The recent plagiarism investigation involving Harvard President Claudine Gay presents an opportunity to reassess our understanding of plagiarism. This reevaluation becomes particularly pertinent in an era increasingly dominated by AI-driven writing, coupled with a not-so-distant past in which plagiarism was treated less stringent. The Harvard case serves as a microcosm of a broader discussion about the evolving nature of intellectual creation and ownership in the modern world.
In this digital age, where information is ubiquitous and easily accessible, the lines defining plagiarism are becoming increasingly blurred. The advent of AI and large language models (LLMs) has further complicated these definitions. These technologies, by their very nature, amalgamate vast amounts of existing work to create something new, thereby challenging the conventional boundaries of originality and authorship. The case of Claudine Gay, therefore, is not just about the alleged academic misconduct of one lazy or sloppy individual working in an intellectually vapid subfield but reflects a larger, more complex conversation about how we define and value originality in a rapidly changing intellectual landscape.
The onset of AI, particularly LLMs that synthesize vast amounts of existing work, signals an unavoidable and perhaps even long-needed paradigm shift in writing. This transformation demands a reevaluation of what constitutes plagiarism in a rapidly evolving digital landscape. AI-driven writing tools, by design, parse and recombine existing texts, creating outputs that, while new in composition, are derivative in origin. These abilities force us to confront fundamental questions about the nature of creativity and originality in the digital age.
Furthermore, as AI writing tools become more sophisticated and widespread, they are increasingly used in academic, journalistic, and creative writing. This widespread adoption is forcing educators, publishers, and legal experts to reconsider the frameworks that have traditionally governed plagiarism. The challenge lies in distinguishing between AI-assisted writing, which is a legitimate tool for idea generation and refinement, and outright plagiarism, which undermines the principles of academic and intellectual honesty. Perhaps, having been so fully absorbed by the former, we’ll eventually decide that there’s no such thing as the latter.
Historically, the concept of plagiarism was not as rigidly enforced as it is today—Laurence Sterne drew liberally on Robert Burton's Anatomy of Melancholy when he was writing Tristram Shandy and immense stretches of Coleridge's Biographia Literaria were cribbed from the German philosopher Friedrich Schlegel, to give two examples of great works that straight-up stole and then improved on source material. In many cultures, the reproduction of knowledge and ideas was seen as a tribute to the original thinker, rather than a theft of intellectual property. The act of borrowing and building upon existing ideas was often considered an integral part of the learning process and a scholar’s intellectual development.
In the realms of literature and philosophy, many classic works were created through a process that involved extensive borrowing from or glossing previous texts. Authors and thinkers often didn’t cite their sources outside of a handful of ur-thinkers like Aristotle or Kong Fuzi, as the concept of intellectual property wasn’t as defined as it is today. This approach wasn’t limited to literature; many scientific and philosophical advancements were made through collaborative and iterative processes that involved reusing and building upon existing knowledge.
The fixation on plagiarism, particularly in high-profile cases like that of Claudine Gay, raises questions about the effectiveness and fairness of such scrutiny. A hyper-focus on the technical aspects of citation and originality can overshadow the substantive value of academic and intellectual contributions—which could very well be nil, in the case of a majority of scholars who have hastily cobbled-together shitty dissertations and monographs (often paying a hack like me to do it for them!)
Moreover, the punitive approach to plagiarism often fails to consider the context and intent behind the alleged misconduct. In many cases, what’s labeled as plagiarism may be the result of poor academic training, lack of awareness about citation norms, inadvertent omission of sources in a complex research process, or deficient reading and writing skills (many such cases). A more nuanced understanding of plagiarism would recognize these factors and focus on education, correction, and early deterrence—driving people out of school as soon as possible, given that academics have been overproduced almost from the day Johns Hopkins issued its first PhD—rather than punishment.
Using plagiarism allegations as a tool to oust individuals from positions of power or influence, as seen in Joe Biden's 1988 election campaign, is a strategy that focuses on technicalities rather than substantive arguments or qualifications (Biden’s borrowings, in speeches or law school writings, should tell you he’s a mediocre thinker forced to grasp at straws left behind by others, not a “thief” of “intellectual property”). This approach can have detrimental effects on the quality of public discourse and academic inquiry. It encourages a culture of nitpicking and fault-finding, rather than a constructive evaluation of ideas and policies.
Furthermore, this strategy can be used selectively to target specific individuals or groups, undermining the principles of fairness and equity. It often leads to a disproportionate focus on minor infractions, overshadowing more significant contributions and achievements or probing critique of a work’s overall merits. This "gotcha" mentality—a cornerstone of the lazy “strength of weakness” approach preferred by hyper-partisans of all stripes—diverts attention from the real issues at hand and stifles meaningful debate and progress.
As AI continues to reshape the landscape of writing, expect to see further questions raised about the future of academic integrity and intellectual property. LLMs, trained on extensive corpuses of existing literature, so-called “plagiarism machines” that inherently blend and recontextualize existing ideas, blurring the lines between original and derivative work. This shift necessitates a rethinking of plagiarism policies to adapt to the new realities of AI-assisted writing.
The integration of AI in writing processes also offers opportunities for enhancing creativity and expanding the horizons of knowledge. AI can assist in uncovering connections between ideas and texts that might not be immediately apparent to human researchers, thereby enriching the intellectual landscape, and give less sophisticated writers an opportunity to turn crude but intriguing insights into formal discourse. However, this potential can only be realized if academic and legal frameworks are adapted to accommodate the unique characteristics of AI-generated content.
In light of the transformative impact of AI on writing and the historical leniency towards plagiarism, it’s clear that we need a fresh approach to allegedly purloined content. While maintaining academic integrity has its place in our society—probably not much of one, given the many problems afflicting or caused by those rich institutions—the focus should be on the value and impact of ideas rather than solely on their provenance. The case of Claudine Gay serves as a reminder of the need to evolve our understanding of plagiarism to suit the complexities of the modern intellectual landscape. This evolution should aim at fostering innovation and insight, prioritizing the advancement of knowledge over the rigid enforcement of increasingly obsolete norms related to copying-and-pasting or citing chunks of text.
