10th November 2025Hard Clauses, Soft Constraints: The Evolving Challenge of Auditing Algorithmic Influence on Digital Music Positioning

As contractual obligations such as reduced-royalty playlist boosts increasingly dictate which content is seen, DSPs, licensors, and auditors alike must grapple with a complex truth: enforcement of these agreements shifts from rigid financial rules to auditing aggregate outcomes within probabilistic systems.

The marketing of compilation and positioning

In the pre-streaming era, the music we consumed was compiled by a person (or a collection of people) ostensibly attuned to what would be popular with audiences. Compilation and recommendation are now largely driven by algorithms which are created to be attuned to what will be popular with audiences, via self-correcting probabilistic assessments of what will engage a particular user.

However, as demonstrated by Spotify’s Discovery mode, the content that is suggested to end users is influenced by more than simply the optimisation of engagement. This model allows content owners to accept a reduced effective royalty rate (via a commission mechanism) for a boost in the positioning of their content in algorithmic playlists and recommendations. This is just one example of the contractual rights increasingly being used to influence algorithmic outcomes in terms of placement and positioning, a development with significant implications for DSPs, licensors and auditors alike.

Algorithmic constraints

The algorithms we interact with on these services are designed with probabilistic optimisation techniques at their core. These experiment and refine, with a typical goal being to maximise the engagement of users, and the algorithm and associated execution shifts in accordance with this objective.

However, since maximisation of engagement isn’t the only objective, and under the hood hundreds (if not thousands) of micro-objectives compete, constraints are required to enable the algorithm to function. These will define acceptable trade-offs within hierarchies of hard constraints and soft constraints, which operate in different manners.

• Hard constraints are rules that must always hold and thus curtail the performance of an algorithm above all other considerations. Examples may include restricting the distribution of content that is explicit to children or restricting content that should not be available in a certain territory.
• Soft constraints are factors that guide the algorithm to an outcome but can be overridden by the primary objective (e.g. engagement optimisation). In the case of any sort of aggregate contractual condition (e.g. boost in market share) a clause of this nature may be overridden should the weighting of the probable engagement outcome of suggesting such a track (or repertoire) be sufficiently detrimental to engagement.

When contractual terms are imposed upon these algorithmic instructions, they join a complex existing web of hierarchical rules, jostling for influence over an inherently fluid system.

Contractual expression and algorithmic interpretation

At first, all contractual terms appear to be ripe to be hard constraints, as they often precisely define a required outcome. However, whilst some contractual rules can be coded as hard constraints, others work best as soft constraints.

Take for example, a broad clause that requires recommendations and placement in playlists to be commensurate with the market share of a variety of licensors, or for certain artists to have boosted exposure. Making such contractual clauses a hard constraint would force the algorithm to:

• Prioritise contractual compliance over optimisation, which may have a negative effect on the service as a whole and on the bottom line of both the DSP and the licensors;
• Substantially increase the number of calculations required as recommendations are constrained by asynchronous market share statistics, increasing cost and latency; and,
• Risk solving a mathematically infeasible conditions at, say, a playlist level, particularly if the number of tracks in a playlist are too small to satisfy the plethora of hard constraints, or in the case of competing claims (more on this in my next article).

For these reasons, many contractual terms are necessarily coded as soft constraints as DSPs seek to satisfy their contractual obligations whilst maintaining optimisation principles that continue to make their products successful. But soft constraints are no less enforceable than hard constraints; they simply require a different approach.

Auditor and licensor takeaway

Contractual obligations remain binding even in probabilistic systems, irrespective of how they are coded.  Whilst there is still value in auditors reviewing relevant sections of code to verify if such a term has been accounted for, the enforcement and auditing of such obligations is instead best achieved by the consideration of (largely aggregate) outcomes.

Whether the licensor has accepted a reduced royalty rate in exchange for boosted distribution, or whether they have some other clauses designed to protect their artists’ or catalogues’ positioning, the importance of understanding and auditing the translation of contractual terms to algorithmic operation and the associated outcomes has never been greater.

Such transparency would benefit both the DSPs (given the complex balancing act that is required to be performed) and the licensors (as placement and positioning has a significant influence on income).