From threat to opportunity: Gaming the algorithmic system as a service

Sax, Marijn; Wang, Hao Article From threat to opportunity: Gaming the algorithmic system as a service Internet Policy Review Provided in Cooperation with: Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin Suggested Citation: Sax, Marijn; Wang, Hao (2025) : From threat to opportunity: Gaming the algorithmic system as a service, Internet Policy Review, ISSN 2197-6775, Alexander von Humboldt Institute for Internet and Society, Berlin, Vol. 14, Iss. 2, pp. 1-33, https://doi.org/10.14763/2025.2.2007 This Version is available at: https://hdl.handle.net/10419/321975 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/de/deed.en Volume 14 | From threat to opportunity: Gaming the algorithmic system as a service Marijn Sax University of Amsterdam Hao Wang Wageningen University & Research DOI: https://doi.org/10.14763/2025.2.2007 Published: 6 May 2025 Received: 12 January 2024 Accepted: 27 March 2024 Funding: This research was funded by a grant from the Research Priority Area ‘Human(e) AI’ of the University of Amsterdam. Competing Interests: The author has declared that no competing interests exist that have influenced the text. Licence: This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License (Germany) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/3.0/de/deed.en Copyright remains with the author(s). Citation: Sax, M., & Wang, H. (2025). From threat to opportunity: Gaming the algorithmic system as a service. Internet Policy Review, 14(2). https://doi.org/10.14763/ 2025.2.2007 Keywords: Gaming, Algorithmic transparency, Ethics, Manipulation, Algorithmic decision-making Abstract: Gaming the system – i.e., strategic attempts to manipulate the input(s) for, or one’s interactions with, an algorithmic system to try to secure a better outcome than intended by the system’s design – is commonly portrayed as a threat to online platforms and services. Tech companies often use this gaming concern to justify their reluctance to provide algorithmic transparency. In this paper, however, we will explore a new business model in the digital economy we call gaming-the-system-as-a-service (GaaS). In this model, transparency promises are wrapped into an assisted gaming service and sold as a premium feature. This way, the alleged risk of transparency – gaming the system – is turned into a monetisation feature for service providers. As such, GaaS is a typical example of how tech companies can attempt to turn regulatory pressures (e.g., to provide more insight into how its algorithmic curation and recommendation systems work) into a commercial opportunity. To begin to rethink our normative and regulatory approaches to the interface of transparency and gaming, we perform a first exploration of several potential challenges posed by this new business model. First, GaaS is entwined with an incentive structure that is hostile to consumers and exploitative in nature. Second, GaaS is essentially a pay-to-win feature, raising questions of equality and fairness. Third, the commodification of transparency through GaaS can ‘taint’ and erode transparency as an important democratic value. Issue 2 Introduction Call it a permanent mud wrestling match, or an eternal game of tug of war: lawmakers demanding more transparency and online platforms and service providers in the digital economy pushing back against those demands.1Lawmakers often rely on transparency obligations as a core feature of their strategy to tame the power of digital technology companies (Diakopoulos, 2020). For example, the recent European Union’s legislative agenda aimed at the technology sector – the Digital Services Act (DSA), the Digital Markets Act (DMA), and the Artificial Intelligence Act (AIA) – contains several transparency obligations. Companies typically do not welcome (additional) transparency obligations. An often heard argument against transparency measures is the gaming-the-system-argument: transparency of (often algorithmic) core systems used for decision-making, curation, and/or ranking can provide users with helpful information to game those systems (Kroll et al., 2017, p. 639, Bambauer & Zarksy, 2018: 15). In this context, ‘gaming’ refers to strategic attempts to manipulate the input(s) for, or one’s interactions with, an algorithmic system to try to secure a better outcome than intended by the system’s design. Petre et al. (2019) point out that online platforms and services “routinely denigrate these activities as system-gaming or manipulation” to signal that such practices are understood as problematic undermining of the ‘proper’ functioning of the platforms or services (p. 1). Following the gaming-the-system-argument, transparency obligations are thus mainly portrayed as risks to the business operations of platform and service providers in the digital economy. The gaming argument against transparency may, however, need to be nuanced in answer to new commercial strategies in the digital economy. In this space where transparency obligations are often portrayed as risks by service providers themselves, one can observe those same service providers offering premium features that look suspiciously similar to – ironically – services that promise gaming of algorithmic ranking and outcomes. In what follows, we explore a new business model we call gaming-the-system-as-a-service (GaaS). The core idea behind GaaS is that users can be charged a premium to help them game the system of a service they use to secure (better chances of) better outcomes. Most interestingly, GaaS and algorithmic transparency are closely related. The promise of GaaS can be understood as being predicated on promises related to transparency. To see why, consider the fact that gaming works best when one has some degree of privileged insight into how the system works. In the context of GaaS, however, one often deals 1. Special thanks to Samantha Bradshaw, Camille Girard-Chanudet, Frédéric Dubois, and Francesca Musiani for their helpful comments and suggestions. 2 Internet Policy Review 14(2) | 2025 with indirect transparency at best. The transparency is indirect because the insight into the workings of the system is partial and offered through a service that is designed and controlled by the system owner. So, rather than providing complete transparency2on the entire system which allows users to exploit that information to devise strategies for gaming themselves, the offered GaaS service pre-structures the opportunities for gaming. This pre-structuring can take shape by the gaming service containing limited information in combination with specific tools, or by structuring the service around gaming with the mandatory ‘help’ of employees of the service. Put simply, the alleged risk of algorithmic transparency – i.e. gaming the system – is actually turned into a monetisation feature. In this paper, we understand algorithmic transparency in two ways. Typically, it is seen from a techno-centric perspective, focusing on the openness and disclosure of how algorithms work from a technical perspective. This involves examining data sets, parameters, and models to provide insights into their inner workings, relating to concepts like explainable and interpretable algorithms (Diakopoulos, 2020). This type of transparency is often aimed at experts, regulators, or stakeholders who need to understand the precise mechanics of the system to secure fairness and accountability. However, as Mittelstadt et al. (2019) show, this technical perspective on transparency is not the only type of transparency that matters; there is also another type that involves everyday explanations and is more embodied for humans. For this type, mathematically-founded algorithms are translated into human terms (Larsson & Heintz, 2020), and transparency emerges from the user’s practical experiences and interactions with the platform (Haresamudram et al., 2023). This is what Haresamudram et al. call ‘interaction transparency,’ or embodied transparency, which refers to the way transparency is experienced and understood through direct interaction with a system or platform, rather than through abstract or purely technical explanations (Haresamudram et al., 2023). This form of transparency often creates “a nuanced understanding” and “rich, contextual, situated explanations” of platforms (Haresamudram et al., 2023, pp. 97-98). For example, consider how including a pet in the photos on your profile may (seemingly) improve the performance of your profile in Tinder’s recommendation algorithm. (Wang, 2023). Although Tinder’s algorithm remains mysterious in terms of its pre2. Full or complete transparency is, of course, an (almost) unintelligible notion to begin with. Transparency is always transparency of something and in the digital economy it is difficult to see how, for instance, an online platform can be completely transparent. Even if a person gains access to all existing internal documentation of said platform, there are still ways in which the platform is not fully transparent. Top-level executives may have made decisions on the basis of informal meetings that are not documented and intentions stated in documents may not correlate with the real intentions existing only in the heads of senior management. 3 Sax, Wang cise technical operation, users can still gain a contextual understanding of how it works through their interactions, forming nuanced and everyday insights. This embodied transparency is vital for users (often non-experts) who may not grasp algorithmic details but care deeply about how platforms affect them and their interactions. This paper builds on two examples – one already operational, and one that was announced but later abandoned – to explore and explain how these two types of algorithmic transparency can be monetised. The FICO Score example demonstrates how platforms package its algorithmic transparency as a premium service, revealing to credit consumers how the FICO Score works and how their scores are influenced by different data points and weights. Another example is Tinder Concierge, which was announced (and later abandoned) as a premium service focusing less on revealing technical details of algorithms and data sets, but instead promised a paid-for coaching service to help users to gain nuanced insights and contextual understanding about how its algorithm works (like how certain photos or actions may enhance their profile visibility) to hopefully secure more (and ‘better’) matches. In practice, when platforms monetise algorithmic transparency, they usually blend these two types and engage with them to varying extents. These two examples serve as a first exploration, but they reflect a more general phenomenon of monetising algorithmic transparency as a gaming service. The FICO case shows how transparency as a gaming service is already operational, while the Tinder Concierge case tells us something about the types of initiatives big platform providers are actively considering and experimenting with. We find it instructive to not only discuss examples of services that have already been implemented, but also take seriously services that are ‘only’ considered by platforms. In a fast developing platform economy, discussions on the ethics and regulation of platform services requires an ongoing anticipatory mindset and with it a willingness to discuss the ongoing experiments of platforms; a merely reactive mindset will undermine our ability to develop future-proof and creative analyses for the platform economy. As we will discuss in detail later, we increasingly live in what Citron and Pasquale referred to as a “scored society,” where predictive algorithms rank crucial aspects of individuals' lives (Citron & Pasquale, 2014). These algorithm-driven ranking systems often include reward and punishment mechanisms, encouraging users to optimise their positions. However, their opaque nature makes it challenging for users to understand how to improve their rankings, creating an opportunity for gaming services to emerge. Whether through technical or embodied explanations, algo4 Internet Policy Review 14(2) | 2025 rithmic transparency can be monetised as a premium or commercial product, enabling users to game the system for their benefit. This might sound like a win-win situation. Users benefit from gaming as a service, while companies balance the risk of system manipulation with additional revenue from users paying from premium gaming services and continued engagement with their services. This seemingly mutual benefit creates a potentially advantageous business model. However, if these business models which flip transparency and gaming risks upside down (from alleged risk to business opportunity) indeed start to materialise and proliferate, we may have to rethink our normative and regulatory approaches to them. In this article we make a start by also exploring several possible challenges posed by gaming-the-system-as-a-service. First of all, GaaS is entwined with an incentive structure that is hostile to consumers and exploitative in nature. If a service provider wants to offer GaaS, this introduces the incentive for the service provider to actively make or keep the workings of their systems opaque or unpredictable precisely because uncertainty concerning how the system works serves as a precondition for offering GaaS. Second, GaaS is essentially a pay-to-win feature, raising questions of equality and fairness. Depending on the context where GaaS is introduced, granting advantages to those who can pay a premium can lead to unfair and unjust (market) outcomes. Third and last, the commodification of transparency through GaaS can ‘taint’ and erode transparency as an important democratic value. When transparency is reduced to a commodity, it not only opens the door to manipulating its presentation for commercial interests but also weakens people’s motivation to actively engage in critical thinking or resist unfair practices in GaaS. This article is structured as follows. In Section 2 we discuss transparency as a regulatory philosophy and unpack the alleged threat of gaming the system resulting from transparency obligations. Here we also discuss how GaaS can position itself in the space typically occupied by discourses on (the need for) transparency obligations. In Section 3 we turn to the phenomenon of GaaS. We use the examples of FICO Score (already implemented) and Tinder Concierge (announced but not implemented) to illustrate core features of GaaS. In Section 4 we discuss potential challenges posed by GaaS, namely the exploitative incentive structure it introduces for platforms and service providers, the unfairness of pay-to-win, and the erosion of transparency as a democratic value. 5 Sax, Wang Transparency as an obligation and the alleged threat of gaming the system 2.1 Transparency as a regulatory philosophy Before we turn to GaaS, we first want to briefly discuss transparency and the alleged risk of gaming the system in the digital economy. The principle of transparency has, of course, a long history in the context of policy and regulation. Already in 1913 Brandeis coined the now famous phrase that “sunlight is said to be the best of disinfectants” (Brandeis, 1913, p. 1). Suffice it to say, the core idea that transparency can serve as an important precondition for accountability by making information available that allows for the inspection and evaluation of entities or actors is not a new one. As Ananny and Crawford (2018, p. 974) summarise it: “The implicit assumption behind calls for transparency is that seeing a phenomenon creates opportunities and obligations to make it accountable and thus to change it”. So, the principle of transparency and its link to accountability is not a recent invention. What is a relatively new development though, is the more explicit embrace by legislators of transparency as a pronounced regulatory principle in response to the increasing use of, generally put, algorithmic decision making systems the functioning of which seem opaque to the outsider (Pasquale, 2015; Leerssen, 2023). According to Morozovaite (2024) “the recurring theme in all examined (proposed) legal instruments [i.e., the DSA, DMA, and AIA] is a strong emphasis on transparency obligations” (p. 253). One can clearly see this in the DSA, where transparency obligations are at the core of the legislative philosophy behind the act. The DSA does not only contain more general provisions on, for instance, transparency reporting obligations for providers of intermediary services (Article 15) and providers of online platforms (Article 24), but also specific recommender system transparency obligations (Article 27). Article 27 mandates online platforms to “set out in their terms and conditions, in plain and intelligible language, the main parameters used in their recommender systems, as well as any options for the recipients of the service to modify or influence those main parameters”. Because the recommender engines used by platforms are algorithmically driven, Article 27 can rightly be understood as a provision aimed at what is often called algorithmic transparency. 2.2 Transparency (claims) as a strategic tool and the space it affords for GaaS To understand the precise relation between transparency and accountability, it can 6 Internet Policy Review 14(2) | 2025 be helpful to distinguish between what can be called the openness dimension and the epistemic dimension of transparency. The openness dimension refers to making information public and inspectable; bringing information out in the open so to say. Birkinshaw, for instance, (2006) emphasises that “openness is very similar to transparency” (p. 190). One can think of the government releasing documents after a freedom of information request, or of a leak such as the Panama Papers where large amounts of previously inaccessible documentation suddenly become available. The openness of information is, however, not the same as the comprehensibility, explainability, and/or usability of information. Either the nature of the information (e.g., highly technical documentation) or the amount of information can result in the difficulties to truly understand or process the now open information. This is why transparency is often thought to have an important epistemic dimension as well: “transparency also requires external receptors capable of processing the information made available” (Heald, 2006, p. 25). When the epistemic dimension is taken seriously, true transparency also requires that public/open information must be understandable to its target audience. Transparency’s openness dimension and its epistemic dimension can, of course, be misaligned – not all information that is made public is also understandable, and not everything that is explained in an understandable manner is backed by publicly accessible and verifiable information. It is precisely in this potential for misalignment, we argue, that one can find the inherent political and strategic nature of transparency (Wang, 2022). Transparency is always afforded by an actor with particular interests and incentives, and to an actor (or several actors) with particular interests and incentives. One actor’s transparency can be another actor’s incomprehensibility. Because our argument focuses on how the space of transparency discourse as it is shaped by (especially) recent regulation can potentially be monetised by service providers with gaming-the-system-as-a-service, we are mostly interested in how transparency claims can be put to strategic use. A service provider can, for instance, claim that providing some additional explanation of how certain functions/systems work counts as practising transparency, in an attempt not to make actual documentation public. Or, vice versa, an actor can make public large amounts of highly technical documentation which can be very difficult to make sense of without additional explanatory guidance. We are, therefore, not interested in being the arbiters of what defines real or true transparency. For our argument it is much more important to acknowledge how different types of actors tend to make different transparency claims to achieve different – often self-serving – outcomes. 7 Sax, Wang 2.3 Proxies and gaming It is precisely in the context of sweeping transparency regulation such as the EU’s DSA, DMA, AIA package that online service providers and platforms decry the risk of their systems being gamed. Cofone and Strandburg (2019) provide a very helpful overview of the debate on algorithmic transparency and the concern of ‘gaming the system’. They draw on literature in (empirical) legal studies, computer science, and game theory to explain how gaming can occur and when it is and isn’t a realistic risk. “Fundamentally, the gaming threat stems from decision maker reliance on proxies for criteria” (Cofone & Strandburg, 2019, p. 626). As a user of a system, knowledge of which proxies are used for decision making can allow one to (try to) exploit those proxies to (try to) steer the eventual outcome/decision in the desired direction. The use of proxies in algorithmic decision-making contexts is inevitable, because they are used “when the ideal decision-making criteria are unascertainable as a practical matter or simply unknowable” (Cofone & Strandburg, 2019, p. 635). Take, for instance, a dating app. If we assume that the dating app decides on matches based on which matches have the highest chance to develop into a successful relationship3, it immediately becomes clear that the ideal decision-making criteria – i.e., a successful relationship – is situated in the future and unknowable at the time of matching people. So, proxies have to be used to approximate as it were the ideal decision-making criteria. Not all proxies function in the same manner though and not all types of proxies are equally suitable for gaming. Cofone & Strandberg (2019, pp. 636-640) describe three layers of proxies. The first layer concerns input data that goes into an algorithmic decision-making procedure. In a dating app, this can be data concerning one’s age, sexual preferences, and interests. The second layer uses the input (and possibly other) data “to compute a predicted value of the outcome variable that is only a proxy for that individual’s “true” outcome value” (Cofone & Strandburg, 2019, p. 638). In the dating app example, this would concern the ways in which input data will be used to compute a predicted value for an outcome value such as ‘predicted chance that a match leads to a date’. The third layer concerns how the outcome value chosen by the designer of the system at the second level itself serves “as a proxy for the ideal decision criterion” (Cofone & Strandburg, 2019, p. 638). For a dating app, the ideal decision criterion – which is not directly measurable and/or knowable, hence the need for the use of proxies – would be something like ‘the two people matching will develop a successful relationship’. 3. The term ‘successful relationship’ should be read as ‘a type of relationship or interaction that the persons that are dating consider to be satisfying relative to whatever standard they themselves deem relevant’. 8 Internet Policy Review 14(2) | 2025 Hence, the FICO Score case shows how gaming the system can happen through monetising three layers of technical transparency. Credit users who buy an informational report on how the algorithm technically works and performs, can improve their credit scores and thereby gain more economic and social benefits. 3.2 Tinder Concierge, and other dating apps Having discussed the FICO scores case, we now turn to a slightly more speculative example, namely Tinder Concierge – a coaching service announced by Tinder in 2020 which promised Tinder users who were willing to pay for it help from Tinder employees to craft better performing profiles (Brown, 2020). The service has not (yet) been released in its originally advertised form, but some of its features have been integrated into some of Tinder’s premium membership package. Despite its somewhat speculative nature, we find this example especially useful because 1) the announced features are almost an ideal type of the type of coaching-based GaaS we are interested in, and 2) the fact that Tinder announced this feature publicly clearly shows that the industry is in fact already thinking along these lines. In a fast-paced platform economy, anticipating future developments by scrutinising the experiments platforms are engaged in is a good way to ensure one’s critical analyses will not only be reactive in nature. Moreover, other services in the dating app ecosystem are also moving in similar directions with premium memberships and features that promise to help one perform better in the (algorithmic curation and ranking of the) dating app in question. Where Tinder promised the expertise of real employees to those willing to pay a premium, other dating apps have relied on AI-driven coaching bots rather than human experts. For example, Match.com has developed an AI dating chatbot named “Lara” that serves as a personal love coach by using natural language processing (Li, 2019). Some other dating companies, like eHarmony, Happn, and Loveflutter, have also developed AI-driven love coaches (Tuffley, 2021; Ghosh, 2017; Silva, 2018). These coaches promise to help users navigate dates and optimise profiles to get more dating opportunities (Tuffley, 2021). Let us now look at Tinder and Tinder Concierge in some detail. Algorithmic ranking and curation obviously plays a central role on Tinder. Users create a profile which contains several pictures, personal information (e.g., age, gender, what one is looking for on Tinder) and information on preferences (e.g., hobbies, music). When using the app, one profile at a time is shown to you based on an algorithmic ranking procedure that is largely opaque. It seems obvious that the information one has provided oneself plays a significant role, but one’s interaction history with Tinder profiles shown to oneself is also hypothesised to play a role. We deliberately write ‘hypothesised’ because as a user you can only guess how the algorithmic ranking works. There is a whole ecosystem of dating websites and communities trying to reverse-engineer Tinder’s algorithmic ranking by modelling 15 Sax, Wang it on the concept of Elo rating systems used more generally in game theory.7Academics, in turn, have also noted how Tinder’s algorithmic opaqueness poses not only methodological challenges in terms of research but also introduces uncertainties to which users can respond in a variety of ways (see, e.g., Duguay, 2017; Courtois & Timmermans, 2018; Wang, 2023). A core presumed feature of Tinder’s algorithm – often discussed online – is an indirect attractiveness score assigned to profiles by proxy, based on how others have interacted with that profile (e.g., if many people swipe right on your profile you are presumed to be attractive). Another often discussed assumption8is that one’s own swiping behaviour also serves as a proxy for one’s attractiveness, where being a more ‘picky’ swiper is assumed to be a proxy for being more attractive and being a very eager swiper is assumed to be a proxy for being a less desirable member of the dating pool. Now, this is not the place to explore these matters in (more) detail. What is interesting though is the fact that the “algorithmic imaginaries” (Bucher, 2017) which are being discussed on dating websites and communities are also, directly or indirectly, constitutive of understanding Tinder as an algorithmic service that can – or maybe even should – be gamed. Here we also see the different proxy layers from Section 2.3 again. One can try to game the matches one will get directly by changing information one puts on one’s profile oneself (e.g., if you change your age on your profile, your profile will be shown to people who have indicated they are looking within a specific age range). But as the Tinder Elo community shows, there are also more indirect ways of trying to influence the relevant proxies by, for instance, experimenting with different types of swiping behaviour to – hopefully – indirectly influence the proxies that determine one’s performance in the algorithm. Within this context of Tinder as a permanent object of gaming attempts by the community, Tinder Concierge was announced in March 2020 as a premium service that would cost between$20 and$50 a month (different Tinder users got different pop-ups announcing the service at different price points9). The pop-up shown to some users read as follows: “Our Concierge service may be headed your way. For $20, you’ll get access to our team of experts who will help you craft the perfect profile. Go on, have a taste of the good life.”10 The GaaS offered here clearly doesn’t revolve around the offering of extensive technical transparency on Tinder’s algorithm directly to the user. Rather, Concierge, in its announced form, could be a great example of embodied transparency. The 16 Internet Policy Review 14(2) | 2025 7. Elo rating systems (named after physics professor Arpad Elo) are used in, for instance, chess to assign values to individual players to predict their performance in tournaments (see, e.g., Olmeda, 2022). Already in 2019 The Verge reported that Tinder stopped using ‘desirability scores’ in its algorithmic ranking (Carman, 2019). The fact that an alleged literal desirability score is no longer used, does not imply that certain proxies for desirability are not still important for Tinder’s algorithmic ranking of profiles. At the moment of writing, there are still many dating websites and communities writing detailed breakdowns of the hypothesised working of Tinder’s Elo score (see, e.g., Bailey (2025) . There are also websites that promise to help you calculate (or better: approximate) your own Tinder Elo score (see, e.g., Bayley, 2020) 8. See for instance this Reddit thread on the subreddit /r/SwipeHelper (faffner100, 2021) 9. On Reddit, this led several Tinder users to compare the prices they were shown for the service and ask whether the price they were shown said something about their attractiveness (m8keup, 2020). 10. The screenshot is included in a Forbes article on Tinder Concierge (Brown, 2020). 17 Sax, Wang service promises access to Tinder employees who presumably have a good understanding of how the algorithm works (they are “experts”) and who can make the algorithm’s performance transparent to the user indirectly as it were by making suggestions for changes to the profile for better performance of said profiles on Tinder. As for the specific suggestions the Tinder experts can make, we can only guess what those could be because at the moment of writing Tinder has not officially launched the service in its advertised form.11 If the service would ever materialise in its full advertised form, we could imagine several GaaS features. First of all, one could imagine the Tinder experts offering advice on how to build one’s profile: which photos tend to work well, which profile texts and stated interests work well. Such advice would basically come down to advice on gaming the first input data layer. If swiping behaviour is indeed also an important proxy in the matching algorithm one could also imagine what could be called ‘behavioural advice’ on swiping to be part of the Concierge service. 3.3 The phenomenon of GaaS These two examples reflect a general phenomenon that algorithmic transparency can be monetised as part of a gaming service. As argued by Danielle Citron and Frank Pasquale, we are increasingly living in a “scored society” where predictive algorithms are applied to rank individuals’ in important parts of their lives (Citron & Pasquale, 2014). For instance, different algorithms are created to rank individuals who are most likely to get a job, commit a crime, default on bills, and find a date. The cases of Tinder (or other dating apps) and FICO Score are typical examples of this general trend toward a scored society. On the one hand, these algorithm-driven ranking systems are often embedded with a series of reward and punishment mechanisms, which may encourage users to game the system to their best benefits. For example, if Uber drivers are algorithmically ranked with low scores, they will be immediately punished by having their recommended passengers reduced or even being removed from the platform (Muldoon & Raekstad, 2023). On the other hand, the opaque nature of these ranking systems discourages users from gaming the systems for their own benefits. As we mentioned, the three layers of gaming make it rather difficult to know how algorithms actually work and how to improve their performance, especially when it comes to the second and third layers of gaming. This gap between users’ needs to improve their ranking and the actual difficulty in knowing how to do so opens room for gaming as a commercial service. This GaaS can work through some degree of algorithmic transparency, as improving performance often requires some knowledge of how the algorithm works. Just like what is shown in the example of FICO Score, which discloses its scoring algorithm’s breakdown into five components, letting credit consumers know how 18 Internet Policy Review 14(2) | 2025 11. Tinder Concierge service did, however, resurface as a part of an even more expensive $500 a month ‘Tinder Vault’ service that was being piloted in the Spring of 2023 (Barr, 2023) . 19 Sax, Wang to improve their scores according to these five factors. Similarly, coaching services in dating apps can provide users with some more embodied knowledge of how their behavior as a user interacts with the (largely) opaque algorithm(s) of the service they are using. Coaches can use everyday language to explain or indicate how, roughly speaking, factors such as profile information and photo selection influence matches. Besides providing some general information about how platform algorithms function, GaaS can also offer users with some personalised knowledge about how their particular situation, such as not ranking high in the dating market, is influenced by the platform’s algorithms. Based on these personalised insights, coaching-based GaaS could suggest tailored methods to improve performance on the platform. GaaS can thus be understood as a process of monetising algorithmic transparency in the scored society, where service providers can charge its users a premium to receive ‘transparency benefits’ which the user can use to make one’s profile or content perform better in algorithmic ranking or curation processes.12In an environment that is controlled by the service provider, however, such a premium service will typically not offer ‘real’, blanket transparency, but rather a more strategic, partial transparency wrapped in an additional service where the same service provider offers ‘tools’ and ‘advice’ to help ‘optimise’ one’s content or profile for algorithmic ranking. Put simply, the alleged risk of transparency – i.e., gaming the system – is actually turned into a monetisation feature. In the following section, we will explore the potential ethical challenges of this GaaS. 4. Exploring ethical challenges of GaaS In this section we discuss some of the possible ethical challenges posed by GaaS. Because GaaS as we describe it is a recent phenomenon, varieties of which we expect to become more frequent in the near future, this section is meant as a first exploration. 4.1 Consumer-unfriendly incentive structure and exploitation First, GaaS offered by the service itself introduces (or reinforces) an incentive structure that is hostile to consumers. To see why the incentive structure is hostile, it should first be noted that GaaS can only be offered against a background of sufficient opaqueness. If a service or platform relies on algorithmic curation/ ranking which is completely transparent and understandable, GaaS will quickly lose its value proposition. Consider the announced (but later abandoned) Tinder Concierge service; this service would only be an interesting service for the public because the Tinder algorithm remains to be seen as enigmatic. So, if GaaS is to be pursued as a commercial strategy, it introduces an incentive for maintaining (or even introducing new) levels of opaqueness as a necessary precondition for 20 Internet Policy Review 14(2) | 2025 12. There are some other more general types of GaaS. For instance, users can be charged a premium to receive more direct ‘gaming benefits’ which serve as de facto pay-to-win features to increase one’s chances to perform better in (ranking) systems one interacts with. But for this paper, we’re more interested in the particular phenomenon of GaaS related to the monetisation of algorithmic transparency. 21 Sax, Wang offering GaaS. Such an outcome would be disappointing given the recent push for (even more) transparency obligations in, for instance, the European legislative agenda for the digital economy (DSA, DMA, AI Act). The strategic market reaction of finding ways to monetise a desire for transparency in ways that, if anything, introduce incentives to not practise genuine transparency, could indeed be seen as both ironic and cynical. It remains to be seen, however, whether variances of GaaS will actually be compliant with e.g. transparency provisions in the DSA. Besides the hostile incentive structure itself, one could also question what type of relationship between the service provider and the user GaaS results in. GaaS is premised on actively fostering opaqueness to be able to offer a service that has users pay an additional fee to alleviate/overcome negative externalities resulting from the deliberate opaqueness. One possible way of characterising such a relationship is as exploitative. Philosophically, there is no consensus on the precise meaning and definition of exploitation (see Zwolinski et al., 2022 for an extensive overview of different views and debates). A minimal, uncontroversial understanding of exploitation is that exploitation involves taking unfair13advantage of one’s target by using a vulnerability of the target (either a personal characteristic, or something in the target’s environment) for one’s own benefit. When we consider Tinder Concierge as an example of GaaS, a case can indeed be made for the service having significant exploitative characteristics. Users use Tinder to find something that is very important to them: dates, love, intimacy, companionship. To ‘gain access’ to those ‘goods’ users are made very aware of the fact that they have to perform in a competitive dating market that is structured around the illustrious proprietary Tinder algorithm (of which there exists a lively algorithmic imaginary as we discussed in Section 3.1). So, what we have is 1) a large group of users with a strong desire for particular outcomes (matches, dates, love, intimacy), and 2) a largely opaque (algorithmic) gate-keeping mechanism. Combined, these two circumstances constitute the fertile soil for deliberately exploitative commercial practices. The strong desires for matches, dates, love, and intimacy serve as an exploitable vulnerability. Tinder’s projected premium service Tinder Concierge would certainly qualify as an exploitative strategy that can be used to take advantage of those vulnerabilities for the benefit of Tinder. And even though Tinder Concierge ultimately did not get implemented, already existing Tinder premium services such as Plus, Gold, and Platinum also bear the marks of a similar exploitative logic. These premium services promise a range of ‘power-ups’ to make you more competitive vis-à-vis your Tinder competitors (Tinder, n.d.-a). For example, for the most expensive subscription called Platinum, Tinder writes: “Increase your match-making potential and enjoy most of Tinder’s premium features with Tinder Platinum™! Dating online just got easier. See someone you’d love to meet and can’t wait to match? As a Platinum subscriber, you can attach a note to every Super Like you send, increasing your match-making potential by up to 25%” (emphasis added, Tinder, n.d.-b). The same 22 Internet Policy Review 14(2) | 2025 13. By using the word ‘unfair’ here, the proposed minimal understanding of exploitation is a normative one: exploitation in this sense is understood as in principle wrong (overriding reasons for deeming the exploitation acceptable all things considered can still exist). A minimal understanding which does not incorporate the word ‘unfair’ is of course also possible, which would lead to a more neutral understanding of exploitation. In that case a football player making use of a weakness in the opponent’s defensive positioning would also ‘exploit’ that particular vulnerability for their own benefit. But no one would call that type of exploitation wrong; it’s part of the game. We would, however, generally consider it unfair if the football player would – for instance – feign an injury to cause the opponents to huddle around him to check on him. If the football player then exploits the defensive disorganization he caused by faking an injury, we would say he exploited his opponents’ sportsmanship in an unfair (i.e., ethically problematic) manner. 23 Sax, Wang analysis of exploitation applies in an even more straightforward manner for FICO Scores. Credit scores clearly determine one’s ability to access a wide range of essential services, as well as the conditions (e.g., interest rates) under which one can access those essential services. Selling premium transparency services which are premised on people’s real fear of bad/worsening credit qualifies as an exploitative practice under most conceptions of exploitation. One may object that Tinder is ‘just a dating app’ which users do not need to use at all if they don’t want to. Deciding to use the app also means consenting to Tinder attempting to pressure you into purchasing premium services that can help you ‘game’ Tinder’s algorithmic match making. There are three brief answers to this possible objection. First, the idea that as long as consumers are not actively forced to use a service anything goes, is simply wrong. In the EU there is, for instance, unfair commercial practice law (Directive 2005/29/EC) which forbids many types of misleading and aggressive commercial practices that take unfair advantage of consumers. Second, the sentiment that Tinder is ‘just a dating app’ is misguided. Love, intimacy, and companionship are basic human needs and people are in fact increasingly turning to apps like Tinder to help fulfil those needs. Moreover, being in a relationship with someone also tends to privilege one societally since it allows one to, for instance, apply for a mortgage together. There are socioeconomic implications of one’s ‘dating status’, meaning that a popular dating app cannot be dismissed as ‘just a dating app’. Third and last, even if one were to (still) think that Tinder is not important and the exploitative characteristics of the user-GaaS-provider relationship are therefore not worrisome, then we should still think of other contexts GaaS could be introduced and where it would be considered (more) problematic. The FICO Scores example with the paid-for services that help users who can afford it to optimise their credit scores comes to mind. Credit scores can play such a decisive role in people's lives – it can determine whether you can get the right mortgage or not – that GaaS(-like) services in the credit scoring context are a legitimate concern. 4.2 Pay-to-win and equality If one uses GaaS in the hope of performing better in an algorithmic ranking/ matching/curation scenario in order to, in the end, secure better outcomes, one is basically engaging in what in the videogame context is known as pay-to-win. Payto-win entails paying for a competitive advantage (e.g., by receiving better weapons or receiving health upgrades), often without the absolute guarantee of actually winning; you still have to actually defeat opponents with your bought advantages helping you. So, technically speaking pay-to-win means pay-to-bemore-likely-to-win in most cases. 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