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AI and the Ugly Environmental Footprint it Leaves Behind
Full text linkOn November 30th, 2022, OpenAI launched ChatGPT, the seemingly magical AI chatbot that can do anything from writing Shakespearean-style poetry and catchy song lyrics to teaching you to solve an iterated integral. In the weeks and months after its launch, ChatGPT experienced a meteoric rise. With this came an onslaught of criticisms about its implications, from concerns about its tendency to “hallucinate”—generating confident-sounding but completely false statements—to worries about its algorithmic bias, reflecting the entrenched inequities in its training data, to predictions that plagiarism would proliferate in academia (Hao, “What is ChatGPT”). ChatGPT represents a tipping point, testing the boundaries between human and artificial intelligence.
Meanwhile, humanity has brought the planet to the edge of another tipping point. Over the past several years, climate change has quickly morphed from a problem of the distant future into one that is immediate. As the world reels from increasingly destructive catastrophes, from historic storms in California to extreme droughts in East Africa, there is a flurry of conversation, international agreements, and pledges from corporations to make amends.
Amid the urgent search for better solutions, AI is seen as a key component in combatting climate change. In the same month that ChatGPT was released, the United Nations Environment Program (UNEP) published an article explaining the role of AI in tackling climate challenges. According to David Jensen, coordinator of the UNEP Digital Transformation team, AI has the potential to revolutionize a multitude of climate-related efforts, from planetary-scale “satellite monitoring of global emissions” to granular-level energy-efficient smart homes. For example, the World Environment Situation Room online platform launched by UNEP leverages AI to analyze “complex, multifaceted datasets” tracking atmospheric carbon dioxide concentrations, sea levels, and glacier mass in order to help policymakers make data-driven decisions (“How Artificial Intelligence”). As climate change is an immensely complex problem with an enormous number of shifting variables, AI is uniquely suited to streamline the process of collecting and analyzing climate datasets to build predictive models and provide invaluable insights into policymaking and mitigation strategies.
But there’s a catch. Even though artificial intelligence may be pivotal in our fight against climate change, the technology itself is a massive culprit in accelerating the crisis that is humanity’s greatest existential threat.
Lost in the flurry of excitement about the future potential of AI is the ugly reality of its immense environmental costs. As highlighted in the title of a recent Bloomberg article from March 2023, “Artificial Intelligence is Booming—So is Its Carbon Footprint.” Authors Josh Saul and Dina Bass explain that, due to the massive amounts of data used in the training process, and the fundamental structure of deep learning models, AI requires orders of magnitude more energy consumption than traditional forms of computing. However, the exact energy usage and carbon emissions of most AI models remain a mystery due to the lack of transparency from the large technology companies developing them.
Out of curiosity, I turned to ChatGPT itself and asked, “What is your carbon footprint?” But, like a seasoned politician trained to weasel its way out of a thorny topic, ChatGPT gave a cautious reply coated in ambiguity: “As an artificial intelligence language model, I do not have a physical form and therefore do not have a direct carbon footprint. However, the computers and servers that power my operation do require energy, which may contribute to carbon emissions” (“What”).
In the absence of any openly reported emissions data from OpenAI, independent researchers in academia and non-profit organizations have attempted to calculate the carbon footprint of GPT and other large AI models—and the results are terrifying. The earliest work in this area stems from a seminal 2019 paper by Strubell, Ganesh, and McCallem from the University of Massachusetts, Amherst. Presenting their study at the Association of Computational Linguistics conference that year, they stunned the computer science community by revealing that the process of training the BERT transformer model—a type of large AI model with over 200 million parameters—emits more than 626,000 pounds of carbon dioxide (Strubell et al. 3645). This is equivalent to “nearly five times the lifetime emissions of the average American car. . . [including] manufacture of the car itself” (Hao, “Training”). Since then, the size of large AI language models has grown exponentially, alongside their energy usage and carbon emissions. In 2021, OpenAI released GPT-3. With 175 billion parameters (Heaven), this large generative language model is nearly 100 times larger than BERT from two years prior. Researchers from the AI startup Hugging Face estimated the training emits more than 500 metric tons (over one million pounds) of carbon dioxide (Luccioni et al. 7), which is around 610 one-way direct flights from New York to Paris. By the release of GPT-4 in March 2023, the parameter count ballooned to a purported 170 trillion, representing another hundred-fold increase from their previous model two years prior (Zaveria). Assuming the computational costs required scale proportionally, this entails that a single training process of the new model emits the equivalent of 61,000 transatlantic flights.
Perhaps the most terrifying thought is that this is only a fraction of the computational resources used in the entire lifecycle of developing a model. As Strubell explains, “Training a single model is the minimum amount of work you can do” (qtd. in Hao, “Training”). In practice, the full development pipeline involves many, many more rounds of training and fine-tuning. Strubell and her colleagues estimate that building and testing a “final paper-worthy model required training 4,789 models over a six-month period” (qtd. in Hao, “Training”). In the case of a commercialized product like ChatGPT, model training was just the first step. In the two months following its launch, it amassed over 100 million unique users at an unprecedented rate of growth (Milmo). Generating responses to hundreds of millions of prompts requires even more computational power. It is almost beyond comprehension just how much power GPT consumes.
As it becomes increasingly clear that AI has a monstrous appetite for energy, OpenAI is desperately trying to hide this ugly truth by keeping silent on the issue. An article by MIT Tech Review shortly after the launch of GPT-4 in March 2023 calls the new model “the most secretive release the company has ever put out, marking its full transition from nonprofit research lab to for-profit tech firm” (Heaven). When asked just how large the model is, the company’s chief scientist, Ilya Sutskever, claimed that it is something he “can’t really comment on at this time” because “[i]t’s pretty competitive out there” (Heaven).
The field of AI research is split into two major theories regarding artificial general intelligence (AGI): one is that AGI can be achieved by simply scaling existing models, and the other is that the current approach, through deep learning, is fundamentally insufficient. OpenAI has doggedly pursued the former path in its quest for AGI. As evident through the exponentially growing size of its GPT language models, most of the breakthroughs by OpenAI “have been the product of sinking dramatically greater computational resources into technical innovations developed in other labs.” For the leadership team, this computation-driven strategy is their “primary competitive advantage” over other research labs (Hao, “Messy, Secretive Reality”). Caught in a race with other giant tech companies such as Google and Meta to build the best AI model, the company has grown increasingly guarded about its research process and increasingly closed off to the public. Ironically, OpenAI is no longer so open.
It wasn’t always this way. Originally founded by Sam Altman as a non-profit research organization in 2015, OpenAI had the goal of democratizing AI. Their core charter states, “Our primary fiduciary duty is to humanity.” But the document later reveals, “We anticipate needing to marshal substantial resources to fulfill our mission,” and “we expect that safety and security concerns will reduce our traditional publishing in the future” (“OpenAI Charter”). This language already insinuates their shift in priorities. In 2019, when OpenAI initially announced the $1-billion investment from Microsoft and the transition to a capped for-profit model, the leadership team claimed that “any commercialization efforts would be far away.” But, in an internal meeting just months later, Altman’s bottom line was clear: “OpenAI needs to make money in order to do research—not the other way around” (Hao, “Messy, Secretive Reality”). In February 2023, OpenAI began charging users twenty dollars per month for premium subscriptions that offered faster results through ChatGPT and API access to GPT-4. But, to satisfy its insatiable hunger for computational resources, OpenAI also agreed to give up almost half of its profits to Microsoft in exchange for access to the company’s Azure cloud computing network. Clement Delangue, the CEO of Hugging Face, which develops open-source AI language models, fears this type of investment is leading to “cloud money laundering” (Bass). The easy access to computing resources crushes incentives to develop more efficient, environmentally friendly solutions. The trend toward larger and larger models creates “unsustainable use cases for machine learning” that threatens both the development of AI and the future of the planet (Bass).
But it does not have to be this way. As outlined by a correspondence in Nature from March 2023, the carbon emissions of large AI models can be drastically reduced by “tailoring the structure of the model and by promoting energy-efficient hardware and the use of clean energy sources” (An et al. 586). For example, Strubell et al. discovered that the fine-tuning process called neural architecture search, used to increase the final accuracy of the model through exhaustive trial-and-error, had “extraordinarily high associated costs for little performance benefit” (Hao, “Training”). Eliminating this step in the BERT model reduced the carbon footprint to less than 400 times that of the original. Another study found that the open-source BLOOM model developed by Hugging Face produced around 25 metric tons of carbon dioxide, which is just five percent of the estimated carbon footprint of GPT-3 (Luccioni et al. 7). Even though the two models are roughly the same size, with around 175 billion parameters, BLOOM has a much smaller carbon footprint because it was “trained on a French supercomputer powered mostly by nuclear energy.” On the other hand, “[m]odels trained in China, Australia, or some parts of the US, which have energy grids that rely more on fossil fuels, are likely to be more polluting” (Heikkilä). With the right incentives and regulations, large AI models can be optimized to be much more efficient through environmentally conscious engineering practices.
However, in our capitalist economy where researchers at for-profit companies are locked in cutthroat competition, they are forced to take whatever steps are necessary to produce better AI results. In this race, any considerations about the consequences on the climate get completely thrown out of the picture. OpenAI “chases a computationally heavy strategy—not because it’s seen as the only way to AGI, but because it seems like the fastest” (Hao, “Messy, Secretive Reality”). It’s not that AI researchers and scientists are inherently evil or don’t care about the environment. In fact, most are good people who genuinely believe in AI’s potential for helping humanity. I recall, in November last year, I had a FaceTime call with a longtime family friend of mine. Two months prior, he had completed his doctorate at MIT and joined OpenAI’s research team working on GPT-3. When I asked him why he chose this path, he explained that he had actually turned down lucrative offers from quant firms to pursue AI research in academia, and he was truly drawn to OpenAI’s founding mission of advancing artificial intelligence for the benefit of humanity.
Humanity’s greatest challenge is climate change, and AI indeed has the potential to be of great service in our search for a solution. But, as it stands today, AI inflicts far greater harm to humanity and the planet at large with its unabated carbon footprint. In order for researchers to not just pay lip service to noble aspirations of advancing AI to help humanity, two key issues must be addressed: the lack of transparency and the misalignment of incentives.
The root of these complications in contending with AI’s carbon footprint problem stems from the privatization of AI research. Strubell explains that “training huge models on tons of data is not feasible for academics—grad students especially, because we don’t have the computational resources” (qtd. in Hao, “Training”). Since the publication of her study on the carbon footprint of AI models, which she wrote as a grad student, Strubell has joined the Computer Science department at Carnegie Mellon University as an Assistant Professor, but her decision to remain in academia places her in the minority. As shown in the most recent 2023 report from Stanford’s Institute for Human-Centered AI, the past decade has witnessed the unrelenting trend of more and more new AI PhDs in North America leaving academia and entering jobs in industry upon graduation, rising steadily from approximately forty percent in 2011 to nearly sixty-five percent in 2021 (Lynch). This phenomenon is intertwined with the trend toward larger AI models. Due to the inequitable access to computational resources, researchers are increasingly drawn to large tech companies whose for-profit natures necessitate a research agenda motivated by drastically different incentives compared to purely academic research. Granted, it might be too late to completely reverse this shift. However, it is not too late to demand more transparency from large tech companies and to push for policies mandating public disclosure of carbon emissions from their AI models. According to a paper from 2022, coauthored by Strubell, Luccioni, and other researchers, some AI and machine learning “[c]onferences such as NeurIPS and NAACL have recently added emissions reporting as an optional part of the submission process,” but “both carbon estimation and reporting in ML publications and technical reports remain a relatively rare phenomenon” (Dodge et al. 1886). The practice is even rarer in industry, where the stakes are higher with larger models. Greater transparency is essential to sparking dialogue, which is the first step to action.
To connect discourse to action, there must be greater efforts to educate computer scientists about the role of AI in accelerating climate change. As it turns out, there is a course offered here at Columbia called “Machine Learning and Climate,” taught by Professor Alp Kucukelbir. Based on the syllabus, of the twelve weeks of the course, eleven are devoted to learning how AI can be used to tackle climate challenges, from tracking worldwide power-plant emissions to modeling stratospheric aerosol injection. However, the question of AI’s own carbon footprint is unaddressed until the last week of the course (Syllabus). While the course still serves as a small step towards raising awareness about the issue, it is only a graduate-level elective. The vast majority of CS students pass through the curriculum without ever touching on the environmental costs of AI (“CS@CU Undergraduate Programs”). This issue must be integrated into the core curriculum for all students majoring in Computer Science and related fields. Education goes hand-in-hand with greater transparency in laying the basis for change. As students become future researchers, they must understand how the AI technologies they build impact the environment. As demonstrated by Strubell and Luccioni, the computational toll of large AI models can be drastically decreased through small steps in the engineering process. The problem is that most researchers are still completely unaware of this issue.
In recent weeks, there have been growing calls for a six-month moratorium on research into AI models more powerful than GPT-4. So far, the petition, “Pause Giant AI Experiments: An Open Letter,” spearheaded by Max Tegmark, an AI researcher at MIT, has been signed by over 30,000 researchers, industry leaders, and policymakers. Among its supporters are Elon Musk, Steve Wozniak, Andrew Yang, Yuval Harari, and Columbia CS Professor Daniel Bauer, who teaches a popular course on Natural Language Processing—which lays the groundwork for the technology behind GPT-4. The letter cites concerns about unknown “risks to society and humanity” as justification for the moratorium but fails to explicitly mention the immense environmental toll of AI models (“Pause”). On a recent episode of fellow MIT AI research scientist Lex Fridman’s podcast, Tegmark discusses fears of GPT-4 replacing human jobs, daydreams about using AI to search for extraterrestrial life—and even engages with catastrophic predictions of how AI might end humankind. But, during the entire three-hour-long interview, the issue of the immense impact of AI on global climate never directly surfaces. It simply feels like the elephant in the room. Clearly, despite the handful of voices sounding the sirens about the alarming carbon footprint of AI models, the issue remains relatively unacknowledged within the wider CS research community. And, even if it is known, there is a lack of incentive to address the problem, as the inconvenient truth is shunned in the frenzied race towards bigger, more accurate models.
Taking a pause on the development of larger AI models presents the perfect opportunity for researchers and policymakers to face the uncomfortable reality. It provides precisely the impetus needed to dissect the true carbon footprint of large AI models and to reevaluate the feasibility of continued research in this direction. This issue must be addressed before we continue spiraling down the dangerous path of chasing ever larger models in the search for greater artificial intelligence. It is, after all, artificial intelligence. We cannot get so caught up in its hype that we forget what it means to be human—especially what it means to be humans residing on planet Earth. It does not matter how much AI advances if we recklessly ignore the terrifying reality of climate change. We must prioritize protecting our only home. This is humanity’s only path forward.
WORKS CITED
An, Jiafu, et al. “ChatGPT: Tackle the Growing Carbon Footprint of Generative AI.” Nature, vol. 615, 23 Mar. 2023, pp. 586, https://www.nature.com/articles/d41586-023-00843-2.
Bass, Dina. “OpenAI Needs Billions to Keep ChatGPT Running. Enter Microsoft.” Bloomberg, 26 Jan. 2023, https://www.bloomberg.com/news/articles/2023-01-26/microsoft-openai-investment-will-help-keep-chatgpt-online#xj4y7vzkg.
“CS@CU Undergraduate Programs.” Department of Computer Science. Columbia University, 21 July 2020, https://www.cs.columbia.edu/education/undergraduate/.
Dodge, Jesse, et al. “Measuring the Carbon Intensity of AI in Cloud Instances.” FAccT ’22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 20 June 2022, pp. 1877-1894, https://doi.org/10.1145/3531146.3533234.
Fridman, Lex, host. “#371 – Max Tegmark: The Case for Halting AI Development.” Lex Fridman Podcast, season 1, episode 371, 13 Apr. 2023, https://lexfridman.com/max-tegmark-3/.
Hao, Karen. “The Messy, Secretive Reality behind OpenAI's Bid to Save the World.” MIT Technology Review, 17 Feb. 2020, https://www.technologyreview.com/2020/02/17/844721/ai-openai-moonshot-elon-musk-sam-altman-greg-brockman-messy-secretive-reality/.
———. “Training a Single AI Model Can Emit as Much Carbon as Five Cars in Their Lifetimes.” MIT Technology Review, 6 June 2019, https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/.
———. “What Is ChatGPT? What to Know About the AI Chatbot.” The Wall Street Journal, 14 Apr. 2023, https://www.wsj.com/articles/chatgpt-ai-chatbot-app-explained-11675865177.
Heaven, Will Douglas. “GPT-4 Is Bigger and Better than ChatGPT—but OpenAI Won’t Say Why.” MIT Technology Review, 14 Mar. 2023, https://www.technologyreview.com/2023/03/14/1069823/gpt-4-is-bigger-and-better-chatgpt-openai/.
Heikkilä, Melissa. “We're Getting a Better Idea of AI's True Carbon Footprint.” MIT Technology Review, 14 Nov. 2022, https://www.technologyreview.com/2022/11/14/1063192/were-getting-a-better-idea-of-ais-true-carbon-footprint/.
“How Artificial Intelligence is Helping Tackle Environmental Challenges.” UN Environment Programme, 7 Nov. 2022, https://www.unep.org/news-and-stories/story/how-artificial-intelligence-helping-tackle-environmental-challenges.
Luccioni, Alexandra Sasha, et al. “Estimating the Carbon Footprint of BLOOM, A 176B Parameter Language Model.” arXiv, 3 Nov. 2022, https://doi.org/10.48550/arXiv.2211.02001.
Lynch, Shana. “2023 State of AI in 14 Charts.” Stanford University Human-Centered Artificial Intelligence, 3 Apr. 2023, https://hai.stanford.edu/news/2023-state-ai-14-charts.
Milmo, Dan. “ChatGPT Reaches 100 Million Users Two Months after Launch.” The Guardian, 2 Feb. 2023, https://www.theguardian.com/technology/2023/feb/02/chatgpt-100-million-users-open-ai-faste
Weaponizing Innocence: The Danger of Anti-Trans Legislation
Full text linkWhen I was four years old, I confidently told my parents, “I am a boy,” and I demanded they call me Kade. Like most parents might do, they laughed it off, claiming they had a tomboy. However, my feelings persisted, and it wasn’t until I was eleven years old that I discovered the language to describe what I had been feeling. I have gender dysphoria; I am transgender. I was lucky enough to start my medical transition in my teens. At fifteen years old, I began testosterone, and, at sixteen years old, I got top surgery. If my parents had denied me access to gender-affirming care (GAC), I would not be here today. Unfortunately, that is the reality facing transgender youths in the United States.
In early 2020, the effort to restrict gender-affirming care began as states started proposing bills to ban such care for minors. The U.S. Department of Health and Human Services defines gender-affirming care as “a supportive form of healthcare” (HHS Office of Population 1) made up of “psychological, social, medical, and legal aspects” (Substance Abuse 37). This care involves interventions such as social affirmation, legal name and gender change, therapy, puberty blockers, hormone replacement therapy, and gender-affirming surgeries. As someone nearing their sixteenth birthday at that time, I was terrified. The COVID-19 pandemic delayed these bills from passing, but 2021 brought a surge of even more anti-trans bills. Shortly after my seventeenth birthday, on April 6, 2021, Arkansas became the first state to ban GAC for youth when they enacted House Bill 1570—the Save Adolescents From Experimentation (SAFE) Act. The situation has only gotten worse since. For example, Tennessee Senate Bill 1 (SB1) will go into effect on July 1, 2023. This bill prevents minors from accessing GAC and demands that youths already receiving GAC (such as puberty blockers or hormones) must stop it by March 31, 2024. I have never been so glad to be an adult.
This surge of anti-trans legislation began due to the increased visibility of the transgender community. The HBO documentary Transhood, released on May 28, 2020, specifically sparked outrage. Transhood follows four children (ages four, seven, twelve, and fifteen at the beginning) over five years as they navigate their gender identities. Although this film cannot be held accountable for the anti-trans legislation, it exacerbated a long-standing controversy by portraying the realities of gender-diverse children, ultimately leading to the final breaking point for some conservatives. On X, political commentator Matt Walsh (@MattWalshBlog) quote-posted a clip of Transhood with the message, “[b]y the way, this is from a new HBO documentary called ‘Transhood.’ I can tell you it’s even worse and more exploitative and dangerous and sadistic than ‘Cuties’ was, and it should provoke an even stronger backlash from us.” The clip shows four-year-old Phoenix being too shy to tell their congregation they are a girl, so their mother does it for them. Walsh’s claim that Transhood is worse than Cuties is grossly misleading. Cuties is a Netflix film with young girls in sexually suggestive scenes—which prompted Republican Congress members to appropriately call Cuties child pornography (Banks 1). Walsh then posts about Phoenix again: “A mother puts her 4 year old son in a dress and reads him LGBT propaganda. You’re literally watching her brainwash the child into thinking he’s a girl. It’s no mystery how children end up ‘trans.’ This is it. Right here.” Walsh’s posts brought public attention to Transhood, and the film received much more criticism from the public. For example: “[s]uch a horrific video, transing children is a crime and people involved in this should be held accountable for their actions,” as one citizen posted (qtd. in Wynne). Thus, proponents of such legislation argue that transgender youths do not actually exist—it is an ideology that parents are forcing onto their kids; it is abuse. People on this side of the debate believe that all GAC is inherently harmful and will cause irreversible damage to children. According to this logic, it must be prohibited since minors do not understand what they are “consenting” to.
On the other hand, opponents of such legislation believe youth know more about their gender identity than anti-trans adults give them credit for. They think we should use a gender-affirmative model to provide youth with GAC that is deemed essential. For example, one X user said, “[w]ithholding gender affirming care from trans youth is absolutely abusive. This will kill children” (@EuphoriTori). Montana State Representative Zooey Zephyr (@ZoAndBehold) posted a clip of herself addressing the House alongside the quote: “If you are denying gender-affirming care and forcing a trans child to go through puberty, that is tantamount to torture, and this body should be ashamed. If you vote yes on this bill, I hope the next time you bow your heads in prayer, you see the blood on your hands.” Parents of trans youth have also spoken out that they fear this type of legislation will kill their children, a fear exemplified in the perspectives collected in a study by Kidd et al. One parent shared, “I asked [my child] the other night how he thinks his life would look without [puberty blockers]. Without needing to think about it, he said, ‘I’d probably be dead.’ He’s 14” (1084). Another parent expressed, “[Legislators] may as well provide the blade for my child to slit his wrists with” (1084).
So, while the passage of bills such as the Arkansas SAFE Act and Tennessee’s SB1 has garnered national attention and sparked controversy, the impact on transgender youth cannot be understated. As someone who accessed gender-affirming care as a minor, I know firsthand how beneficial it is and that its positive impact on one’s mental health can be outstanding. However, the political debate overlooks complex ethical issues that the debate surrounding transgender youth has raised. Both sides of the debate argue that they are “thinking of the children,” albeit in different ways, but neither side addresses the actual impact of this legislation on all transgender people. Thus, the question arises: how do anti-trans legislative measures contribute to a harmful and dangerous climate for transgender individuals?
Now, to understand where both sides are coming from in the context of “protecting the children,” it will be helpful to understand what “the child[ren]” means. Literary critic and scholar Lee Edelman analyzes the concept of “the Child” in American politics and culture. Edelman reveals that the idea of the child is a symbol of futurity—a representation of the continuation of society. However, queerness does not fit into this vision of the future because “[t]he Child… marks the fetishistic fixation on heteronormativity: an erotically charged investment in the rigid sameness of identity” (Edelman 21). That is, invoking “the Child” is a way to replicate the past and maintain the heteronormativity of society. Edelman argues that “the sacralization of the Child thus necessitates the sacrifice of the queer” (28) because conservatives seek to eliminate the “queerness of resistance to futurism” (27). Queer identity inherently resists this idea of a future-oriented society, as the normative ideas inherent in futurism—such as reproduction and traditional family structures—marginalize queer people whose mere existence challenges them. The right-wing tries to eliminate queerness through confrontation and repression. As the issue of trans youth healthcare has become completely politicized, it is productive to examine the legislation through the lens of Edelman’s understanding of “the Child” to understand the negative impact on the trans community.
As it happens, the Arkansas SAFE Act displays how the right-wing desires “the elimination of queers” (28). The title of the act, “Save Adolescents from Experimentation,” immediately sets the tone of the bill, implying that GAC is an unproven and inherently dangerous experiment on adolescents. The opening line emphasizes this: “Arkansas has a compelling government interest in protecting the health and safety of its citizens, especially vulnerable children” (State of Arkansas, Legislature, House 1). People associate the use of words such as “compelling,” “protecting,” “safety,” and “vulnerable” with the manipulation of innocents. In turn, the general public is more likely to agree with the bill, as such images will remain in mind. The bill uses the word “irreversible” five times, all in the context of infertility and sterilization (3, 5). This repetitiveness and focus on the ability to reproduce aligns with Edelman’s theory of futurism. The Arkansas SAFE Act cares about “reproduc[ing] the past” and maintaining the heteronormativity of society (Edelman 31). Tennessee SB1 also opens with a similar message: “The legislature declares that it must take action to protect the health and welfare of minors” (State of Tennessee 1). This language is slightly less emotive but has the same intent as the Arkansas SAFE Act. Plus, Tennessee SB1 also uses phrases such as “harmful” (1, 2, 4), “experimental” (1, 2), “minor’s best interest” (2), “protecting minors” (2) “minor injured” (4), and “threat” (5) to evoke the same message found in the Arkansas SAFE Act. The word “purported” shows up 12 times in the bill (1-4), delegitimizing the lived experience of transgender people and further pushing the message of coercion. The fear-mongering language purposefully evokes the image of a helpless and highly impressionable child, pushing the notion that children cannot make decisions for themselves and implying that children are being manipulated or forced into transitioning.
Thus, the proponents of anti-trans legislation argue that children do not have the right to autonomy for “decisions” as consequential as their gender. This lack of autonomy comes from the notion that children constantly play make-believe, so a child claiming they are not their assigned gender at birth (AGAB) is just pretending. Indulging children in this “fantasy” by allowing them to transition sets them up for future regret. Walsh expresses this viewpoint well in a conversation with Tucker Carlson about Transhood when Walsh states, “Children literally cannot differentiate between fact and fiction, reality and fantasy. I have a four-year-old boy who thinks he’s a stegosaurus, so I’m not going to take him to Jurassic Park” (Carlson 3:24-34). In this same conversation, Carlson claims that “four-year-olds don’t make decisions like that. They can’t,” when referring to Phoenix (1:35-8). Journalist Jesse Singal builds off Walsh and Carlson by reminding us that teenagers are constantly going through phases and trying to be rebellious. Singal goes on to explain that teenagers are often lost and trying to find their place in society, so teenagers believing they are not their assigned gender at birth “[stems] from rigid views of gender roles that [are] internalized” (91). Ultimately, this argument against GAC for minors boils down to what some believe will result in their future regret. They believe the minor in question will regret transitioning because they did not understand the repercussions, and they will then detransition and live as a broken, scarred version of their AGAB. So, by banning GAC, they are “saving” the children from “radical gender ideology” (Shapiro 1:04:14-15). They are “fighting for the children” by protecting them from “abusive” parents. They are trying to ensure what they believe will be a healthy future as their AGAB. They are arguing that they know what’s better for children in the long run than the parents and doctors of these children and, especially, the children themselves.
However, children are not too young to know their gender. Biologist and activist Julia Serano explains subconscious sex as “this unconscious self-understanding that (for many trans people) precedes any conscious or deliberate grappling with questions of gender identity” (178). Most people know their gender without consciously thinking about it and the language that describes it. Plus, evidence supports that “children develop the ability to label gender groups and to use gender labels…between 18 and 24 months,” begin developing an “awareness of their own ‘self' at roughly 18 months,” and develop an understanding of gendered stereotypes by age three (Martin and Ruble 3). So comprehending gender stereotypes helps children understand what role they want to play in society, which can lead to “falsely” identifying as trans. However, even then, that misunderstanding occurs at an age when only a social transition would occur anyway. There is no harm in allowing children to explore their gender. Even Singal agrees. For example, a boy wearing “girls” clothes is only deemed unacceptable because of the rigid gender roles that Singal claims cause people to regret transitioning.
Furthermore, the anti-trans argument stems from the idea that gender and sex are entirely biological. These individuals believe that genetics dictate gender, so, if a child claims they are transgender, the parents are forcing that identity onto their child, or elderly trans people are indoctrinating the child. For example, while reviewing Transhood, Walsh claimed that parents (primarily mothers) supporting their trans kids is an example of “Munchausen Syndrome by proxy” (Walsh, “SHOCKING” 9:20-22), a condition in which parents convince their healthy children that they are sick and force them into unnecessary medical procedures. Walsh and Carlson are not alone in this belief. One “whistleblowing” educator expressed her concerns about the growing number of children identifying as trans. This teacher believes children are “easily influenced” and learn to identify as trans from older students and YouTube stars (Manning). This article describes younger children as “vulnerable,” “exploited,” “brainwashed,” and “tricked.” The story portrays the YouTube stars and older trans kids as villains, describing them with words such as “to blame,” and “groom[ers]” (Manning). The article weaponizes trans identity and transition, characterizing them as “mutilation,” “harmful,” “tragedy,” “nightmare,” “agenda,” and so on (Manning). Such language portrays children as being harmed and requiring protection. This piece uses such language to create a pedophilic overtone that further pushes the idea of these children needing to be saved from trans ideology.
Popular books also showcase this opinion. One of The Economist’s 2020 books of the year gives the same message as above. Irreversible Damage uses “indoctrination,” “cult,” “coach,” and “propaganda” repeatedly, as well as “brainwashed” (Shrier). This type of language evokes the image of people endangering and coercing innocent children, promoting the erasure of queerness and transness from the public view. The reason kids identify as trans, according to this thinking, is because they were tricked and forced to by older trans people or parents who have fallen victim to “radical left-wing gender theory” (Walsh, “SHOCKING” 9:59-10:00). Some of this legislation banning GAC for youth was born from people like Walsh and Carlson voicing their “concerns.” Such legislation publicizes the belief that children need to be prevented from mutilating their bodies and causing irreversible damage, simultaneously promoting the belief that trans adults are sick and damaged. However, these anti-trans advocates fail to acknowledge all of the scientific and medical research showing how vital GAC is for mental health and overall well-being, only focusing on the minute number of people who detransition (Coleman et al. S41).
The medical consensus is that gender-affirming care is lifesaving for many trans individuals. “[R]esults align with past literature, suggesting that pubertal suppression for transgender adolescents who want this treatment is associated with favorable mental health outcomes,” and “participants’ suicidality scores…significantly decreased following administration of [gender affirming hormones], . . . [and] participants’ general well-being scores significantly increased” (Turban et al. 1; Allen et al. 307). A study published in 2022 followed 317 binary trans children (between ages three and twelve) who had already socially transitioned over a five-year timeline. After five years, only 7.3% of those children changed their identity; a mere 2.5% detransitioned, and the rest maintained a transgender identity (Olson et al. 2-3). This indicates that just 2.5% ultimately identified with their AGAB, while the majority continued to identify as transgender, albeit with some fluctuations in certainty during the study period (the 7.3%). Plus, most adults who stop transitioning do so due to external pressures, not the regret that anti-trans advocates suggest (Roberts 2).
While some may claim that anti-trans rhetoric comes from concern for children, this rhetoric in fact seems to come from hate and ignorance. Those who spread this rhetoric do not understand that there are many guidelines for treating transgender youth—all of which give timelines on when to start medical care. For example, individuals must reach Tanner stage 2 of puberty before starting puberty blockers “because the experience of physical puberty may be critical for further gender identity development for some” adolescents (Coleman et al. S64). Individuals must be at least fourteen (and usually sixteen) years old to start hormones, and eighteen years old to get genital surgeries (Mahfouda et al. 486). Plus, surgeons (no matter their patient’s age) typically require at least two letters from mental health care professionals supporting the patient getting the surgery (Milrod and Karasic 628). For me to access any gender-affirming medical procedures, even as an adult, I had to fulfill the requirement of living as male for at least a year and obtaining letters from both a therapist and an evaluative psychologist. Perhaps, then, this anti-trans rhetoric comes not merely from hate or ignorance, but from a desire to do battle with the queer and trans survival instinct that Professor Jack Halberstam would call “failure” (88). Halberstam argues that mainstream society is oppressively obsessed with the notion of success, so failure is a way to resist these dominant structures—and queer people have long been resisting social norms. Heteronormative society has made it so that “the queer body and queer social worlds become the evidence of that failure” (94). Queer people have long been seen as “failures” in society because they subvert societal norms, and queer people have learned to embrace that, making it their version of success. Those spewing anti-trans rhetoric fear the redefining of “success,” as they need to maintain the heteronormativity of society. The only way to do so is to protect children from seeing happy queer and trans adults. The so-called epidemic of trans youth threatens the “default heteronormativity of modern culture with its worst nightmare, a queer planet” (Warner 16).
Thus, bills such as the Arkansas SAFE Act and Tennessee SB1 are crucial aspects of a much larger problem facing the transgender community. These extreme anti-trans bills make less severe bills, such as the bathroom bills, look reasonable. They stoke fear against transgender people, making us seem like a public enemy with our supposedly predatory ways, which, in turn, emboldens transphobia and gives people a sense of justification for hating trans people—so much so that, nationwide, there were 615 anti-trans bills proposed in 2023 (Trans Legislation Tracker). Some bills try to make it illegal for me to continue hormones, as I am younger than 26 (State of Texas 6). In other states, I can go to jail if I do not use the women’s restroom (State of Arkansas, Legislature, Senate 2). States are trying to write us out of existence by doing the very thing they are accusing us of doing: redefining “sex” (State of Montana 1). Ultimately, anti-trans legislation does more than deny medical care—it erodes the humanity of trans people. By framing trans existence as a threat to children, society denies us our right to live authentically. These bills are not about protecting society but about dehumanizing, criminalizing, and erasing trans people. These bills restrict our lives, take away our rights, and oppress us with the goal of making it impossible for us to exist. My survival is a testament to the life-saving power of GAC—and my story is just one of thousands that reflect this sentiment. At the Conservative Political Action Conference in 2023, Michael Knowles said, “[T]ransgenderism must be eradicated from public life entirely,” and the crowd cheered (qtd. in Hawkinson). It was never about “the children”; it was always about the utter elimination of transgender people.
WORKS CITED
@EuphoriTori. “Withholding gender affirming care from trans youth is absolutely abusive. This will kill children.” X, 22 Apr. 2023, https://twitter.com/EuphoriTori/status/1649738141681561601.
Allen, Luke R., et al. “Well-Being and Suicidality Among Transgender Youth After Gender-Affirming Hormones.” Clinical Practice in Pediatric Psychology, vol. 7, no. 3, 2019, pp. 302–11, https://doi.org/10.1037/cpp0000288.
Banks, Jim. Letter to William Barr recommending charges against Netflix for distributing film Cuties. 17 Sept. 2020. https://banks.house.gov/uploadedfiles/9172020_banks_cuties_letter.pdf.
Carlson, Tucker. “Matt Walsh – Fox News – Tucker Carlson – 12.3.2020.” YouTube, uploaded by Stagepost Live Shot Guests, 3 Dec. 2020, www.youtube.com/watch?v=XPM91hMd9dg.
Coleman, E., et al. “Standards of Care for the Health of Transgender and Gender Diverse People, Version 8.” International Journal of Transgender Hea
Table of Contents, Acknowledgement, & Editor's Note
No full textTable of Contents, Acknowledgement, & Editor's Not
Motivators and Hindrances of Consuming Reusable Water Bottles: An Exploratory Case Study at Columbia University
No full textThis pilot study explores the factors that motivate and hinder tap water consumption and reusable water bottle usage among students at Columbia University. Despite Manhattan's tap water being among the highest quality globally, the prevalence of single-use plastic water bottles on campus remains significant. Choosing reusable water bottles over single-use plastics is a pro-environmental behavior that can reduce plastic production and waste. This research investigates the underlying reasons behind students' choices regarding tap water consumption and reusable bottle usage. The data were collected from 58 students selected through convenience sampling utilizing questionnaires and participant observation. The questionnaire responses were thematically coded, and descriptive statistics, including percentages and frequencies, were used to analyze the data. Findings indicate that, while a relatively high percentage of students consume tap water compared to other universities, hygiene concerns related to water quality are the main hindrances. In terms of bottle usage, the primary motivation for carrying a reusable water bottle was to increase water intake. The major barrier was the inconvenience and lack of portability of reusable bottles. Notably, students overestimated the positive environmental impact of using reusable water bottles, which could potentially lead to greater environmental harm due to misconceptions. This pilot study underscores the need for further in-depth research to identify and address the misconceptions and barriers affecting students' pro-environmental behaviors
Exploring the relationship of diagnostic reasoning, self-efficacy, and clinical reasoning of physical therapy students
No full textPurpose: One of the primary objectives of entry-level physical therapy education is to develop students’ clinical reasoning (CR) skills to provide optimal, patient-centered care. However, CR is a highly contextualized concept, and the assessment of CR development to ensure students have the requisite skills for safe patient care prior to clinical experiences is challenging within an education program. Self-assessment tools may provide a time-efficient opportunity to assess students’ CR development across their education. Both diagnostic reasoning and self-efficacy have been correlated with CR performance in physical therapy. This study aimed to explore the relationship between diagnostic reasoning, self-efficacy, and CR development among physical therapy students.
Methods: Diagnostic reasoning was assessed through the Diagnostic Thinking Inventory (DTI). Self-efficacy was measured by the New General Self-Efficacy (NGSE) scale and Physical Therapist Self-Efficacy (PTSE) scale. CR ability was evaluated through the Think Aloud Standardized Patient Examination (TASPE) performed during a standardized patient simulation, and scores for the CR performance criteria as assessed by clinical instructors on the Physical Therapist Clinical Performance Instrument Version 2006 (CPI).
Results: There was no correlation between self-assessment scores on the DTI, NGSE, PTSE, and CR performance assessed by faculty during a standardized patient simulation (TASPE) or clinical instructors using the midterm and final CPI during a 12-week full-time clinical experience.
Conclusion: This study was unable to identify a self-assessment tool or a student performance indicator that could accurately predict CR performance during upcoming full-time clinical experiences
Replacing Seclusion & Restraint Practices in Psychiatry With Sensory Rooms
Full text linkThe use of seclusion and restraint (S/R) in acute psychiatric inpatient settings persists as a controversial practice, causing significant harm to patients and stress to staff. This policy brief examines the ethical, financial, and systemic implications of S/R and advocates for replacing S/R with sensory rooms—an evidence-based approach fostering emotion regulation, patient autonomy, and trauma-informed care. Recognizing that eliminating S/R may not be immediately feasible, this brief proposes an incremental approach through a hypothetical pilot program at Jackson Behavioral Health Hospital: converting an isolation room, or a room where a patient receives intervention separately from other patients, on each psychiatric inpatient unit into a sensory room, alongside incentives to reduce overall S/R usage. Sensory rooms can then be evaluated as a humane and cost-effective alternative to S/R practices. This policy brief aims to advance knowledge on patient-centered interventions in mental health care and underscores the ethical imperatives and financial incentives for legislative and organizational policy reform in psychiatric care.
Keywords: seclusion, restraint, sensory rooms, psychiatric inpatient care, policy reform, trauma-informed care, social justic
Understudied and Underserved: Advancing Inclusive Mental Health Care for Individuals with Intellectual and Developmental Disabilities
Full text linkIndividuals with intellectual and developmental disabilities (IDD) face profound inequities in accessing and receiving quality mental health care despite being at increased risk for psychological distress. This paper examines the historical and systemic barriers that perpetuate these disparities—including financial limitations, imbalances within the healthcare system, provider shortages, inadequate research funding, and persistent misconceptions about the therapeutic potential of individuals with IDD. The exclusion of individuals with IDD from research and psychotherapy further exacerbates these challenges, creating significant gaps in clinical knowledge and guidance. In addition, individuals with IDD face disproportionately severe mental health challenges, including heightened exposure to trauma, diagnostic overshadowing, and the impact of social stigma.
In response, inclusive strategies are proposed to improve care by addressing the unique cognitive, communicative, and emotional needs of this population. Central to these recommendations is a shift toward person-centered, dignity-affirming care that recognizes individuals with IDD as autonomous participants in their own treatment. To advance equity in mental health care, it is imperative to pursue transformative change through inclusive research, targeted provider training, and evidence-based therapeutic adaptations. By amplifying the voices of individuals with IDD and addressing the systemic factors that have long excluded them, it will be possible to move toward a more equitable and responsive mental health care system for this underserved community.
Keywords: intellectual and developmental disabilities (IDD), mental health disparities, diagnostic overshadowing, person-centered therapy, trauma-informed care, inclusive psychotherapy, disability advocac
Regulating AI: Differences Between the U.S. and the EU
Full text linkThe topic for this lecture is very much about the future. Basically, we are talking about things we are only starting to see. So, it may still be premature to talk about regulating Artificial Intelligence (“AI”). Not too long ago, several experts and tycoons in the AI community issued an open letter last year saying, in effect, “let’s pause it a bit. Let’s see how to regulate it.”
Since then, we have seen the rise of ever more forms of AI, notably ChatGPT, Stable Diffusion, and all these AI systems and processes of diffusing images, adding noise to them and then de-noising them, so as to subsequently generate a different image or a different audiovisual recording at the request of a prompt. Creators and professionals are concerned about that. And so are we, professors. In fact, AI seems to be a game changer in the world of copyright. We are not Luddites but we are starting to sympathize with them (if I may put it that way). This is how much AI is shaking our lives
A Case for Libraries' Survival in the Internet Age: Mass Digitization of Literary Works and the Legality of Controlled Digital Lending
No full textIn the United States, copyright law rests upon a delicate balancing act. Our system aims to maximize both incentives for right holders to create and public access to creative works under a constitutional mandate to “[p]romote the Progress of Science and useful Arts.” Forces of technology and globalization have compounded the complexities of striking that balance, making it far easier for a physical literary work to be scanned, digitized, and shared around the world—often without the author’s express permission. In turn, the digitization of creative works offers widespread benefits for the maximization of public accessibility: A work can reach the hands of countless students and scholars who would not otherwise have the funds, resources, or accommodations to read it