Tag: AI Class Policy

  • Things that Happened in the Class I Didn’t Teach

    Things that Happened in the Class I Didn’t Teach

    On Adjunct Teaching and Generative AI

    A photo by Valentin Ivantsov from pexels.com; not AI-generated

    I.

    If you read my last post about the Generative AI policy-turned-manifesto that I wrote for my college writing class, I probably left you wondering: How did my students respond?

    It went as you might have imagined: Blank stares from eye-contact avoidant young people raised in the age of excessive online engagement and the COVID-19 pandemic. They clearly planned to use ChatGPT for their assignments no matter what I said.

    As I peppered them with questions and attempts at humor, the class remained painfully silent. I joked, “In my day, we found ways to write five-page essays without even opening the book.” Crickets.

    Later, reading the online responses I required students to post, I learned that our discussion resonated with them more than I realized. While they found AI useful in many ways, they conceded that it came with a high social and ecological cost. Additionally, many of them were against local data centers and some had even attended protests.

    They showed in-depth knowledge of current events and an aversion to the future insisted upon by Silicon Valley CEOs. If someone at their commencement had brought up AI, they would have booed.

    Actually, they wouldn’t. “They” don’t exist. I never taught the class because it didn’t enroll with enough students.

    II.

    Teaching classes as a graduate student, I disabused myself of a future as an English professor fairly quickly. If you are prone to self-deprecation and thinking that everyone hates you, teaching young adults might not be the best career move.

    There were other factors, of course: I was in my late twenties at a semi-prestigious private university that attracted undergraduates who had failed to get into their top choice schools. They were well-trained—not to be confused with highly-educated—students who knew how to pass tests and climb social ladders. In other words, they were there for the A, not to develop their passion for writing and critical thinking from a young woman who looked as old as, if not younger, than them.

    Additionally, I was a burgeoning scholar in Black literature, constantly defending myself—to students and colleagues alike—against the assumption that I was a white lady who fetishizes racial difference. Authentic Black experience wasn’t something I could offer, but, to clarify, I broadly focused on the early 20th-century, a historical context that none of us had an authentic experience of. Plus, like all professors, I wasn’t teaching from my life; I was teaching fiction and how narratives make and unmake identities.

    The biggest reason I didn’t think about becoming an English professor, though, is that I didn’t have a choice. No jobs existed. An abundance of graduate students outnumbered the retirement-age tenured faculty refusing to retire, leaving the academic job market oversaturated and fiercely competitive.

    III.

    I also wanted easy employment with a stable source of income.

    After completing a successful dissertation defense, my mentor, a Jamaican woman who I still think of as my surrogate second mother, smiled and clapped her hands when I told her that I was moving onto hairstyling. “Yay!” she had said. “You will make money!”

    A few months later, I performed as a novelty in the salon. Clients who didn’t know about my academic past treated me as if I were less educated than them. I remember a client introducing me to her daughter: “Her name is Djuna, pronounced ‘Juna,’ after Djuna Barnes, a famous writer from the early 20th century.”

    Those who knew would say things like: “Well, if hairstyling doesn’t work out for you, you can always go back to teaching!”

    I’d nod in agreement to everything everyone said. It was too hard to explain that I’ve read Nightwood five times; that academics have shelf lives, like medications, and once you stop teaching and producing scholarship, you expire, becoming unhirable; that I don’t have a teaching degree or the experience required to teach below the college level; that I was too thin-skinned and filled with imposter syndrome to confidently assert myself at the front of a classroom.

    While cutting and coloring hair, dying my fingernails brown and listening to banal tales—ones usually reserved for psychotherapists—I kept tabs on my graduate school cohort. Some dropped out. Some moved from one, underpaid, temporary adjunct job to the next, securing a contract in one city, only to have to uproot their lives and move across the country for the next gig. Some led writing centers, finding it droll, never digging back into their preferred areas of research. Some taught in private high schools, migrating from English to other subjects deemed more important, like history.

    Some got the associate professor jobs we had thought we wanted and found themselves burned out and wanting a different career after a few years. Some found jobs in other sectors, giving up the dream of professorhood for something more clock-watching-in-a-cubicle adjacent. Some wrote books while hustling in service jobs (I envied them). Some got into other graduate programs, like law school. Very few are still English professors, if they ever became one.

    IV.

    Before I entered graduate school in 2009, the media had been insisting that college English departments, along with the rest of the humanities, were in crisis. This crisis has never abated, and I’m sure the fight to prove the relevance of the humanities hasn’t gotten any easier with the Trump administration’s funding cuts, pressuring schools to abandon diversity initiatives and redact “woke” curricula.

    The doom and gloom surrounding the dearth of jobs and flagging popular interest in English literature was something my department never tried to hide from its graduate students: I was told over and over again that I had little hope of securing a tenure-track position. Yet, thinking that I had several more years to finish the program, I felt confident that, by the end of it, the tide would change.

    A decade later, I briefly entered the academic job market. While writing my dissertation, I spent months preparing CVs, cover letters, and teaching statements for junior faculty positions.

    I received no response, not even a polite rejection.

    If I had been invited to proceed, I would have faced further uncertainty while investing more time: I’d have to prepare for a series of intensive interviews, write new scholarship to present to the department, guest-teach classes, and go through a long process that would have likely led to harder-hitting rejections.

    None of this eternity of rejection appealed to me, but it also didn’t devastate me. Before sending out my applications, I knew that I hadn’t written enough papers, attended enough conferences, and taught enough classes to be considered for a faculty position. I was well-prepared for confirmation of the absurdity that you have to already be a professor to become a professor.

    Perhaps if I had wanted that life more fervently, I would have spent years adjuncting, writing those papers, going to those conferences, and finding myself pushed to the limit of my abilities and sanity after endless seventy-hour work weeks. Instead, I gave up and chose a different path.

    V.

    Considering everything I accomplished while also teaching and being a teaching assistant for undergraduate courses, it’s hard not to be gripped with a weird mix of pride and resentment. How could there really be no carrot at the end of the stick after all of that? Why did I go through it—the foreign language requirements, the oral examination after reading over 100 books, and writing a book-length manuscript on a niche area of study that no one would read, not even the three people on my dissertation committee— just to find myself trying to look less educated on a resume?1

    Repeatedly hearing in various thought pieces about how bad it is for people with advanced degrees doesn’t really mean much to most of us unless we find ourselves crossing the finish line of the highly educated, learning that such a distinction is synonymous with over-qualified. For me, it felt like graduating college all over again, mired in student loan debt and getting paltry job offers that guaranteed a drab life of unfulfillment, not worth the embarrassing salary.

    VI.

    I’m still trying to figure out how to sell myself as a valuable employee with a PhD. The fact is that none of the tests I aced, the book-length piece of scholarship I wrote, and the various other fiery hoops I expertly jumped through translate as “real work” experience to people hiring for jobs outside of the university. Moreover, while I want to find a way to put my creative and intellectual talents to gratifying social use, I am faced, like so many of us, with the realization that work and jobs in a capitalist context don’t cater to the desire to harmoniously integrate into a community.

    So, once I realized that hair wasn’t my calling, I wasn’t sure how to rebrand myself and pivot into a career that better suited my sincere passion—writing and editing.

    Spoiler alert: This isn’t a story in which, by the conclusion, I reveal how I found my true calling and path. I still feel stuck.

    One thing I thought I was always sure about, though, was that I would never debase myself by seeking work as an adjunct.

    VII.

    The head of an English department at a community college asked me to teach a college writing course, and I accepted.

    Although I had been resolved to avoid exploitative, underpaid adjunct work, after years of feeling unseen and undervalued, I was vulnerable. This unsolicited offer made me feel wanted. I also felt like I might lose the skills I worked so hard to hone during graduate school. It was time to dust off my PhD.

    Although, I was also pursuing a post-graduate certificate in creative writing at this institution—and interested in the six credit hours per semester that I could take for free—it wasn’t just for self-centered reasons that I took this job offer. The desire to do something meaningful and engage deeply with others also tempted me.

    At any rate, the one-thousand-dollar wage I was offered for one three-credit-hour course did not do much to persuade me.2

    VIII.

    Months before my scheduled class, fueled by perfectionism, I began to plan. In part, my anxiety was heightened by feeling like a time traveller: I had stopped being an educator when online classrooms and using AI to complete assignments became standard. There was a lot for me to catch up on.

    So, I pulled nearly every book from my bookshelf and got more from the library, creating a barricade of research around my bed. I wrote one ambitious syllabus after another, realizing that I was writing graduate student seminars, and then deleting them and starting over.

    I wrote assignment descriptions and rubrics. I took a required, unpaid training about accessibility and constructing online courses. After completing it, I built my course online, while researching Open Educational Resources to implement. I rewrote my syllabus again.

    When I went on walks, all I could think about was my class. At night, I couldn’t sleep because I was thinking about my class. Thinking about my class, I wrote a long-winded AI-policy-manifesto that none of my students would have read anyway.

    Months before the class was scheduled, I had already provided a lot of unpaid labor for a job I hadn’t started and that never promised to adequately pay me for my enthusiasm and time. (Did you read footnote 2?)

    A couple of days before the class was scheduled to start, it was cancelled.

    VI.

    The course cancellation notice came on the heels of another email, summoning me to HR for adjunct orientation. Since I was also scheduled to teach a different, late-starting section of the course, I still needed this orientation and to sign a contract.

    Orientation consisted of signing tax forms, not a contract. Unprepared, I didn’t bring the two forms of ID needed to complete my paperwork, andHR told me to come back on a different day.

    That was it. There was no actual training or introduction to the school; no explanation of the employment timeline following hiring; no explanation of what to do if a class gets cancelled.

    Since I live in a different town an hour away from the school, I asked if I could scan my passport to submit it to HR instead of having to drive back with the document. They said no—it had to be delivered in person.

    After I drove to the college to hand-deliver my identification, my contract never appeared.

    X.

    A month into the classes I signed up for while hoping to take advantage of the employee education benefit, I learned that my late-start course was also cancelled due to low enrollment.

    XI.

    Eight weeks into the semester, I found out that my employee education benefit form had been rejected, and there was no option to un-enroll and receive a refund.

    XII.

    I chided myself for ever falling for falling prey to an adjunct job offer.

    XIII.

    One of the classes I took—and am still fighting with administration not to have to pay for—was an online art class. 

    The teacher, a person I like and respect, fully embraced AI. They wrote large sections of their online lectures attributed to ChatGPT. Additionally, they invited students to summarize lectures and other class content using AI, instructing them on how to cite a LLM in correct MLA format.

    During an online class discussion, I asked the instructor about their wholehearted endorsement of AI. Especially as a fellow instructor—or rather, an aspiring one—I wanted to learn what the benefit of this approach might be.

    The gist of their response was that AI is here to stay, and so, if you can’t beat them, join them.

    XIV.

    AI is a product pushed on us to further enrich Silicon Valley companies that are sponsored by billionaires who have sociopathic views about the planet and the rest of the human race. It is a tool leading us to a future that none of us should want, and we should be fighting against this escalating dystopia tooth and nail.

    Moreover, AI doesn’t offer us any actual service. We already know how to do online research, write emails, make art, and find connections with others. If we don’t know how to do these things, we desperately need to learn.

    Especially as educators, we are obligated to help students who don’t have these skills learn how to wield them before they start using AI—and before they will be required to use AI to do their jobs. If we don’t continue to teach the next generation to be better thinkers and communicators without AI, we dangerously advance a future in which humans are the superfluous stewards of machines, helping computers talk to other computers.3

    Additionally, most of the services we think AI offers—medical advice, relationship guidance, friendship, therapy, legal advice, tax advice, consumer guides, and factual knowledge about history, philosophy, and other areas of study—are, at best, flawed and untrue, and, at worst, harmful and life-threatening. You don’t have to look far back into recent news headlines to learn how AI has helped young people facilitate mass shootings and kill themselves, either by suicide or from following fatal medical advice.

    What’s worse is that the companies who are pushing AI on us don’t care if their product hurts us and sucks up natural resources—all they want is for us to stay engaged with the digital hell they’ve created because that’s how they amass influence and wealth.

    XV.

    I didn’t push back on the online class instructor’s pro-AI stance. For the most part, the instructor encouraged students to use AI appropriately and thoughtfully. Then, the instructor undercut their own credibility: One course module lecture included two unverifiable images as examples of a type of art technique. The caption for these images read: “Images from ChatGPT. No info included on artists.”

    Flummoxed, I privately messaged the instructor, wanting to know how they could present these images as real if the sources were unknowable. How did we know if they were made by humans or AI? Moreover, if the instructor couldn’t verify the sources and the authenticity of the images, why use them as educational material at all?

    My concerns were acknowledged, but no deeper discussion came of it.

    XVI.

    The story I told at the beginning of this essay about my students’ responses to my AI policy wasn’t a complete lie. This semester, I did engage with students on the topic of AI—they just weren’t my students.

    In the discussions I partook in with my fellow students in the online class, students betrayed a healthy amount of skepticism toward AI. They acknowledged that AI has negative consequences on the environment and society even as they also used it to summarize lectures and develop discussion responses—properly attributed, as instructed.

    Even when the instructor did not always effectively model responsible AI use, these students followed the rules and used AI responsibly and creatively.

    Although finding me a little too pessimistic about the future and technological advancements, the students also agreed with a lot of the rants I posted. I got the sense that I didn’t need to compose such long and passionate responses because they had already thought deeply about AI. I found myself preaching to the choir. 

    XVII.

    I’ve been invited to teach another section of my cancelled course next semester, and I’m feeling conflicted about it. On the one hand, I spent a lot of time developing a course I really want to teach. On the other hand, I am still fighting with the college about my rejected EEB form and unpaid bill.

    If I decide to teach, instead of getting paid, I will go into debt.

    Regardless, this semester taught me that the next generation doesn’t need me to warn them about AI. They already know and are proceeding with caution.

    1

    In response to the crisis I’m describing, the department I graduated from has tweaked its PhD program. They got rid of the traditional dissertation model, opting for a “doctoral project” that is not limited to a book-length piece of scholarship for future publication. Additionally, they revamped the foreign language requirement. While I had to prove a reading proficiency in two foreign languages by translating pieces of theory into English during a timed exam, students now must provide evidence that they are striving towards proficiency in one additional language, which might include enrolling in a foreign language class and receiving a passing grade. They also reimagined the field examination so that it is no longer an oral test one takes after being sent off, without much oversight, to read over 100 books pertaining to the area of study one wants to specialize in.

    2

    Let’s think about the math here: For an eight-week accelerated composition course, I would first have to design the course and set it up online for students, that’s already, conservatively, a forty-hour work load. Then, I would be in the classroom for three to four hours each week, which would also require around the same amount of time in class prep. I’d then have to grade essays and homework assignments, log grades, communicate with the department and administration about student progress and any problems, conduct office hours, and attend meetings and additional training sessions (which would be optional, but, of course, nothing is truly optional). Let’s say that this list of tasks amounts to eight more hours of work per week. So, by the end of the semester, I would estimate that the bare minimum of work I would do for one three-credit-hour course would total 168 hours. That means that I would be making around six dollars per hour. Then, if we factor in my twice-a-week commute from an hour away—a two-hour round trip—-at what point do we concede that I effectively make no money to feel like a good person who is helping the next generation become better citizens?

    3

    I concede that AI does seem inevitable in the corporate workplace as many employers in various industries increasingly encourage or demand that their employees use AI. I also concede that jobs involving computer programming and data annotation don’t suffer from a lack of human intervention. Perhaps, too, outside of a capitalist context, AI technology could shorten our work week and allow for universal based income. Yet, without the promise of a sustainable future involving less work without less pay, workers are training machines to replace them. Eventually, they will be left without a job and source of income.

    Thanks for reading! Consider subscribing to my Substack or following me on Instagram.

  • Everyone’s Generative AI Policy

    Everyone’s Generative AI Policy

    A College Course Class Policy Turned Manifesto

    Sunset in the Nob Hill Neighborhood of Albuquerque. Photo by me.

    This post originally appeared on my Substack in December, 2025.

    I haven’t stepped into a classroom since 2017, and now, preparing to teach a composition course this spring semester, I feel like I’ve time travelled. I missed teaching through COVID, with the rise of remote learning, and I never needed to think about how generative AI impacts classrooms and education.

    In thinking about it now, I’m finding myself a little overzealous, or what the kids might call “unhinged.” What should have been a paragraph about whether students are allowed to use this technology or not in the classroom, became a lengthy manifesto about how we need to resist harmful technologies and futures we do not want.

    I’m publishing this policy for a composition class I am teaching this spring semester because I think we should all be required to think about how and why we use generative AI.

    Thanks for reading! Subscribe for free to receive new posts and support my work.

    The Policy

    This policy is more of an essay or manifesto. It’s also a work in progress. After we discuss it in class, I may add or take away from it.

    I know it is atrociously long. Still, I hope you will read it through and deeply contemplate your use of generative AI in this class and any aspect of your life.

    Generative AI, different from traditional AI, is a type of artificial intelligence that creates new content—such as text, images, music, or data—by learning language patterns from existing, human-created examples. Whereas traditional AI follows explicit algorithms and rules to perform specific tasks (think: the chatbot that coordinates a customer service chat or call), generative AI distinguishes itself by innovating material, which is why it sounds human. With this distinction in mind, when I refer to AI in this policy, I am referring to generative AI.

    To sound like us, large language models (LLMs) are fed online content directly sourced from the internet. Our emails, YouTube videos, and Reddit and social media posts have all been used to train LLMs.

    AI companies, such as Scale AI, also employ humans, known as data annotators, who train LLMs.1 Typically, these workers are underpaid and exploited, living in the Global South. They perform tedious tasks, such as identifying visual data that the LLMs cannot recognize. For instance, a data annotator might sort images of pants, shirts, and accessories from a commerce site for hours. This has been well documented in articles and books.

    Humans are also needed to regulate violent, unsafe, or illegal content. Journalist Karen Hao has written for The Wallstreet Journal about workers in Kenya who have been traumatized by sexually and violently explicit images and videos while working to produce models like ChatGPT. And, since language and culture are always in flux, data annotators will continue to be needed to train and distinguish harmful content for LLMs into the foreseeable future.

    Unsurprisingly, considering LLMs learn from internet content, generative AI reproduces our culture’s racist and sexist ideas. In an article from Science News Today, the editors write: “In a strange and unsettling way, AI holds up a mirror to society. And in that reflection, we see not just our intelligence, but our inequality . . . AI can be racist. It can be sexist. Not because it chooses to be, but because we have not yet chosen to be better.” This learned bigotry impacts society as it proliferates harmful ideologies. For instance, when AI is used to sift through resumes, it discards non-white and non-male applicants.

    On top of reflecting back to us our own biases, AI has not yet learned how to distinguish facts from fictions. Its errors are called “hallucinations.” Instead of getting better as AI technology develops, though, these hallucinations are getting worse, and companies like OpenAI are unsure why.

    Recently, a lawyer used AI to file a court case, which was found to be riddled with hallucinations, including made-up cases.

    So, even though it may seem counterintuitive, using generative AI for school or professional work often means working harder, not smarter. To avoid a similar humiliation, you need to verify all AI claims, which means that using AI to research and write your papers often results in more work.

    Copyright and plagiarism issues continue to arise as well, since, again, LLMs need human-created material to learn from. AI reproduces our writing, art, music, voices, and physical appearances. Think, for instance, about AI bands on Spotify, AI film and television actors, and commercial designs sold, like T-shirts and tote bags, with AI-generated images printed on them.

    Scarlet Johansson recently threatened legal action against OpenAI when its chatbot stole her voice. They had based their chatbot on her character Samantha from the film Her, and she was understandably upset about it.

    Let’s not forget the negative effect AI has on the environment: The creation of expansive data centers across the globe comes with high carbon costs. These sites replace farm land, neighborhoods, communities, and ecosystems with industrial sites that suck up resources and produce tremendous levels of noise and light pollution. In drought-stricken Argentina, for example, local activists vehemently object to the staggering amount of potable water these data centers require, which takes away from the local drinking water.

    Another harm proliferated by generative AI includes the dissolution of human-to-human connection. Think about a situation in which a teacher creates her Composition I course entirely with the help of AI. In the class, the students write papers, which they use AI to write. Then, the teacher grades those papers using AI.

    What would be the point?

    You might be asking: If AI is so bad, why do we have it? Who benefits from AI?

    As Karen Hao argues in her book Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI (2025), billionaires in Silicon Valley lead a for-profit AI industry, amassing their wealth and prestige at the world’s expense. If we define “colonialism” as a wealthy entity’s control over resources as it expands its authority into other territories (which companies like Google and Meta do as they outsource low-wage labor and mine precious minerals and resources for computer chips and data centers), then Hao is correct that US-based generative AI companies clearly position themselves as neocolonialists.

    Generative AI CEOs like Sam Altman promise us that the benefit of AI outweighs the cons. He has made claims that AI is the key to administering universal basic income (UBI) once AI takes over our work, decreasing our need for employment. Instead of launching a company that would help him realize this future, though, he launched Worldcoin, a company that collects people’s biometric data—using an iris scan—in exchange for cryptocurrency.

    In other words, instead of tilting the world toward betterment, Altman and other Silicon Valley AI tech CEOs create dystopian applications that surveil us and collect our personal data, which they then sell to advertisers and state governments. These technologies also advance facial recognition capabilities, used to police citizens, monitor political dissent, and enable the activities of ICE. If you are unfamiliar with the company Palantir, I suggest looking them up.

    TL;DR, the bottom line is: Because OpenAI and enterprises like it usher in a new era of US imperialism that is neither good for the people nor the planet, LLMs are unethically produced and used toward unethical ends.

    So, when you use generative AI to write your paper, correct your grammar, or plug in correct MLA format, you are paying a high social and ecological price to do so. Is it really worth it?

    As I hope this course demonstrates, writing and communication are useful skills in all aspects of your life—personal and professional. Without good communication, for instance, we cannot forge deeper intimacies.

    Additionally, when we use AI, we also lose our confidence and sense of value in the things we produce through our own, unassisted labor.

    Perhaps most significantly, learning to read analytically and think critically for yourself may be the only defense you have while sorting the “real” from the AI scam. One of the biggest of these scams includes that we need to use generative AI to better ourselves and society.

    To navigate the road ahead, we need our critical thinking skills to really assess if a future with AI slop and technofeudalism is one we want. We also need these skills to find alternatives to harmful technological advancements. White it is not AI that is innately bad, it is the way it is wielded as a precious resource by the people in control of it, people who put capital over people and the environment. Finally, we need our critical thinking skills to determine how to resist and decolonize the technological and economic systems that greatly shape our lives.

    I acknowledge that AI, to some extent, will be part of our future and that it has the potential to be a helpful tool that could lead to a better society if the people and economic system behind it were truly non-profit and better regulated. At the end of her book, for instance, Hao cites researchers and activists in New Zealand developing AI technology for the non-profit purpose of preserving Māori language.

    But the imperialism of generative AI is not the only reason I want to discourage your use of it in this class.

    Frequently, in the workplace and in your communication with peers and colleagues, you won’t always have AI to rely on. There are many, many contexts in which you will need to be able to write and think without it. 

    Do you really want all of your email exchanges and text messages with colleagues and friends to consist of LLM output on both ends? Again, what would be the point? 

    And while writing assistants, like Grammarly, can be great tools for proofreading, I want to see your errors. Your errors confirm your humanness. Additionally, I want to help you identify weaknesses, overcome challenges, strengthen your writing, and feel confident. None of these things can be accomplished with an assisting tool.

    After you establish your writing and analytical abilities, you may eventually turn to generative AI tools—your job may even require you to do so. But in this context, I want you to embrace that being a writer—whether a novice or expert one—is a process in which you fumble, grow, and always remain vulnerable to critique.

    Plus, how can I get to know you and your unique voice through the words of a machine? What value will my feedback have for you—or me—if it is directed to a LLM?

    In this class, the quality of your ideas counts the most, and you will never be penalized for struggling through the elements of the conventional college essay (including structure, format, style, citation, and grammar). I only ask that you work to improve these skills over the semester, letting go of the idea that mastery is the end goal.

    To summarize (and this is a joke!): There are more ethical ways to cheat than using AI.

    But, not joking: Cheating or plagiarism of any kind is not permitted.

    Additionally, any use of generative AI for any assignment or activity, unless designated otherwise, is considered misuse in this class. These and other types of misuse may be considered academic misconduct and consequences will follow college policies.

    1 I briefly worked as a data annotator for Outlier (part of Scale AI) after I was recruited through LinkedIn in 2023. At the time—and I believe currently, too—the company was looking for creative writers and people with advanced degress, particularly PhDs, to train LLMs. They lured me in with the promise of high pay—at $45 an hour—before dwindling that rate down to $15 (also before quarterly-paid independent contractor taxes, which subtracts about 15%). 

    Training included unpaid exams that you had to take and pass before work would be assigned to you. Then, the work was infrequent, and I often faced an “empty queue” of tasks. If I was assigned tasks, I had to work on them right away before they ran out, taken up by others eager to make money from the platform while they could. 

    Since we all worked remote and were assigned into ever-shifting teams of people—while hundreds of new employees were onboarded on a daily basis—we were alienated from our thousands of coworkers. Slack channels were filled with disgruntled and confused workers, often new to the platform, and the moderators of these channels were unhelpful. Our questions, critiques, and complaints led to more dead ends as the blind led the blind—we took it upon ourselves to respond to our colleagues posts on issues we also had little clarity on. 

    Algorithms assessed the quality of our tasks, monitoring how much time we took per assignment and how our work was rated by other people. The people who rated our work were other data annotators who didn’t understand the standards any better than the person they were rating. So, poor evaluations were not only demoralizing but rage-inducing as you could not contest, start a dialogue with your evaluator, or get any transparency from anyone about your poorly graded tasks. 

    Tasks were also timed, and the system often glitched—if it did, we would have to submit “tickets” to explain the glitch, using screenshots as evidence. If we forgot to track our own time and take screenshots, we wouldn’t get paid. 

    The tasks themselves were varied, but mostly I either rated LLM responses to human-generated prompts or wrote a response to a human-generated prompt as if I were the LLM. 

    Rubrics and policies changed continually, sometimes daily. Additionally, I was often bumped off and assigned to new projects that held similar but different tasks. So, every time I got the hang of a particular work flow, I was moved to a new project with different standards, rubrics, and an insurmountable learning curve that I’d never master.

    For example, one day I was told to use wordy, human-like greetings and other conversational fluff in the responses I would write, and the next day I would need to get straight to the point. Or, sometimes I would receive praise for crafting lengthy assessments of why I rated one LLM response higher than another and, in another project, I would be penalized for producing the same quality of work. Moreover, any mistakes you made while doing this work, with its constantly changing expectations and technical lingo, resulted in less tasks assigned to your queue. 

    It was a Kafkaesque nightmare run by people much younger than me who didn’t share or care about my field of expertise. Yet, somehow, even though they needed me more than I needed them, and even though they ran this platform inefficiently and unethically, they succeeded in making me feel useless and stupid. So, when they cut my pay rate by more than half of what I had been promised, that was the last straw, and I quit. 

    I also realize now, too late, that they were using people like me to circumvent blocked access to copyright-protected content. In other words, since using copyrighted novels, books, and databases with scholarly articles like JSTOR and ProjectMuse were forbidden, they used the authors of such content to generate new content for LLMs to copy from.

    Thank you for reading! Consider following me on Substack or following me on Instagram, @gina.yo.gina.