The rapid rise of AI-generated content is reshaping how Digital Service Providers (DSPs) ingest, identify and attribute content. Traditional content identification systems were primarily built to recognise re-uses and transformations of existing works which maintain some detectable connection to a licensed source asset. AI-generated content can break this link.
As generative models produce audio (and/or) visual material that is novel and unattributed, DSPs face a complex challenge: how to detect AI content and correctly attribute content that no longer maps cleanly to a known asset? For rights-holders, licensors and auditors, this shift has far-reaching implications for contractual compliance, royalty flows and transparency around the usage of licensed works.
This article examines the critical shift from simple AI detection to sophisticated attribution models, exploring how DSPs must evolve their identification systems to ensure royalties are accurately allocated back to rights-holders as AI works transition from fraudulent anomalies to exploitable assets
AI content
In our last article “When Two Become One” we explored the impending challenges inherent in the algorithmic automation of composite works. In that context, the DSPs that offer such a service at least have direct knowledge of the source assets being used.
However, that is not necessarily the case in content utilising generative AI. When such content is created outside of DSP systems, platforms do not receive any guaranteed metadata linking this content to a known asset. This makes identification and attribution substantially more difficult.
The prevalence of AI-generated content supplied on DSPs is rapidly increasing. Such content may have been generated by a model that is opt-in and licensed or may be unlicensed and a potential copyright violation. Regardless, in both cases it is essential that the DSP can identify that content is AI-generated and, where exploitation of such content is permitted (rather than subject to takedowns), can correctly attribute any elements based on licensed works. AI content therefore poses a structural challenge for existing DSP content identification systems with downstream implications.
Detection versus attribution
In the near term, detection of AI content is likely to be the primary requirement for DSPs. Many rightsholders may prefer AI-generated works to be treated similarly to fraudulent or non-qualifying content; for example, being excluded from market-share calculations and not treated as royalty-bearing repertoire.
Over time, however, this stance may evolve. As more AI tools adopt licensed, opt-in training data sets, there may be a shift towards controlled exploitation of AI-derived works rather than automatic removal. In that scenario, platforms will require more than simply detection: they will need to attribute content, so that any economic value from AI outputs can be correctly allocated to the relevant label, distributor, artist or repertoire group.
There is precedent for such a transition. In the early days of User Generated Content (UGC) platforms, unauthorised uploads were often removed as standard practice. Over time, exploiting such content became more common, so long as it did not violate certain guidelines and usage rules. A similar trajectory could emerge with AI-generated content, particularly if the financial rewards become meaningful for those artists that do choose to opt-in.
If that happens, distinguishing not only whether content is AI-generated but also how it relates to licensed catalogues would have direct implications for royalty allocation and contractual compliance.
The breakdown of current asset matching
Reliable content identification is a crucial process for any platform which allows for the upload of UGC. Historically, DSPs have relied on fingerprinting, which maps audio/visual media into digital signatures which can be matched against reference assets supplied by rightsholders and/or distributors. This includes detecting music in a video, recognising segments of live performances and identifying scenes from audiovisual works.
However, these techniques rely on matching uploaded content to a known reference asset. AI-generated material presents a different challenge. Unlike covers or reposted recordings, AI works often have no reference asset for a fingerprint system to match against. Generative models create new audio distributions rather than re-purposing existing works. As such, these can imitate timbre and style without necessarily sharing audial similarity to existing catalogue works. As a result, DSPs may need new approaches to identification.
New detection and attribution models
To address these challenges, platforms are experimenting with next-generation identification and detection methods.
Firstly, some systems primarily focus on the detection of identifying synthetic audio. Some example approaches include detection systems trained to recognise specific AI model signatures or watermark-based approaches. These tools in-effect behave more like fraud-detection systems, flagging content for moderation.
Secondly, other systems are attempting to address the breakdown of existing content identification methodologies. SoundPatrol is an example, using novel fingerprinting techniques to capture tone, timbre and other stylistic patterns with the aim of matching AI-generated tracks back to the licensed works they resemble. If approaches like this mature and are adopted by DSPs, or supplement existing content identification systems, these could help to enable attribution of content, and thus economic flows to the underlying rightsholders.
Auditor and licensor takeaway
The increased proliferation of AI-generated content means new detection and attribution models are needed. Licensors should be mindful of how their commercial approach to AI content (takedown versus exploitation) affects the systems DSPs need to have in place to contractually comply (with detection most important with the former, and attribution also required in the latter).
Auditors must fully understand the contractual mechanisms and licensor’s commercial strategies for handling such content and determine the key risks to be tested (e.g. wrongful inclusion in market share denominators) prior to embarking on process understanding sessions with the relevant data engineering and reporting teams. Testing of known exceptions (e.g. known AI-derived content) can also supplement such conversations.
Whilst transparency and communication between DSP and licensor is always important, this is an external issue that affects both, and a collaborative approach in addressing these challenges will be of great benefit to both sides.
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