Systemic Misrepresentation: IMDb’s AI Summaries Miss the Mark on Film Editing

IMDb's new AI-generated summaries and sentiment markings consistently misrepresent the art of film editing. They frequently misattribute creative choices, fail to grasp how filmmakers collaborate, and, most critically, they assume credit or blame for specific roles instead of simply reporting what reviews say. This isn't a small mistake; it's a fundamental misunderstanding of how movies are made. Film editing is often called "the invisible art" for a reason: great editing often goes unnoticed, seamlessly guiding the story, highlighting performances, and shaping emotions. But it's exactly this subtlety, and the complex reality of film production, that IMDb's AI seems unable to grasp. A Clear Pattern: Unfair Blame and Missed Praise A close look at how IMDb's AI assigns sentiment to editing reveals a troubling trend: it's quick to assign negative sentiment (often by implying blame), yet frequently fails to recognize and praise excellent, even award-winning, work. I examined the ten most recent films nominated for Best Editing at the Academy Awards. Shockingly, only one received any explicit editing sentiment at all in its IMDb AI summary—and it was marked as 'Editing – negative'. This stark lack of positive recognition, combined with direct criticism, speaks volumes. Let’s look at some specific examples of this problem: Killers of the Flower Moon (2023 nominee): Edited by the legendary Thelma Schoonmaker, a three-time Oscar winner, this film was the only recent Best Editing nominee to get any direct editing sentiment—and it was 'Editing – negative'. The AI summary states: “Reviewers say the film suffers from excessive length and poor pacing… drawn-out dialogue, irrelevant subplots, and a lack of impactful editing.” Criticisms like “excessive length” or “irrelevant subplots” are usually about the script or director’s vision. Editors work with the footage they're given; they don't alone decide how long a film is or write plot points. Crucially, reviews often discuss general "pacing" or "length" without directly mentioning "editing." Yet, the AI jumps to assign blame to "editing," showing it doesn't understand the real causes in filmmaking. Justice League (2017) & Zack Snyder's Justice League (2021): The original Justice League (2017) was heavily criticized for its inconsistent tone and uneven pacing—due to a director change and extensive reshoots. You might think IMDb's AI couldn't possibly blame the editing for this. Oh, ye of little faith. Of course it can! Editing sentiment – 'Negative'. Then, for Zack Snyder's Justice League, a film many fans praised as a triumph of re-assembly, the IMDb AI gives it a 'Neutral' sentiment marker for editing. This is the only "neutral" I've come across, I might add. This "neutral" stance is astonishing for a film that created strong opinions with its four-hour length and unique rhythm, which were intentional artistic choices. The AI focuses on length as an "editing failure" or stays ”neutral," even when the edit is considered superior to the previous iteration, missing the enormous editorial challenges and artistic decisions involved. Again, it assigns blame or indifference where reviews might simply mention overall "length" or "pacing" without pointing specifically to the editor. Apocalypse Now (1979/Redux 2000): Another example of the AI failing to appreciate iconic work. Walter Murch is one of cinema's most respected editors, whose groundbreaking work on Apocalypse Now defined modern sound design and earned him an Academy Award nomination for Picture Editing (winning for Sound). Despite its huge impact, IMDb's AI summary includes a 'Negative' sentiment marker for editing, especially linked to the "Redux" cut's longer runtime. This criticism misinterprets intentional artistic decisions about length and structure as "poor editing," failing to see the difference between various versions and showing the AI's inability to recognize masterful, often unconventional, editorial choices that prioritize artistic vision. Mad Max: Fury Road (2015): This film won the Academy Award for Best Film Editing, with its editor Margaret Sixel widely praised for her groundbreaking work in creating its relentless, yet remarkably clear, action. Critics raved about its "kinetic energy," "relentless pace," and "masterful visual storytelling"—all directly thanks to the editing. Yet, the IMDb AI summary for Mad Max: Fury Road does NOT include 'Editing - positive' sentiment. As if the edit never happened. This clearly shows that even when editing is not just excellent, but award-winning and essential to a film's highest praises, the AI simply misses the connection, failing to credit the craft that produced the acclaimed outcome. Oppenheimer (2023 winner): Reviews often describe Oppenheimer's pacing as "fast" or "jumpy," with "confusing transitions"—results directly linked to Jennifer Lame’s Oscar-winning editing work, which masterfully navigated its complex, non-linear timeline. Yet, the AI assigns no explicit editing sentiment here, failing to connect these observed qualities to the editor's deliberate choices or to credit her crucial role in shaping the film's unique rhythm and structure. This consistent tendency to blame editing for problems that originate earlier in the process, or to ignore its profound positive impact, clearly demonstrates the AI's flawed approach. Superficial "Praise": When Editing Is Noticed, It's for the Obvious Even in the rare instances where editing gets positive sentiment marker in these AI summaries, the praise often focuses on obvious, visible stylistic choices, rather than a deeper understanding of editing's nuanced contributions: Requiem for a Dream is praised for “time-lapse,” “split screen,” and “extreme close-ups.” However, time-lapse is a visual effect, and extreme close-ups are camera choices, not purely editing techniques. The AI only notes the visible stylistic elements, failing to acknowledge the deeper structural genius of Jay Rabinowitz's actual editing work. Snatch is credited for its “quick cuts,” “freeze frames,” and “stylized transitions.” While these are valid editing techniques, the praise focuses on their visual prominence, not on the underlying narrative structure or rhythm that editing also delivers. Consider Whiplash (2014) and La La Land (2016), both Oscar winners for Best Editing. While their AI summaries often contain positive editing sentiment highlighting "fast pace," "tension," or "seamless transitions," the praise tends to focus on rapid cutting or visually apparent transitions. The AI often overlooks the subtle editorial craft that builds character, maintains coherence, and shapes emotional arcs when cuts aren't strictly tied to a beat or overtly stylized. These examples reinforce the pattern: IMDb’s AI primarily notices editing when it’s visually prominent or "loud." It rarely seems to "hear" when it’s quietly doing everything right, especially the fundamental work that shapes character, theme, and pacing in a less obvious way. Or when the editing creates thrill, suspense, or expertly assembles action sequences. In these cases, the AI gives positive sentiments to the outcome but fails to understand the depth of the editor's contribution, confusing effect with cause. Editors Inherit the Work of Many Others Editing doesn’t happen in isolation. By the time footage reaches an editor, it carries the imprint of everyone who came before—writers, directors, actors, cinematographers, designers, producers, and more. Editors work with what exists: the approved script, the chosen camera coverage, the captured performances, and the compromises made on set. In today’s workflows, editors receive all the takes—the good, the bad, and everything in between. Consider cinematography, often consistently praised in IMDb’s summaries with sentiment marker ”positive”. But what happens when visual priorities come at the expense of clear storytelling or efficient pacing? These choices might yield striking images, but the editor is left to rebuild coherence with limited material. If the final film feels disjointed or drags, cinematography might still be praised for its "look," while the editor is implicitly or explicitly blamed for issues that originated earlier. The same applies to overly long scripts, visually interesting but editorially chaotic blocking, or performances captured without enough camera coverage. Editors handle all of it. We spend countless hours navigating other departments’ choices, trying to shape something coherent and engaging. And when those upstream choices strain the final result, editing still gets the blame. The AI, however, by isolating 'editing' for criticism, completely bypasses this intricate web of interdependence, wrongly assigning blame without understanding the root cause. The Contradictions Are Impossible to Miss One of the clearest signs of the AI's limited understanding is its consistent failure to apply explicit sentiment tags for "script" or "writing"—even when reviews clearly point to issues with dialogue, plot structure, or character development. Instead, when these flaws lead to perceived "excessive length" or "poor pacing," the AI defaults to assigning negative sentiment to "editing," thus reinforcing its flawed understanding of cause and effect. When an AI says a film’s editing is poor because it’s “too long” or “dialogue-heavy,” it is wrongly assigning authorship and cause. These are decisions made in the script and by the director—often long before the editor steps in—and frequently, the director fights hard to keep it that way. Moreover, a fundamental logical flaw emerges when the AI blames editing for a film being "too long" or having "bloated dialogue," yet the same film is simultaneously described as "thrilling," "suspenseful," or boasting "great action sequences." If a film effectively achieves these powerful and emotional impacts, its editing, by definition, cannot be poor. Good editing is precisely what enables clarity, tension, and dynamic flow, regardless of overall runtime. The AI fails to grasp this intrinsic link between effective storytelling and effective editing. Editors spend countless hours fighting to streamline cuts, tighten momentum, and clarify bloated material. We flag problems. We suggest trims. We propose off-screen dialogue fixes. We even ask for reshoots when something crucial is missing. Editors make these suggestions constantly. But if the director “doesn’t like ADR,” or the producers won’t approve reshoots, that’s the end of it. We work with what we have. And if we lose the argument? The AI summary simply states 'Editing – negative'. Unless you’ve seen the raw footage and understand the production process, you simply cannot determine the true cause of such issues. We’re fighting the good fight every day - but we don’t always win. IMDb Can Do Better—and Should Here’s how IMDb can improve its AI-generated summaries to accurately reflect the complex art of film editing: Crucially, the AI should report factual observations about review sentiment, without trying to infer credit or blame to specific filmmaking roles. For example, it could state: "Reviewers noted the film's pacing was slow" rather than "The editing was poor due to slow pacing.” Alternatively, at the very least: Don't assign negative editing sentiment for complaints clearly tied to script, direction, or other earlier creative or production choices. Apply positive editing sentiment when summaries praise pacing, suspense, flow, clarity, or emotional impact—qualities that editors directly shape and are critical to a film's success. For example, when a thriller is praised for its “intense, well-orchestrated action,” “edge-of-your-seat suspense,” and “constant thrill throughout”—these are precisely the results editing helps deliver, and should be attributed accordingly. Respect the craft even when it’s invisible. Maybe especially when it’s invisible. Use editing tags consistently and accurately, not just selectively when there’s perceived "blame." Why This Matters IMDb shapes how audiences understand who does what in filmmaking. When editing is only mentioned to point out failure—and never credited when it creates momentum, coherence, and emotion—the record becomes distorted. This perpetuates a profound misunderstanding of one of cinema's most fundamental and vital crafts. This isn’t about ego. It’s about accuracy. Editors are often the last people in the room trying to make the story work, often overcoming hurdles created earlier in the process. If editing truly is the invisible art, it’s time for IMDb’s AI to stop looking the other way. Sincerely, A Professional Film Editor