Introduction
In the past few years, the international and EU competition law community has turned its attention to the rise of artificial intelligence (AI) and, in particular, algorithmic pricing, its impact on competition and the search for an appropriate response under antitrust laws.
Worldwide, companies are increasingly using algorithms and AI to power their operations, from product development to manufacturing and marketing. Product pricing and consumer targeting are no exception to this trend. The exponential improvement of these technologies and the very specific impact of algorithm-related conduct on consumer behaviour, competition and even personal data protection are being increasingly targeted by regulators all over the world.
The use of these technologies is still precocious in most markets, however, and enforcers have had little opportunity so far to identify competition issues. This trend appears to be changing, as, at a public event in July 2025, Deputy Director-General for Antitrust Linsey McCallum confirmed that the European Commission is currently conducting multiple inquiries into the use of algorithmic pricing mechanisms, without specifying in which sectors. Although there are still very few precedents finding anticompetitive pricing algorithms in breach of EU competition rules, this has not precluded models and studies from being developed and published on the matter.
Much of the analysis carried out has focused on assessing possible theories of harm of pricing algorithms and their respective fit into the definitions of Articles 101 and 102 of the Treaty on the Functioning of the European Union (TFEU). The theories of harm most commonly identified regarding pricing algorithms and EU competition law include (1) algorithmic collusion and (2) algorithmic unilateral exclusionary conduct (predatory pricing and rebates) and exploitative conduct (excessive pricing, unfair trading practices and price discrimination). Regulatory limitations to certain algorithmic pricing practices introduced by the new Digital Markets Act (DMA), the Digital Services Act (DSA) and the Artificial Intelligence Act (the AI Act) may also be added to this list. Several provisions in these recent legislative initiatives have a strong competition policy component (particularly in the case of the DMA) and may curb the unfettered use of pricing algorithms.
The evidence, models and studies carried out on algorithmic pricing do not unequivocally point to systematic anticompetitive effects, particularly in real economy conditions. As highlighted by the Organisation for Economic Co-operation and Development (OECD), the magnitude of the threat of algorithmic collusion by autonomous self-learning algorithms is still disputed in the academic literature, and there are few known cases. With regard to certain unilateral pricing algorithm practices, such as price discrimination, it is still unclear to what extent this practice is deployed by companies.
Against this backdrop, a more reasonable and proportionate approach would involve policymakers and regulators so that more information can be gathered on real-life cases of anticompetitive algorithmic pricing and so as not to put the proverbial cart before the horse and hinder a technological development that undoubtedly also has societal and consumer benefits.
Pricing algorithms in today’s economy
Over the past few years, the European Commission and other national competition authorities have tried to investigate and understand how widespread algorithmic pricing is in the wider economy.
Algorithms are sequences of automated operations and processes that transform an input into an output. Pricing algorithms are algorithms of which the output is the determination of a price of a product.
Studies distinguish between pricing algorithms that monitor other companies’ prices (price monitoring algorithms), those that recommend or automatically set a price based on other companies’ prices or market conditions such as demand (dynamic pricing algorithms) and those that tailor prices to specific individuals based on their features (personalised pricing algorithms).
With regard to price monitoring algorithms and dynamic pricing algorithms, the European Commission found in its 2017 E-Commerce Sector Enquiry that almost one-third of all retailers analysed effectively tracked the online prices of competitors using automatic software programs and subsequently adjusted their own prices based on those of their competitors.
The OECD’s background note on algorithmic competition (the OECD Background Note) has further emphasised that firms operating online frequently use monitoring and dynamic pricing algorithms. While personalised pricing does not seem to be as widespread, the increasing availability of customer data and the development of technology make personalised pricing ever more feasible and, therefore, exposed to legislative and regulatory action.
Algorithmic pricing and collusion
The OECD Background Note on algorithmic competition explains that, in recent times, policymakers and regulators around the globe have been working on understanding the opportunities and threats that algorithms pose – particularly pricing algorithms.
The main concern raised by pricing algorithms is the potential ability to facilitate coordinated conduct, resulting in higher prices. Competition authorities and the OECD have identified three main ways that pricing algorithms lead to collusion.
Algorithms facilitating explicit collusive agreements
Some algorithms can facilitate explicit collusive agreements. For example, automated pricing systems based on available pricing data can detect and respond to pricing deviations, making explicit collusion between firms more stable. Under this scenario, the explicit agreement would violate Article 101(1) of the TFEU, while the algorithm would act as an instrument that allows the companies to implement the agreement or to retaliate against deviations thereof.
An example of a pricing algorithm that facilitated an explicit collusive agreement may be found in the United Kingdom’s Topkins US and GB Eye Trod UK case. The investigation revealed that online poster retailers were using simple pricing algorithms in the context of a horizontal cartel among retailers to coordinate their prices on Amazon. In Spain, the Proptech case shows how real estate agencies using a multiple listing system to share properties and fees colluded to set the minimum fees that they would apply for property sales and rentals. The real estate agencies used this system to monitor their alignment with the anticompetitive agreement.
Algorithmic pricing may also be used to support collusive agreements entered into by companies placed in different levels of the supply chain. For example, the European Commission’s investigation into consumer electronics (Asus, Denon & Marantz, Philips and Pioneer) showed how technology could be used to monitor retailers’ alignment with prices dictated in a vertical relationship.
Algorithms as catalyst in hub-and-spoke settings
Algorithms can be a catalyst in hub-and-spoke settings. For example, if several firms use the same third-party pricing software to determine their prices, this may result in a hub-and-spoke situation that can facilitate information exchanges in the context of an agreement or concerted practice. Academic research into retail petrol prices in Germany and certain hotel room and rental cases in the United States suggests that the use of the same pricing software by competitors represents the biggest threat. From this perspective, markets where actors use predominantly third-party pricing software may be more exposed to the (unintended) implementation of coordinated conduct.
The closest real-life investigation that one might find in relation to hub-and-spoke algorithmic collusion is the Eturas case. In this case, the Lithuanian Competition Council considered that travel agencies using the Eturas booking system had entered into a tacit agreement or concerted practice, having learned that the system would limit their discounts to a maximum of 3 per cent. Although the facts of the investigation do not show that the travel agencies were in contact with one another, the EU Court of Justice ruled that knowledge of the full terms of the 3 per cent discount cap (including the assumption that all travel agencies would be subject to the same restriction) could amount to a tacit agreement.
Algorithmic autonomous tacit collusion
Algorithmic autonomous tacit collusion can occur, which implies the implementation by competitors of self-learning autonomous algorithms to decide on colluding (or at least to avoid reaching a competitive outcome) – all without the competitors sharing any information, having any connection or contact among each other, or coordinating explicitly.
Since at least 2015, academics have identified the potential for algorithmic autonomous tacit collusion. More recently, economists have started to publish an increasing number of papers explaining the models created and the outcomes analysed in certain scenarios of algorithmic pricing. In particular, this literature suggests that algorithmic collusion is possible without communication or even without making competitors’ prices an input of the pricing algorithm. In these cases, algorithmic autonomous tacit collusion is modelled using reinforcement learning algorithms (i.e., algorithms that learn through autonomous trial and error exploration). Several authors have used Q-learning reinforcement learning algorithms, only to conclude that these algorithms learn to set supra-competitive prices without communicating with each other.
Other authors have even concluded that pricing algorithms can soften competition by undermining competitors’ incentives to undercut prices, as any price reduction by a company would immediately be met by an equivalent cut in price from its competitors. In these markets, prices could remain above competitive levels, even in the absence of explicit collusion.
While some of these papers show the tendency of independently used pricing algorithms to reach tacit collusive strategies automatically, the risks that these strategies materialise in real economy conditions are still unclear. Pricing algorithms have been adopted progressively, but are not yet universal or homogeneous across all competitors in a given sector. Even if that were the case, specific market conditions would need to be present for the simultaneous use of pricing algorithms to lead to appreciable anticompetitive outcomes (e.g., a low or limited number of market actors, or equal levels of vertical integration).
Further, even if companies in a given market used self-learning pricing algorithms, there is no conclusive evidence that algorithmic autonomous tacit collusion is a significant issue (e.g., convergence to a collusive equilibrium may be slow and often unsuccessful); therefore, while some authors consider that continued research and enforcement efforts regarding tacit collusion of algorithmic pricing may be unwarranted, others invite regulators and academia to further investigate possible scenarios of tacit collusion in real life environments.
The identification of algorithmic autonomous tacit collusion not only presents technical and economic hurdles, but also legal ones. Even if a competition authority were to identify a potential case of tacit collusion, the current state of the law could make such practice irreproachable in the absence of an explicit communication or contact among the companies using such autonomous algorithms. As demonstrated in the case law in Anic Partecipazioni, Hüls and T-Mobile Nederland, the ‘concerted practices’ doctrine requires, among other things, at least one instance of ‘direct or indirect contact between such operators, the object or effect whereof is either to influence the conduct on the market of an actual or potential competitor or to disclose to such a competitor the course of conduct which they themselves have decided to adopt or contemplate adopting on the market’.
The OECD and other regulators are aware of what are perceived as shortcomings of the existing legislation, and several calls to action have been made for policy changes to address this potential enforcement gap. From the enforcer’s perspective, existing competition law may not be sufficient to capture algorithmic autonomous tacit collusion, and the OECD has tabled the possibility of ‘changing the definition of “agreement” and “concerted practice” to move away from being defined by “act of reciprocal communication between firms” or “meeting the minds”’.
Some scholars have also discussed what remedies could be considered to limit the potential negative effects of algorithmic pricing and tacit collusion. A first, market-based remedy, involves using consumer algorithms to counterbalance the algorithmic collusion taking place on the supplier side. Alternative remedies put forward rely on intervention from regulators, including:
- turning to merger control to block or remedy transactions giving rise to situations that stimulate algorithmic pricing;
- introducing disruptive algorithms to alter the market conditions in a way that disincentivises tacit collusion (i.e., by charging lower and more competitive prices); and
- creating an artificial time lag, reducing the speed at which pricing algorithms can address novel market conditions.
In the absence of a coherent and firm approach from the antitrust community, legislators around the globe are moving to adopt laws that prevent or regulate the use of algorithmic pricing in specific sectors. For example, in the United States, two House representatives introduced the Preventing Algorithmic Facilitation of Rental Housing Cartels Act on 6 June 2024, which would prohibit digital price-fixing by landlords. In the European Union, it was revealed in July 2024 that the European Commission’s justice and consumer department had launched a new workstream relating to AI in contracting, concerning specifically scenarios where machines make decisions without explicit human consent.
There have also been some attempts to introduce comprehensive legislation not limited to specific sectors. For instance, on 23 January 2025, US Senator Klobuchar, supported by eight Senate co-sponsors, reintroduced the Preventing Algorithmic Collusion Act. The aim of this legislative proposal is to amend the Sherman Act by ‘prohibiting the use of pricing algorithms that can facilitate collusion through the use of nonpublic competitor data’ and ‘creating an antitrust law enforcement audit tool’.
The question remains whether the Sherman Act actually needs to be amended to capture infringements facilitated through algorithms. In various statements of interest filed in cases concerning algorithmic price fixing, the US Department of Justice (DOJ) has made clear that, in its view, the currently applicable antitrust law is sufficient to address these types of infringements. In one case, it concluded that the same legal principles applying to price-fixing schemes-based collusion through human pricing agents apply to circumstances in which the common pricing agent is an algorithm. In another case, the DOJ explicitly argued the following:
algorithmic price fixing must therefore be subject to the same condemnation as other price-fixing schemes. It makes no difference that prices are fixed through joint use of an algorithm instead of by a person, just as sharing information through an algorithmic service should be treated the same as sharing information through email, fax machine, or face-to-face conversation.
Algorithmic pricing and abuse of dominance
Algorithmic pricing can not only be analysed in the context of actual or potential collusive conduct, but also as unilateral conduct aimed at excluding competitors from the market by using pricing strategies, or exploiting customers by imposing unfair prices.
Algorithmic pricing may be qualified as an abuse of dominance under Article 102 of the TFEU if the algorithm is operated by a dominant entity. The dominant undertaking’s intention is immaterial for the competitive analysis: the authority only has to demonstrate that the algorithmic pricing is capable of producing anticompetitive effects.
Exclusionary conduct
A dominant firm can use pricing algorithms to pursue anticompetitive exclusionary strategies, primarily via predatory pricing and rebates. These exclusionary objectives can be achieved by means of personalised pricing and algorithmic targeting. The reason for this is that dominant firms seeking to engage in more typical exclusionary anticompetitive conduct (and being unable to price-discriminate) would generally face the challenge of applying a single price across the market, which could act as a constraint on the profitability or duration of the anticompetitive behaviour. Personalised pricing and algorithmic targeting can weaken this constraint, as they enable dominant entities to adjust the pricing strategy to targeted customers or categories of customers.
Predatory pricing
Dominant companies can use algorithms to conduct predatory pricing strategies that target marginal customers. For example, a dominant company can identify a customer at risk of switching or a competitor’s customer that is price-sensitive (i.e., a marginal customer), and target it with below-cost price cuts to retain it or to steal it from the competitor. This way, a dominant company would use the predatory pricing strategy to achieve anticompetitive foreclosure of rivals.
Algorithmic targeting and pricing can lower the costs of predation for the dominant company, as they allow it to avoid losses on inframarginal customers (i.e., customers not at risk of switching or competitors’ customers that are not price-sensitive) and allow it to charge excessively low prices. This can lead to extended durations of predatory pricing strategies (as foregone profits, or even losses, may be more sustainable) and reduces the need for the dominant company to recoup its profits, making the overall predatory pricing strategy more feasible.
Rebates
According to the OECD, algorithmic targeting allows dominant firms to adopt a new form of rebate that combines the most compelling elements of standardised rebates (i.e., application to large groups of customers, such as larger rebates for marginal customers and lower (or no) rebates for inframarginal customers) and personalised rebates (i.e., maximisation of profits across customers, such as by targeting transactions where competition is fiercest). Accordingly, algorithmic targeting mitigates the limitations of standardised rebates (e.g., lack of profit maximisation for selected customers) and those of personalised rebates (e.g., offering personalised prices to a large and diverse set of customers may increase transaction costs and undermine profitability). Algorithmic targeting can therefore facilitate the use of rebates by dominant firms to prevent customers from switching to rivals resulting in anticompetitive foreclosure of the latter.
Challenges raised in substantive assessments
Algorithmic pricing and targeting also impact the assessment of price-based exclusionary conduct by competition authorities.
First, as the price-cost test determines whether a dominant firm is charging a below-cost price, algorithmic pricing and targeting have an effect on the prices that are considered for this analysis. Reason dictates that actual costs and prices applied to the products sold and to the contestable part of demand should be used for comparison purposes, as opposed to, for example, average prices prevailing in the market.
Second, algorithmic pricing and targeting may require rethinking the scope of the as-efficient competitor (AEC) test. To date, the AEC test had been applied solely to the analysis of the ability of companies to reduce costs and prices to match the strategy of a dominant company; however, algorithmic pricing and targeting introduce a new level of sophistication in the sale of products that might only be available to incumbents or long-time players that have collected substantial customer data.
It remains to be confirmed to what extent this consideration is relevant, as stakeholders participating in some market studies have considered algorithm performance as a key driver for generative AI performance – even more so than the volume of data used to train AI models. If data were considered to be relevant and key for the analysis, this would beg the question whether the ability, or inability, of smaller competitors to collect or replicate such data sets for algorithmic targeting and pricing should be factored in any way into the AEC test.
Finally, questions may arise regarding how dominant companies may use algorithmic pricing and targeting safely, or what remedies may be sought by authorities in the event of a finding of the use of algorithms as abusive. The OECD has pointed to a number of behavioural remedies that could remedy personalised pricing and algorithmic targeting, including:
- restricting the amount of personal data collected by the dominant undertaking;
- obliging the dominant undertaking to share customers’ data with rivals;
- disclosing the personalised pricing strategy and corresponding parameters to users; and
- offering users a right to opt out of personalised pricing.
From an EU competition law perspective, these potential remedies may be considered far-reaching and raise some proportionality questions. Furthermore, other remedies (including those mentioned above to avoid algorithmic collusion) could be considered, such as limiting the frequency with which the dominant firm’s algorithm changes or adjusts prices.
Algorithmic exploitative conduct
Exploitative conduct by dominant undertakings directly harms consumers through the imposition of excessive prices or unfair trading conditions. In general, exploitative abuses of dominance have rarely been prosecuted, either because they are not outlawed by statute (e.g., United States and Canada) or because they are investigated only very rarely (e.g., Australia, European Union and Japan); however, algorithmic pricing increases the feasibility for a dominant company to engage in this type of exploitative conduct, thereby also raising the risk profile of this technology.
The following types of exploitative conduct are typically distinguished: excessive pricing (e.g., unfair purchase or sales prices), unfair trading conditions (e.g., unilaterally imposing other unfair trading conditions) and price discrimination (e.g., applying dissimilar conditions to equivalent transactions). The following sections focus on two of these exploitative conducts: excessive pricing and price discrimination.
Excessive pricing
Exploitative abuse cases, and by extension decisions on excessive pricing, are very scarce, even in jurisdictions where authorities are allowed to investigate this type of behaviour (e.g., European Union). Despite the lack of precedent, a strategy of algorithmic excessive pricing can still – at least in principle – be found to be implemented by a dominant player. A key challenge in pursuing exploitative abuse cases is to determine whether the conduct is excessive or unfair, as there is often not a clear boundary. A case-by-case analysis, which takes into consideration the particularities of each relevant market, does not seem to provide solid ground to devise an applicable test.
Price discrimination
Pricing algorithms can be used for personalised pricing and algorithmic targeting. Personalised pricing involves tailoring prices for different consumers based on information about personal characteristics or behaviour. Algorithmic targeting, on the other hand, allows the firm to price differently for marginal and inframarginal customers (i.e., a distinction is made between ‘core’ and ‘fringe’ customers). Companies can therefore differentiate (and, in a way, discriminate) between the prices displayed to different categories of customers.
The broader welfare implications of personalised advertising are also not entirely clear. Studies have shown that, while the consumer surplus lowers under personalised pricing, more than 60 per cent of customers benefit from a more personalised approach. Nevertheless, the OECD has concluded that there is not much evidence of widespread personalised pricing yet, and while this may change in the coming years, it will not be without facing practical challenges.
Behavioural economics suggests that, while consumers may accept third-degree price discrimination (e.g., lower prices for the elderly and children), consumers do not favour personalised pricing. This is primarily because of considerations of a perceived lack of fairness in different terms being applied to equivalent transactions and a lack of transparency (as pricing decision-making would not be fully understood); therefore, companies may either refrain from adopting personalised pricing strategies to protect their reputation or be less forthcoming and open when they do use personalised pricing. A company’s ability to successfully price-discriminate – while losing only a fraction of marginal customers – could in itself prove that it is able to behave to an appreciable extent independently of its customers and competitors.
Some studies have also analysed whether all price discrimination, or only specific types of it, could be considered abusive. For example, a price-discriminating monopolist could apply lower prices to consumers that have a lower willingness to pay, and higher prices to customers with a higher willingness to pay. This could result in a redistribution of consumer welfare, but by setting prices at a level that is closer to consumer willingness to pay for all consumers, part of that consumer surplus would be transferred to the monopolist.
Price discrimination can amount to an exploitative abuse if it applies dissimilar conditions to equivalent transactions, ultimately putting a customer at a competitive disadvantage. The OECD has put forward a step-by-step guide to assess whether personalised pricing constitutes an exploitative abuse, based on the following five steps: (1) establishing the undertaking’s dominance; (2) identifying non-cost-related price differences; (3) analysing impact on consumer welfare and efficiency; (4) confirming the persistency of the consumer harm; and (5) identifying the precise source of discrimination in light of possible remedies.
In the European Union, the Court of Justice has indicated that the use of Article 102 of the TFEU to sanction exploitative abuses would require a competition authority to meet an elevated burden of proof. Applied to personalised pricing, this jurisprudence would require an authority to show that: (1) price discrimination is applied on a recurring basis; (2) the algorithm consistently discriminates between groups of consumers; (3) there is a lack of objective justifications (e.g., consumer welfare); and (4) the relevant counterfactual shows a negative impact on consumer welfare.
New legislative initiatives in the tech space
Although some of the new EU regulations dealing with issues in the digital sphere do not fall under the remit of competition authorities, it is beyond doubt that these initiatives affect the way in which companies in the European Union may deal with data, algorithms, AI and the display of information to consumers. Accordingly, algorithmic pricing may also be subject to this legislation.
The DMA and the restriction of use of business users’ non-public data
The main DMA provision that is of relevance with regard to algorithmic pricing is Article 6(2), which prohibits gatekeepers in a competitive relationship with business users from deploying non-public data generated by those business users in the context of their use of the relevant core platform services.
The DMA goes on to specify that the concept of ‘non-public data’ includes any aggregated and non-aggregated data generated by business users that originates from the commercial activities of business users or their customers (including click, search, view and voice data) on the relevant core platform services or on services generally provided together with the relevant core platform services.
Given the reliance of algorithmic targeting and pricing on customer and pricing data, this prohibition is liable to significantly restrict the ability of gatekeepers to operate algorithms in this way. That said, only a handful of tech companies have been designated as gatekeepers for core platform services. This restriction will therefore not apply directly to the multitude of smaller players in the digital sphere that may also apply algorithmic targeting for their own products and services.
The DSA, the prohibition of dark patterns and the regulation of recommender systems
Even though the DSA does not regulate pricing practices specifically, it does provide rules on how online platforms organise, display and provide information to consumers. As product prices are only one of the pieces of information generally offered to users of online platforms, it is not unreasonable to consider that the DSA could be applied to instances where algorithmic pricing and targeting is carried out in breach of these obligations.
Article 25 and Article 27 of the DSA, respectively prohibiting ‘dark patterns’ and regulating ‘recommender systems’, seem particularly relevant in this context. For example, if algorithmic pricing or targeting was coupled with other display strategies susceptible of nudging consumers into taking decisions against their best interests, digital service coordinators could have grounds to act to prevent such practices. In the case of recommender systems, any prices featured as part of a product recommendation would probably need to be explained to consumers, including the parameters that can be modified by consumers.
The AI Act and its prohibition of social rankings
Article 5(1)(c) of the EU AI Act prohibits placing AI systems for the evaluation or classification of natural persons or groups of persons over a certain period based on their social behaviour or known, inferred or predicted personal or personality characteristics, with the social score leading to: (1) detrimental or unfavourable treatment of individuals or groups in social contexts unrelated to the original data collection context; or (2) detrimental or unfavourable treatment that is unjustified or disproportionate to the social behaviour or its severity.
On the face of it, the hardcore prohibition enshrined in Article 5(1)(c) of the EU AI Act, and in particular the widely formulated second test (i.e., ‘unfavourable’ and ‘unjustified’), seem liable to restrict activities of companies using AI systems to engage in algorithmic targeting. As explained above, algorithmic pricing implies a distinction in pricing between different groups of customers. It is foreseeable that this prohibition may be invoked against companies targeting a specific customer group (e.g., customers openly displaying a higher willingness to pay) to which subsequently a higher social score is tagged. When this involves discarding customer segments that do not exhibit this type of behaviour, and granting these segments a lower score, the prohibition may kick in.
Conclusion
While algorithmic pricing can enhance dynamic efficiency and benefit consumer welfare, it also harbours risks of anticompetitive behaviour.
A notable concern is its potential to facilitate explicit collusive agreements, violating Article 101 of the TFEU. The United Kingdom’s Topkins case and Spain’s Proptech case illustrate how pricing algorithms were exploited to coordinate prices among competitors. Additionally, hub-and-spoke collusion, as seen in Lithuania’s Eturas case, exemplifies how shared pricing software can lead to tacit agreements among firms.
Algorithmic pricing can also enable unilateral conduct by dominant entities, potentially abusing their market power under Article 102 of the TFEU. This includes predatory pricing strategies aimed at undercutting competitors and using advanced rebate tactics to discourage customer switching; however, assessing such practices is challenging because of the complexities involved in determining whether prices are below cost and what their impact is on consumer welfare.
Exploitative practices in algorithmic pricing, such as excessive pricing and price discrimination, are also concerning issues. While instances of these practices being identified as abuses are rare, algorithms can facilitate them by using personal data. This can lead to uneven pricing conditions and potentially transfer consumer surplus to monopolistic firms, ultimately harming consumers. In the European Union, however, the Court of Justice has set a high standard under Article 102 of the TFEU for proving exploitative abuses in cases of personalised pricing. This clarification is a welcome development in navigating the complexities of algorithmic pricing practices in the context of abuse of dominance, with the requirement for objective justifications adding a certain layer of protection for companies.
Finally, despite the challenges of determining and predicting the extent of anticompetitive practices in this evolving field, the European Union’s legislative initiatives in recent years in the tech sector – the DSA, the DMA and the AI Act – show an aim to use all available tools. The DMA, for instance, seeks to restrict the use of non-public data generated by gatekeepers’ business users in the context of their use of the relevant core platform services, potentially curbing the influence of gatekeepers in algorithmic pricing. While the DSA primarily addresses transparency and fairness in online platform practices rather than pricing directly, it still affects how algorithmic pricing strategies are disclosed to consumers. Similarly, the AI Act introduces prohibitions against AI systems unfairly ranking individuals based on social behaviour, posing implementation challenges for companies using AI in algorithmic targeting.
These legislative efforts illustrate a proactive approach to regulating digital practices, including relating to prices, while acknowledging the complexities involved in addressing potential anticompetitive behaviours in algorithmic environments.
Acknowledgements
The authors thank Jonathan Saké for his contributions to the chapter.
Endnotes
link