Introduction: Copyright law paradigm shift under algorithm reshaping
In the evolution of the digital content industry, the “Safe Harbor Principle” (Safe Harbor), the core exemption mechanism of copyright law, is undergoing unprecedented underlying deconstruction and paradigm shift. This principle was originally established in the early stages of the transition from Web 1.0 to Web 2.0 (represented by Section 512 of the 1998 Digital Millennium Copyright Act (DMCA) in the United States and the European Union's 2000 E-Commerce Directive). Its legal basis is that it is assumed that Internet Service Providers (ISPs) only serve as "passive storage" and "neutral transmission channels" for information1. According to this system design, as long as the platform promptly removes the infringing content after receiving a valid infringement notice from the rights holder (i.e., the "notice-and-takedown" rule), it can be exempted from direct or indirect liability for compensation caused by user uploads1.
However, with the full deployment of deep learning, collaborative filtering, natural language processing (NLP) and large-scale generative AI technologies, modern network platforms have long since broken away from their role as pure information intermediaries and have substantially evolved into “super distributors” and “invisible editors” in the digital content ecosystem 4 . Recommended algorithms (Recommender Systems) are not only a tool to improve the efficiency of matching information supply and demand, but also a coded expression of the platform’s will, business model and value orientation5. Driven by algorithms, the platform accurately distributes massive amounts of content by analysing user portraits, tracking behavioral trajectories, and extracting content metadata, in exchange for extended user stay time and a surge in advertising monetization7.
In this complex socio-technical context, algorithmic recommendations are completely deconstructing the "technology neutral" presumption that traditional copyright law relies on. The global judicial practice community’s review standards for platforms’ duty of care have shifted from a simple formal review (“whether a valid notice has been received and responded to”) to a substantive penetration of “algorithm operation logic, profit model and platform control capabilities”9. Especially at the critical node of 2026, the deep integration of generative AI and distribution algorithms has caused the form of infringing content to evolve from "physical transportation of complete works" to "probabilistic generation and implicit reproduction after large model training" or "AI-synthesized infringing fragments" 10 . This report will be based on the latest judicial adjudication, administrative regulatory regulations and technological development realities around the world (covering China, the European Union, and the United States), from core dimensions such as legal characterization, liability determination models, FTO (freedom to implement) compliance strategies, protection of specific industry objects, and algorithm transparency in the era of generative artificial intelligence, to conduct an in-depth practical penetration and reshaping analysis of copyright infringement communication liability under algorithm recommendations.
1. Qualitative changes in legal characterization: from “passive storage” to “active intervention”
One of the core defenses of traditional copyright law lies in the "Principle of Technological Neutrality", that is, if a technology or service has substantial non-infringing uses, the technology provider should not be held liable simply because the technology is used by some users for infringing activities. However, the in-depth involvement of algorithmic recommendation engines has caused an irreversible qualitative change in the role of the platform, and its legal characterization has jumped from a "passive network storage space provider" to an "active content intervener and architecture designer."
1\. The bankruptcy of technology neutrality defence and the redefinition of the role of platforms
At the technical operation level, algorithmic recommendation systems work through multi-dimensional feature extraction. This not only includes capturing the user’s demographic characteristics, historical browsing records, likes and comments, but also includes advanced semantic analysis of the content, such as video frame extraction, audio fingerprinting, natural language sentiment analysis, etc. 7 Under the mapping of legal practice, this automated accurate matching process based on big data is no longer regarded as a simple "automatic access, caching or information positioning", but is characterized as the "selection, organization, arrangement, weighting and presentation" of massive user-generated content (UGC)11.
When the courts review such cases, they are increasingly penetrating into the "design purpose" and "commercial logic" of algorithms. For example, if the original intention of the algorithm is to increase user stickiness through weighted recommendations for content with high completion rates and high interaction rates (and such content statistically often contains a large number of unauthorised popular film and television clips or sports event highlights), then this mechanism will objectively amplify the scope of dissemination and damage consequences of infringing content12. In the United States, although Section 512(c) of the DMCA provides a safe harbor for "storage due to user instructions," the U.S. Copyright Office and courts pointed out when re-examining this provision that if the platform provides advanced algorithmic services that go beyond pure storage and systematically promotes specific content to obtain direct economic benefits, its safe harbor status will face severe challenges2. This means that technology neutrality is no longer the “gold medal” for algorithmic black boxes.
2\. Practical Evolution of China’s Internet Courts: Legal Mapping of Algorithms as Core Functions
China's judicial practice in this field is at the forefront of the world. The Internet courts in Beijing, Shanghai, and Guangzhou have clearly pointed out in many benchmark judgments that platforms use algorithms to accurately distribute content, which has actually crossed the line of simply providing space services.
According to relevant statistics, between 2021 and 2025 alone, the Beijing Internet Court has accepted more than 83,000 online copyright infringement cases. Among them, platform copyright cases are mainly disputes over information network dissemination rights, and the proportion of cases covering new productivity elements such as artificial intelligence is rising14. In many typical cases, the court analysed in detail the infringement logic of algorithm recommendations. For example, in a typical case involving artificial intelligence heard by the Beijing Internet Court, the court determined that the defendant's product design and algorithm application were not only neutral tools, but actually encouraged and organized the creation of the avatar involved in the case; the algorithm directly determined the realization of the software's core functions, so the defendant could no longer be regarded as a neutral technical service provider, but should bear direct or contributory liability for infringement as a network content service provider15.
The Guangzhou Internet Court also adopted a substantive penetration standard when dealing with the boundaries of liability involving algorithm recommendations. The court pointed out that when an Internet service provider makes algorithm recommendations, its role has changed from a "neutral channel" of information to a "gatekeeper" controlling the flow of information9. With the iteration of technology, the platform's ability to review infringing content has significantly improved, and it can use a variety of review technologies such as content metadata indexing and algorithm identification to filter infringing content. Therefore, the court believed that based on the perspective of utilitarianism and law and economics, allocating more stringent risk management and control obligations to algorithm recommendation service providers is consistent with their capabilities and commercial benefits. This upgrade of the duty of care is not "impossible"9.
3\. Upgrading of the duty of care from the perspective of comparative law: EU DSA and German Störerhaftung system
In the EU, legislation and judicial practice have simultaneously promoted the upgrading of platform responsibility. The formal implementation of the EU's Digital Services Act (DSA) has established systemic risk assessment obligations for very large online platforms (VLOPs) and very large online search engines (VLOSEs). The DSA clearly recognizes that the “toxicity” or rampant infringement of the online environment is by no means a natural by-product of Web 2.0, but is actively shaped by the algorithm design of the platform (especially the recommendation algorithm and profit model)16. Therefore, the DSA requires the platform not only to delete the content afterward, but also to examine whether its recommendation system, interface design (Design of functionalities) and other algorithm features increase the systemic risk of the spread of illegal content (including infringing content), and requires the adoption of commensurate technical mitigation measures16.
In German judicial practice, the Federal Supreme Court (BGH) has long regulated platform behaviour through the "interferer liability" (Störerhaftung) system18. This liability model with German characteristics stipulates that even if the platform itself does not directly commit infringement (not a direct infringer), it constitutes an "interferer" as long as it provides substantial causal convenience for the infringement activities and fails to perform its obligation to eliminate the danger after receiving clear notice18. With the advent of the algorithmic era, BGH has further clarified in a series of judgments in recent years (such as the series of judgments on platforms such as YouTube and Uploaded) that the platform's activated duty of care after receiving clear notification from the rights holder is no longer just to "delete specific links" (Take down), but must deploy effective algorithmic filtering mechanisms within a technically and economically reasonable range to prevent the same infringing files from being uploaded repeatedly (Stay down), and even prevent the proliferation of similar infringements on the platform18. This marks the European copyright practice community’s comprehensive confirmation of the “active intervention” nature of algorithm platforms.
2. The core model of liability determination: the practical distinction between direct infringement and contributory infringement
Under the algorithmic distribution model, the plaintiff's definition of the defendant's platform's liability type directly determines the distribution of the burden of proof in the lawsuit, the amount of compensation available, and the defence path for the platform's subsequent business model. The focus of current judicial practice is mainly on the in-depth extension of indirect infringement (contributing to infringement), as well as breakthrough claims on direct infringement in certain extreme technical scenarios.
1\. Indirect infringement and the expanded application of the “red flag principle” in the context of algorithms
At present, incorporating copyright disputes caused by algorithm recommendations into the analytical framework of indirect infringement (especially contributory infringement or subrogation infringement) is still the mainstream logic of the global judicial system. The core element is to determine whether the platform has "fault" - that is, whether the platform has "actual knowledge" or "constructive knowledge" in disseminating infringing content.
Expansion and Recalibration of the “Red Flag Test” The traditional “Red Flag Test” requires that the fact of infringement must be as obvious as a red flag waving in the wind, so that any rational ordinary person can easily detect it21. In the era of traditional manual review, when faced with massive amounts of UGC content, platforms usually avoid identification of red flag knowledge by saying that "the amount of data is too large to be reviewed one by one." However, in the era of algorithmic distribution, this logic has been completely overturned.
If the platform’s recommendation engine pushes works that clearly have high copyright attributes and high market realization value (such as popular theater movies and real-time live broadcast clips of well-known sports events) to the app homepage, hot search lists, or prominent locations through algorithmic weighting, the court is often inclined to conclude that “the red flag of infringement has been raised.” 2 For example, in the series of cases heard by Chinese courts against "Aiyouteng" against short video platforms, the plaintiff provided evidence to prove that the defendant platform not only allowed users to upload popular dramas that had been "cut into strips" (such as "Story of Yanxi Palace", etc.), but also used algorithms to identify users' viewing preferences and continuously and coherently When these infringing short videos are pushed to specific users, and even when the user comment area is filled with messages that clearly reflect the fact of infringement, such as "Watched an entire drama here," "VIP-free prostitution," etc., the algorithm not only fails to reduce the rights, but further increases the traffic allocation due to the high interaction rate12. In this case, the platform can no longer claim that the safe harbor is exempt from liability on the grounds that it “failed to receive the standard notice from the rights holder”. The court will directly infer that the platform has “should have known” fault based on the recommendation behaviour of the algorithm5.
However, in the American judicial system, the identification of “red flag knowledge” is sometimes relatively cautious. In the case *Ventura Content v. Motherless*, the Ninth Circuit Court of Appeals reiterated that even if a platform operator (such as the Motherless website) has "general knowledge" that "there may be a large amount of unauthorised infringing materials on its website", and even if it hires an independent contractor to conduct a manual spot check for a few seconds, this is not enough to constitute "red flag knowledge" under the requirements of the DMCA22. The court held that red flag principle 21 may be triggered only when the infringing nature of a specific, specific document is objectively extremely obvious (such as the widespread dissemination of a well-known singer’s MV as discussed in the Io Group case and the UMG case). This kind of judicial caution shows that in the field of non-key protected objects, the plaintiff still needs to bear a heavier burden of proof to establish an obvious causal link between the algorithm's general recommendation behaviour and the specific infringing content.
2\. Algorithm black box and subjective fault presumption of “knowing/should have known”
In practice, the powerful characteristics of the algorithm itself are becoming a powerful weapon for plaintiffs to prove the platform’s fault. Algorithms are not only tools for distribution, but also tools for content identification. Current mainstream platforms widely use advanced technologies such as MD5 signature collision, audio fingerprint recognition (such as YouTube's Content ID system), and key frame comparison for content duplication and copyright management23.
If the plaintiff proves in the lawsuit that on the one hand, the platform frequently uses these underlying identification technologies to accurately construct user portraits, commercialize and monetize targeted advertisements, but on the other hand, when faced with accusations of copyright infringement, it argues that it "cannot effectively identify and filter infringing content due to technical limitations." This contradictory logic will face a great risk of credit bankruptcy in court12. Chinese courts have repeatedly emphasized in their rulings that the principle of “technology neutrality” cannot be used as an excuse for platforms to be exempted from liability, and the development of new business models must not be at the expense of the legitimate rights and interests of others24. The platform's duty of care must increase with the improvement of its technical capabilities. When an algorithm has the ability to push content to the target audience "thousands of people", it will also legally have the ability to identify and prevent the spread of obviously infringing content. If the platform directly obtains direct commercial benefits from the recommendation of infringing content (such as accurately mounting the purchase link of related products under the infringing skit and drawing a commission), it is more likely to be determined to constitute contributing to infringement or even joint infringement4.
3\. Breakthrough attempts and judicial restraint in direct infringement: taking the U.S. Pinterest case as an entry point
Compared with the mainstream application of indirect infringement, in some extreme or deeply involved cases, plaintiffs' lawyers have begun to try to claim that the algorithm's in-depth recommendation or participation constitutes the "provision" or "dissemination to the public" of the work, thereby requiring the platform to bear more stringent direct infringement liability. This proposition is particularly acute in scenarios where algorithms are deeply involved in content generation or induced recommendations.
However, when faced with purely commercial recommendation algorithms, current mainstream judicial decisions still maintain considerable restraint. The case *Harold Davis v. Pinterest, Inc.* heard by the U.S. Federal District Court for the Northern District of California is an important benchmark for examining this issue25. In this case, professional photographer Davis sued the well-known social platform Pinterest, arguing that Pinterest not only allowed users to upload their copyrighted photography works, but more importantly, Pinterest used its unique algorithm to create "variants" of the work (i.e., resolution-adjusted or cropped versions automatically generated to optimize platform display), and used the algorithm to actively "select and display" these variants in the context of advertising display25. The plaintiff claimed that this kind of algorithm-based display and promotion behaviour has broken away from the category of "storage due to user instructions" and constituted direct infringement.
The court ultimately rejected this novel claim and granted Pinterest’s DMCA safe harbor defence in its entirety. The core reasons for the judgment are as follows: first, Pinterest automatically formats and optimizes (generates variants) the content uploaded by users for the convenience of user access. This is a reasonable technical process that has long been recognized by the court and is inevitably accompanied by "storage due to user instructions"25. Second, the plaintiff failed to raise any legal precedent to support its core theory - that is, "the use of algorithms to track user engagement or the use of algorithms to display advertisements near platform works constitutes copyright infringement"25. Third, the court found that Pinterest’s advertising algorithm did not specifically target Davis’ specific works, nor did the platform obtain any unique, directly attributable financial benefit (Financial Benefit Distinctly Attributable to the Alleged Infringement) from the specific allegedly infringing content. 25
This judgment sends a clear signal to the practical community: as long as the operation of the recommendation algorithm is for the conventional commercial purpose of improving the overall user experience of the platform and systematic advertising display, and the algorithm does not make substantial original editorial changes to the original work, nor is it specifically designed to promote specific infringing content to make huge profits, the judicial community still tends to regard algorithm recommendation as part of the basic service of a neutral platform, so as to remain extremely cautious in determining direct infringement and avoid a devastating blow to the existing Internet platform economy.
3. Core defenses in practice and key points of FTO (freedom to implement) review
For legal practitioners, they must have the ability to penetrate the "algorithm black box" when providing compliance consulting for technology companies or content platforms with large-scale distribution algorithms, issuing FTO (Freedom to Operate) reports, or organizing defence in infringement lawsuits. FTO analysis has traditionally been widely used to investigate infringement risks in the patent field29, but in the context of digital copyright in 2026, FTO also covers systematic "legal audits" of platform recommendation logic, filtering mechanisms and copyright compliance strategies29.
When dealing with algorithm copyright disputes, the core focus of FTO should always be on: how to find a dynamic balance between algorithm efficiency (maximizing traffic, highest user retention rate) and copyright compliance (strict filtering mechanism, minimizing risk of infringement compensation). This requires in-depth collaboration between the legal team and the research and development (R\&D) department to convert legal specifications into executable engineering codes31.
1\. Legal risk and compliance matrix based on algorithm characteristics
When conducting FTO analysis or litigation review of the platform, it is necessary to focus on dismantling the "technical details" of the following algorithms and establishing a corresponding risk identification and practical intervention matrix:
| Algorithm technical characteristics | Legal risk point definition and qualitative penetration | Practical compliance suggestions and FTO review focus |
|---|---|---|
| Collaborative Filtering & Tagging (Collaborative Filtering & Tagging) | The algorithm automatically tags content through natural language processing and clusters it based on user group preferences. This can easily be regarded as an act of “categorizing and editing” infringing content in law. If the labels automatically generated by the algorithm are obviously inductive (such as automatically aggregating "Watch the complete series without VIP", "Gun version of so-and-so movie"), it will directly prevent the application of the safe harbor principle and be regarded as the subjective malignancy of the platform4. | Establish a "Blacklist" of keywords for high-risk and popular works. Intervene in the tag system at the code level, set strict regular expression restrictions, and prohibit the algorithm from automatically aggregating, classifying, and making associated recommendations for tags that contain obvious infringement features or banned words31. |
| Weighted Recommendation & Trending (Weighted Recommendation & Trending) | This is the core method used by algorithms to increase platform activity. However, weighted recommendations greatly enhance the dissemination efficiency of infringing content and constitute a "substantial substitution" of the original work. When determining the amount of damages, the huge amount of exposure brought by the high weight will be directly converted by the court into evidence of "high benefits to the platform", thus becoming the legal basis for applying the maximum statutory compensation limit or even punitive damages9. | Implement a dynamic linking mechanism between traffic and copyright review intensity. Establish a "white list" and a more stringent copyright manual/machine review pre-review mechanism for high-weight accounts that enjoy algorithm-weighted treatment (such as MCN organizations, Vs with millions of fans). The higher the traffic recommendation position, the higher the copyright care obligation that the platform must bear. |
| Auto-generation & Summarization (Auto-generation & Summarization) | In order to increase the click-through rate, the algorithm will automatically intercept the most visually impactful key frames in the video as the cover, or use a large model to automatically extract text summaries. This operation is extremely risky and may be characterized by the court as "modification", "editing" or even "adaptation" (Derivative Work) of the original work, thereby crossing the pure transmission boundary and triggering direct infringement liability10. | Introducing the concept of “Compliance by Design”31. The algorithm is restricted from performing operations that substantially create original changes to the original audio and video images. Ensure that automatically generated content is limited to factual indexes and avoid substantively replicating the core expressive elements of the work. |
| Personalized push for thousands of people (Pe |
rsonalization) | Personalized distribution greatly increases the difficulty of evidence collection for the plaintiff (rights holder), because the information flow seen by different end users is completely different; at the same time, platforms often use this as a reverse defence (that is, "the platform cannot foresee each user's unique information flow environment")33. | In terms of technical architecture, ensure that the recommendation engine has a built-in underlying copyright filtering layer, that is, filtering first, and then personalized distribution. When facing litigation and evidence collection, the plaintiff needs to use notarization and preservation methods to simulate test accounts with different interest tags to confirm that the algorithm generally has directional infringement push logic; the platform needs to retain recommendation logic logs for review. |
2\. Construction of algorithm audit (Algorithmic Audit) and machine-readable exit mechanism (Opt-outs)
In the increasingly stringent global regulatory environment in 2026, it is no longer enough to rely solely on "notice-and-remove" after the fact. High-end legal practice requires the platform to introduce independent third-party technical experts or a dedicated compliance team to conduct a pre-algorithmic audit (Algorithmic Audit) of the platform's underlying recommendation logic31.
Delineate the "Sacrificial Scope" When conducting FTO due diligence, the R&D department and legal advisors must jointly define the "Sacrificial Scope" of the algorithm32. This means that if a certain radical recommendation strategy in the algorithm that is used to maximize user retention time (such as the bottomless recommendation of controversial hot film and television strips) is evaluated as having extremely high copyright infringement or systemic social risks, the platform must compromise in the face of commercial interests and prepare alternative implementations of the function (Design-arounds) or completely remove the strategy, thereby cutting off the source of legal risks32.
Establish a machine-readable Opt-outs Facing the ingestion and distribution of massive content, platforms must mechanically prove that their recommendation algorithms are "copyright-friendly". This problem is particularly prominent when it comes to large-scale data capture and AI model training. In the case *Robert Kneschke v. LAION e.V.* heard by the German High Court of Justice Hamburg (OLG Hamburg) in December 2025, the court deeply discussed the applicable boundaries of the text and data mining (TDM) exception. The judgment pointed out that if the rights holder clearly publishes a "no scraping or data mining" statement in the terms of use of its website in natural language, and it is technically feasible to identify this natural language statement at the time when the scraping occurs, then this should be regarded as an effective and machine-readable opt-out mechanism, thus preventing the application of the TDM exception36.
This judicial development has strong guiding significance for the practical community. Before the platform designs an algorithm engine to capture and distribute wide-area network content, it must develop and deploy an identification module that can automatically parse the Robots.txt protocols, metadata tags, and natural language statements in terms of various websites. Giving sufficient respect and concessions to copyright protection statements in code logic is the last line of defence to build an effective defence for algorithm neutrality.
4. Industry Special Cases: Responsibility Boundaries of Special Copyright Objects under Algorithm Distribution
In practice, algorithmic recommendation is not an abstract unity, and the legal liabilities arising from it show significantly differentiated characteristics based on the object attributes, industry model and market value of the distributed content. This difference in objects directly affects the court’s delineation of the platform’s “should-know” standards and the boundaries of the duty of care in individual cases.
1\. Sports Law: Extremely high timeliness and the normalization of pre-litigation injunctions
The commercial value of live sports events and related audio-visual programs is extremely dependent on their "real-time" and "on-site unpredictability". Once the final whistle blows, the replay value of the game will plummet. Under the algorithm distribution model, if the platform uses a powerful recommendation engine to push unauthorised illegal slices of the event, key goal animations or unauthorised derivative short videos to users at high frequency and in real time during the game, this behaviour is not only a gross infringement of the event broadcasting rights (or the copyright of audio-visual works), but is also objectively regarded by the court as extremely subjectively malicious and destructive21.
When dealing with algorithmic infringement cases in sports events, the traditional "notice-and-takedown" mechanism appears extremely lagging and ineffective—by the time the rights holder sends a notice and waits for the platform to review and delete the case, the game is often over, and irreparable traffic loss and economic damage have been caused. Therefore, in current practice, for large-scale algorithmic infringement of sports events, the core weapon of rights holders is to apply for pre-litigation behavioral preservation (injunction/injunction)**21. When reviewing an application for an injunction, the court will focus on the speed of fission dissemination of infringing content caused by algorithm recommendations and the degree of immediate damage to the interests of broadcasters. In many judgments, courts tend to issue "dynamic injunctions", requiring platforms to not only immediately stop recommending specific known links, but also to implement active algorithmic pre-filtering and interception of relevant event keywords, specific team names, and even video key frames during the live broadcast of the event, which greatly increases the platform's censorship obligations38.
2\. Popular film and television dramas: algorithmic depth and punitive damages for “substantial substitution”
In a series of cases in which long-form video streaming giants (such as China's "Aiyouteng") sued short-video content aggregation platforms, the focus of judicial debate has shifted from the early "whether there is copyright infringement" to "how the depth of algorithm recommendations affects the determination of the amount of damages."
On the short video platform, a large number of users are keen on "cutting" and "carrying" dozens of episodes of popular film and television dramas, and providing them with AI-synthesized electronic audio commentary (i.e. "taking you through a certain movie in a few minutes"). When the platform's recommendation algorithm keenly captures the extremely high completion rate and retention rate of these fragmented content, and then pushes it coherently and systematically to the user's recommendation information flow on the timeline, the algorithm actually completes a "substantial replacement" of the original long video work. Users have satisfied their consumption needs for the core plot of the series on the short video platform, thus completely losing the motivation to go to genuine streaming media platforms to pay for subscriptions or watch the original film12.
In practical judgment, if the plaintiff can prove through background packet capture, notarial evidence collection and other means that the platform algorithm not only failed to filter such content, but instead conducted "deep intervention" and "systematic distribution" based on popularity, the court will not only completely deny the platform's defence based on the "safe harbor principle", but will also be inclined to impose severe economic sanctions. In many landmark judgments, the court has broken the upper limit of statutory compensation stipulated in the current copyright law, and directly determined high compensation based on the estimated advertising revenue obtained by the platform through this part of the infringing traffic, or the VIP membership subscription fees lost by the plaintiff. The recommendation depth of the algorithm is directly equated with the intensity of punitive damages4.
3\. Map data (GIS) and algorithm distribution: the boundary between factual description and illegal data processing
In addition to entertainment content, the copyright and compliance responsibilities of algorithm recommendations in certain professional data fields have become more complex and intertwined. Map data and geographic information systems (GIS) are typical representatives.
In Internet electronic maps and local life recommendation services, algorithms must distribute massive point-of-interest (POI) data, business location information and navigation routes to users every day. This often triggers disputes between brands regarding trademark infringement or underlying database copyright infringement. China's Guangzhou Internet Court and Beijing Court have established important practical standards in related cases: Internet map service providers' algorithmic labeling and distribution display of geographical information (such as business names, locations, navigation data) is a factual description indicating the existence of a certain geographical location in the real world, which is necessary to provide map services. It is a typical "non-trademark use" or reasonable utilization of factual information and should not be easily identified as infringement39. It is unjustifiable for brands to attempt to transfer the infringement behaviour of offline merchants to the map platform through the directivity of the map algorithm to claim joint infringement. 39
However, this exemption is not without boundaries. In other cases, the court pointed out that if the recommendation algorithm of the platform has serious flaws and the sensitive geographical location, travel trajectory and other personal information of citizens are distributed without authorisation (for example, the information about a user's frequent visits to specific medical institutions is incorrectly associated and exposed through the algorithm), this constitutes illegal processing of personal information rights40. A deeper copyright risk is that if autonomous driving or navigation algorithms, without authorization, large-scale capture and reverse engineering of the underlying structure of other people's copyright-protected high-precision digital map databases are used for the training and recommendation of their own models, this will completely break the boundaries of fair use and face severe infringement accusations41.
4\. Architectural drawings and design drawings: Copyright penetration from physical space to virtual rendering
Architectural engineering design drawings and interior design renderings, as highly practical and professional copyright protection objects, face new legal challenges today as algorithm distribution and generative AI-assisted design tools (such as intelligent CAD systems, Midjourney architecture rendering, etc.) become increasingly popular.
First of all, in the traditional image sharing and content distribution algorithm, the German Federal Supreme Court (BGH) made very enlightening judgments in three cases involving "Photo Wallpapers" in 2024. In this case, copyrighted physical photo wallpapers were purchased and posted in private or commercial spaces (such as tennis centers, hotel rooms), and then photos of these spaces were taken and distributed and displayed by algorithms on social media or websites such as Facebook. Through the judgment, the BGH extended the concept of "implied license" from the purely digital field to the analog physical field. The court ruled that purchasing physical wallpapers and displaying them as backgrounds (or incidental works) in digital photos does not constitute an infringement of wallpaper copyright as long as it meets normal commercial and private use expectations. 43 This provides a valuable safe harbor for algorithms when distributing images containing physically copyrighted design backgrounds.
However, in areas involving core architectural drawings and AI-generated designs, German courts have established extremely strict standards. A number of recent rulings (including the Munich, Frankfurt and Düsseldorf courts) have unanimously stated: "The human's creativity must penetrate the black box of the algorithm"44. This means that if the user only inputs an abstract concept (such as "design a modern minimalist style villa") into the AI system, and the algorithm independently completes the arrangement of the building structure, load-bearing calculations and detailed drawing generation, the generated result cannot be protected by copyright law because the substantive originality of humans is not effectively retained in the final result. 44
On the contrary, at the level of infringement determination, if highly original CAD drawings or BIM models are uploaded without authorisation on design drawing sharing platforms (such as Pinterest or professional architectural forums), and the platform’s recommendation algorithm analyzes the download preferences of professional users and performs high-weighted targeted push of these pirated drawings, the practical risks are extremely high. Architectural design drawings have high commercial monetization attributes and a very narrow professional audience. The precise recommendations of the algorithm are not just eye-catching, but directly replace the commercial licensing market of the original rights holder (substantial market substitution)45. In this case, even if there is a contract defect, the Chinese court has emphasized that it must rely on the "Emperor Clause" of the civil law of good faith to prohibit the infringer from taking advantage of the convenience of algorithm distribution and maliciously lower the compensation standard on the grounds that the contract is invalid, and must give the rights holder protection that matches the market value of his drawings47.
5. The next deep water area: Generative AI (GenAI) reshapes content ecology and "single process theory"
The year 2026 is considered a key watershed in the evolution of copyright law. The large-scale commercialization of generative artificial intelligence (GenAI) and large language models (LLM) is completely reshaping the operating model of the online content ecosystem from the underlying logic. At this time, the results of algorithm recommendations are no longer just a physical stack of ready-made third-party independent works created by humans, but have evolved into a large number of "generative content" synthesized in real time by large models through deep learning. This kind of content may mix the characteristics of multiple original works and appear as an "AI-synthesized infringing fragment"10. This has brought unprecedented theoretical challenges and practical dilemmas to the traditional standards for determining liability for infringement, division of jurisdiction, and allocation of the burden of proof.
1\. Crisis in the application of the TDM (text and data mining) exception: the landmark case of GEMA v. OpenAI
In order to obtain powerful generation capabilities, the AI model must ingest and process a large amount of copyright-protected text, images, and audio works during the training phase (input side). In Europe, this behaviour triggered a fierce controversy over whether the "Text and Data Mining (TDM) Exception" stipulated in Article 4 of the EU's "Single Digital Market Copyright Directive" (CDSM Directive) can protect commercial-level large model training.
In November 2025, Germany's Munich District Court (Landgericht München I) made a landmark ruling in the high-profile *GEMA v. OpenAI* case, providing the first clear judicial response to this issue in the world. In this case, GEMA, the German music rights management agency, accused OpenAI of using copyrighted lyrics under its jurisdiction (such as 9 well-known German songs such as "Atemlos") to train the ChatGPT model, and when the user inputs simple prompts (Prompts), it outputs a lyric passage that is almost word-for-word identical to the original work48.
The court ultimately ruled against OpenAI and explicitly refused to apply the TDM exception to the training of large language models. The court’s reasoning was very penetrating: the traditional TDM exception aims to protect those temporary or auxiliary copies made solely for the purpose of “analyzing trends and extracting abstract facts and data patterns.” The core premise is that this analysis process will not threaten the commercial development interests of the original work itself. 48 However, under the technical architecture of generative AI, the model not only extracts information, but also permanently memorizes copyrighted works (such as the precise expression of lyrics) in the weight parameters of the neural network, and gives highly similar substantive output (Regurgitation/Hallucination) when triggered by the user. 48 The court held that this mechanism of internalizing protected expressions into the ability to generate models has substantially harmed the rights holder’s legitimate rights and interests, directly constituted “Reproduction” and “Communication to the public” within the meaning of copyright law, and exceeded the scope of protection under the TDM exception. 49
This judgment has greatly compressed the compliance space for AI developers and declared that "grabbing is fair use" has suffered a major setback in Europe, prompting developers to avoid infringing output by obtaining explicit authorization, paying licence fees, or building a more powerful filtering system51.
2\. The legal nature of “next token prediction” and CJEU’s review in the Like Company v. Google case
Echoing the clear statement of the Munich court, the global intellectual property community is currently focusing with bated breath on the case of *Like Company v Google Ireland Limited* (Case No. C-250/25), which is undergoing preliminary ruling at the Court of Justice of the European Union (CJEU). The case was filed by a Hungarian news media publisher, accusing Google's generative AI system Gemini (formerly Bard) of ingesting its online articles protected by copyright and Press Publisher's Rights without authorization, and directly displaying synthetic summaries containing the substantive content of the original articles in response to user questions (such as a request to summarize news about a celebrity's plan to introduce dolphins to Lake Balaton)52.
The core technical and legal point of contention in this case is whether the probabilistic output mechanism of the large language model based on "Next-token prediction" and "Retrieval Enhanced Generation" (RAG) constitutes "copying" or "communication to the public" within the meaning of copyright law. 55 The defendant Google argued that its system did not generate an exact copy of the original web page on the physical server, and the interface of the chatbot was also different from the traditional search engine results page. The text it generated was a factual response calculated and reorganized in real time through abstract mathematical probability and word model models, and should not be regarded as direct use of the original news work57.
However, the plaintiffs and many copyright scholars believe that practical review must not be blinded by the technical appearance of an algorithmic black box. Because Gemini combines multi-modal learning, context understanding and even enhanced retrieval (RAG) of real-time network content, its output results essentially reproduce and replace the core business value and information expression of the original news work55. If the final result of the algorithm weakens the publisher's traffic and advertising revenue, no matter what fragmented reorganization logic is used in the underlying code, it should be regarded as an infringement of the exclusive rights of the rights holder.
3\. "Single Process Theory" Deconstruction of Transnational Jurisdiction and Offshore Haven Strategies
At the six-hour CJEU hearing on March 10, 2026, the case triggered a more profound legal earthquake: multiple member states (including Hungary, Denmark, Greece, Spain and France) and some scholars supported a highly subversive "Unitary-process theory" in court. 52
This theory advocates that when evaluating the infringement liability of generative AI, the "input/training stage" of the AI model and the "output/deployment stage" that ultimately face the user should not be mechanically separated for review. Instead, the entire AI system operation from data capture, model training, bottom layer residency to front-end output should be regarded as an indivisible and organic single behavioral process52.
The proposal of this theory has strong practical relevance. In the past few years, many multinational AI giants have adopted an "offshore safe haven strategy" in order to circumvent the EU's strict copyright protection and data compliance laws. That is, setting up servers and placing the massive data-consuming model training process in jurisdictions with loose "fair use" exceptions (such as certain states in the United States or some Asian countries), and only deploying mature "clean" models back to the EU market to provide services. If the CJEU adopts or partially agrees with the "single process theory" in the final ruling (expected to be issued at the end of 2026 or early 202759), this will have devastating jurisdictional penetration consequences: as long as the AI system is eventually integrated into a search engine or recommendation flow within the EU and distributes the generated content to European citizens, the EU Court of Justice can assert jurisdiction over the entire underlying training and data ingestion process far abroad based on its final "dissemination to the public" behavior, and hold it jointly and severally liable for infringement52. This complete reshaping of the traditional territoriality principle of copyright law will make the territorial compliance firewall of global AI companies disappear, and will impose unprecedented stringent requirements on the formulation of FTO strategies.
6. Conclusion and practical outlook: finding a dynamic balance between algorithm efficiency and copyright compliance
Based on the above-mentioned in-depth penetration analysis, the explosive evolution of algorithm recommendation and generative AI technology has completely ended the golden era when Internet platforms retreated to "technological neutrality" and enjoyed immunity from liability. Through in-depth calculation of massive user data, covert arrangement of information flows, and real-time generation of synthetic content, the platform has transformed from a mere network infrastructure into a substantial content controller, agenda setter, and maximum benefit sharer in the digital society. This fundamental change in role objectively requires the global intellectual property legal system to reconstruct the applicable boundaries of the "safe harbor principle".
Judging from current judicial practice cases, whether a platform can continue to enjoy legal protection from immunity no longer depends on whether it superficially fulfills the mechanical "notice-and-takedown" procedure, but depends on whether the underlying architecture and value orientation of its algorithm design have sufficient good faith in copyright compliance**. Whether it is the substantial expansion of the “red flag principle” in the context of algorithms by Chinese and American courts, or the European Court of Justice’s active exploration of direct liability for infringement and the “single process theory” in cases involving generative AI (such as *GEMA v. OpenAI* and *Like Company v Google*), they all clearly point to an irreversible legal trend: in the era of the digital economy, the level of technical capabilities must resonate with the level of legal liability.
As a legal practitioner, in 2026, a deep-water area that is reshaping the content ecology, when providing strategic compliance consulting or designing FTO (freedom to implement) risk elimination strategies for enterprises, we must abandon the passive defensive thinking of pure after-the-fact remedies. The reach of compliance review must move forward to the underlying writing of algorithm codes, the directional design of web crawlers, and the cleaning stage of large model training data sets. A solid legal moat can be built by forcibly introducing the concept of "Compliance by Design", establishing dynamic human-machine collaborative filtering whitelists/blacklists, carefully delineating the "sacrifice scope" of radical algorithm functions, and fully deploying a machine-readable response mechanism to natural language "Opt-outs" on the technical infrastructure.
In the final analysis, in the face of the profound "algorithm black box", the defence path that attempts to shirk responsibility by hiding technical details has been gradually blocked by judicial practice. Only when the platform ensures that the underlying logic fully respects the intellectual property rights of creators and the transparency mechanism can withstand professional audits can its pursuit of algorithm efficiency and maximization of commercial monetization have a sustainable foundation of legitimacy. In the future, building a transparent and proportional responsibility review model that takes into account the incentives for technological innovation and the substantive protection of rights holders will be a core proposition for the global copyright law practice community that requires long-term in-depth exploration and game play.
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