Automated Content Recognition: The big brother of creativity

1. Creativity Under Surveillance

Modern creativity lives under constant observation. Every video, sound, or image uploaded to most digital platforms is scanned by automated copyright filters before it ever reaches an audience. These tools –collectively known as Automated Content Recognition (ACR) systems– promise to protect copyright owners from infringement. In practice, they have become powerful, but invisible regulators of online content creation.

For advertisers and creators alike, there is a clear implication: success now depends less on legal compliance and more on technological compatibility. The challenge is no longer simply to follow the law, but to outsmart the algorithm that enforces it. In the digital environment, copyright enforcement has been quietly transformed from a legal to a computational issue, reshaping the boundaries of creativity, compliance and market access.

2. The Rise of Algorithmic Copyright Enforcement

ACR technology became popular in the early 2010s as a pragmatic response to massive breach on user-generated content platforms. Systems such as the YouTube Content ID or Meta Rights Manager identify matches between uploaded material and databases of registered works. When a match occurs, the platform may automatically block, monetize or mute content, depending on the decision of copyright holders[i].

At first, the use of such technologies was only conceived as a tool for protecting copyrighted content on large-scale platforms but the automation of copyright enforcement has introduced a new logic: platforms now apply a technology test, not a legal one. The algorithm does not ask whether a use is fair, transformative or incidental; it simply detects a match and acts on it.

The result is what scholars have called over-blocking: licit or trivial uses of protected material are pre-emptively eliminated, while the user’s ability to challenge that decision remains limited. Exceptions such as citation, parody or incidental inclusion –cornerstones of creative freedom– are not only virtually invisible, but irrelevant to the code[ii].

3. The Legal vs. the Technological Battle

The spread of ACR systems has reoriented the way in which creators, advertisers and even legal teams think about compliance. Before, success depended on navigating the complexities of copyright law. Today, it depends on understanding how the platform filter “thinks”.

Creators adjust sound frequencies and edit images to evade detection. Marketing teams buy redundant licenses “just in case.” Agencies modify the artwork or choose replacements generated by AI to avoid overlaps with existing content. None of these decisions responds to a legal need: they are acts of algorithmic self-defence[iii].

This change has transformed copyright from a normative issue into a technical one: whoever understands the system wins the war of visibility. The practical result is a “technological chilling effect”. Creativity is not held back by fear of demands, but by the opaque logic of automated moderation.

For the in-house legal adviser, this means that traditional intellectual property manuals are no longer sufficient. Risk management in the digital economy requires both legal and technological fluidity. Companies that dominate both will not only meet but remain visible in markets increasingly dominated by algorithms.

4. Governance and Accountability: Who watches the watcher?

ACR systems operate with minimal external oversight. There is no legal framework that defines the standards for technology accuracy, reporting obligations or resources available to users to challenge decisions made by platforms. Rights holders can register reference files without verification; algorithms can generate false positives with no consequences.

This dynamic has produced a privatized form of copyright enforcement in which platforms act as regulators, algorithms as judges and users as defendants. Meanwhile, governments have even gone so far as to promote the use of such technologies as the European Union has done through the Digital Markets Act.

For companies, the consequences are tangible. A mistakenly blocked campaign can generate lost revenue, reputational harm, and breach-of-contract claims. An over-zealous takedown may distort competition if one brand’s content remains online while another’s disappears.

To mitigate these risks, corporate legal departments could:

• Audit platform policies to identify how ACR systems operate.
• Include ACR contingencies –such as compensation or re-posting rights– in marketing contracts.

5. The Human Role in a Machine-Moderated Market

Automation was supposed to facilitate copyright enforcement. However, it has introduced new layers of uncertainty because creativity cannot –at least for the moment– be interpreted by code or algorithm.

Human oversight remains essential, not only to correct errors but to interpret culture, humor, and intent. A meme, a parody, or a cinematic reference can be illegal to an algorithm yet lawful and socially valuable to a human reviewer.

In this context, it is important to implement hybrid compliance models that combine legal expertise with an understanding of how these platforms work. There has never been a greater need to integrate intellectual property lawyers into the marketing and product design teams; train creators to assess risk at the storyboard stage or evaluate the implications of using AI tools before publishing content.

Such investments may seem more expensive in the short term, but they ensure brand resilience and integrity. Understanding the functioning of platforms will ensure not only greater reach to target audiences, but better management of intellectual property rights and risk prevention.

[i] Lester, T., & Pachamanova, D. (2017). The Dilemma of False Positives: Making Content ID Algorithms more Conducive to Fostering Innovative Fair Use in Music Creation. UCLA Entertainment Law Review, 24(1).

[ii] Carruitero, S. (2023) El sistema de Content ID de YouTube frente a la excepción de uso honrado del derecho peruano. dissertation.

[iii] Guzman-Zavaleta, Z. J., & Feregrino-Uribe, C. (2016). Towards a video passive content fingerprinting method for partial-copy detection robust against non-simulated attacks. En PLOS ONE, 11(11).