of research known as distant reading. In the past, he has done this with statistical models to study trends like gender stereotypes across two centuries of written fiction. More recently, he has shown an example of how an LLM-based AI model can not only be more accurate than a statistical one in estimating time passage in fiction—another trend he has studied before—but can also be more interpretable in documenting its “thought process” verbally. 

“I don’t see us just not doing the technology,” Underwood said. During a presentation at the Wolf Humanities Center at Penn, he recommended that humanities scholars ought to work with AI developers to help their models represent a more diverse array of cultural perspectives. In computer science terms, this is called pluralistic alignment. Fortunately, Underwood notes, computer scientists recognize this need for humanities input and better cultural representation. 

Panic, Policy, and Regulation 

Patrick Grady and Daniel Castro from the Center for Data Innovation describe the phenomenon of the “tech panic cycle” as a bell curve of public concern. In this curve, Trusting Beginnings in a new technology are followed by a Rising Panic that eventually reaches a Height of Hysteria. This hysteria then subsides in a Deflating Fears stage, and finally we reach a Point of Practicality in Moving On from the panic. 

In April of 2023, Grady and Castro placed the hysteria surrounding generative AI, due to the introduction of ChatGPT, in the Rising Panic segment of the curve (Figure 1). Now, two years later, Smith believes we are still in the same vicinity. He noted that in practice, the curve is not smooth, but rather contains many oscillations, and different sectors of people—educators, creatives, etc.—can be at different points along the curve. Overall, though, “I don’t think we’ve come as far as Practicality [yet],” Smith said. “It’s a moving target.” Printed books, recorded sound, and motion pictures all went through tech panic cycles, suggesting that at some point, we will reach an overall Point of Practicality with generative AI—and AI broadly—as well. 

Figure 1. The tech panic cycle. Grady and Castro place generative AI in its Rising Panic phase as of April 2023. Taken from https://datainnovation.org/2023/05/tech-panics-generative-ai-and-regulatory-caution/.

This is not to say that we should just sit back and let the curve happen; we can still make more careful choices about how generative AI tools are developed and implemented. The extent to which this should involve regulation at the policy level is a thornier issue. Some, such as Grady and Castro, think that regulating generative AI will only stifle innovation, even with the flexibility that guidelines like the EU AI Act offers. 

“I think there is room to say, ‘we want to be responsible,’” Smith believes, citing policy development surrounding nuclear energy and biotechnology. “I think you could do that without saying, ‘and then there will be no more innovation.’ There have to be some kind of rules or constraints that govern the decision-making around those things, and also conversations with developers where they’re in communities aligned with people who could help them see, ‘there’s a potential problem here.’” 

He understands Grady and Castro’s point, though: “A law is built on precedent, so it’s all going to be [hypothetical] until some specific cases come up.”

Our Future With AI

Many grievances against AI are not intrinsically technology issues, but social issues that AI forces us to reckon with. The panic people feel at the idea of losing their jobs to AI’s cheaper labor is a capitalism problem. Cheating in school did not start because of ChatGPT. Academic integrity, which Smith likes to say is the “AI” we are actually concerned about, is more about how teachers are teaching and assigning work, and what kinds of values students are learning from the people around them. Data centers that house AI machinery guzzle enormous amounts of water and electricity. This is an environmental issue that younger generations, trending more eco-conscious, may want to weigh against cheating in school with ChatGPT and other LLM-based tools. Misinformation is not a new phenomenon, either. AI has just made it easier and faster to produce, which means the general public needs more digital media literacy alongside improvements in watermarking AI-generated content. 

There is no promise, as Underwood noted in his talk at Penn, that the good AI can do will outweigh its consequences. AI’s many associated problems, beyond what I have covered here, are very real and should not be dismissed in the name of technological progress. But, some aspects of AI are perhaps not as awful as their reputation to date, and we can still be optimistic about our ability to build and harness AI systems for good. 

When I asked Underwood if there was anything he particularly looked forward to AI being able to do, he said, “I would love to see [AI models] be able to tell a suspenseful story. Right now they don’t understand suspense.”

Will generative AI ever get there?

I guess we will find out. 

Philly Spotlight: Philly AIDS Thrift @ Giovanni's Room

By Kay Labella

For Pride Month, the PSPDG blog is collaborating with students from LTBGS and Lambda Grads for a series of posts highlighting the LGBTQ+ community and related matters at Penn and beyond.

Looking for your new favorite bookstore, ideally one with a rich and storied history? Well, look no further than Philly AIDS Thrift @ Giovanni's Room. The oldest queer bookstore in the United States still in operation, Giovanni’s Room was founded in 1973 by Tom Wilson Weinberg, Dan Sherbo and Bern Boyle. These three members of the Gay Activist Alliance (GAA) created a space that was part bookshop and part community center for both locals and visitors alike. The store has changed hands and locations several times over the years, including its purchase in 2018 by Philly AIDS Thrift, but the mission remains the same: be the number one source for new and lightly used LGBTQ fiction & non-fiction, comics, music, artwork, and more. Giovanni’s Room offers over 7,000 titles on the shelves as well as their database of more than 48,000 titles, a veritable treasure trove of queer literature.