Pricing algorithm is a computer program which monitors and/or sets prices and can be possessed and applied unilaterally by a business entity, can be an outsourced software or a simple publicly available price comparison website. Such programs allow to rapidly respond to market changes, adjust prices, optimise business rules and costs and therefore became very popular with both online and block-and-mortar businesses. At the same time, pricing algorithms can distort competition by increasing market transparency, simplifying communication between market players and detection of deviations from collusive agreements and therefore has attracted significant attention of antitrust authorities around the world.ii
Pricing algorithms can be used to facilitate and maintain collusive agreements between the entities as well as can lead to tacit price alignment in cases where entities unilaterally choose algorithms which monitor their rivals’ pricing behaviour and react to it in a certain way or in case companies unilaterally use sophisticated self-learning algorithms,which being programmed for profit maximising can autonomously collude on higher prices.iii
In recent years EU and US antitrust authorities were successful in prosecuting cases of overt collusion facilitated by pricing algorithms. It is generally recognised that overt collusion is expressly prohibited by competition laws per se and collusion facilitated by pricing algorithms is treated like any other concerted actions. Certain exemptions can exist for monitoring of recommended and maximum resale prices, unless such monitoring results into price fixing which shall be considered as prohibited by object.
At the same time, the question of liability for using pricing algorithms in case of tacit price alignment by such algorithmsis is still an open question.
Measures suggested to held entities liable for tacit algorithmic collusion in the EU
Unlike cases of overt collusion facilitated by pricing algorithms,considering the absence of evidence of an agreement prosecuting cases of potential autonomous collusion by pricing algorithms will be challenging. EU competition law provides that business entities can be held liable for anticompetitive concerted actions only when there is evidence of an agreement to collude.iv In its turn, parallel pricing is not itself prohibited by antitrust laws and is considered illegal only if it cannot be explained by any reason other than collusion.v
It means that to held entities liable in case of tacit price alignment antitrust agencies will have to link commercial decisions of entities to algorithmic prices, which will be challenging in the absence of explicit evidence of collusion.vi To do so, antitrust agencies will have to investigate longer periods preceding the alleged collusion to track historical functioning of the algorithm to identify whether the price alignment is a result of self-learning or manual adjustments.vii
German and French antitrust authorities noted that they already have at their disposal means to efficiently investigate price alignment by self-learning autonomous algorithms, namely, information requests, dawn raids and interviews.viii For example, they can request business entities to provide descriptions of implementing principles, explanation of inputs and outputs, usage patterns of the algorithm, frequency of learning, recalibration or manual adjustments, etc.ix Also,an antitrust authority can ask for internal documents such as specifications for the algorithms, user manuals or the code used in the development phase.x It is already known that self-learning algorithms must include a code that reveals an intent to collude.xi The antitrust authorities might investigate the functioning of an algorithm to reveal such code. The authorities can also request a source code of the algorithm to approximate or recreate the algorithm in controlled conditions to understand whether the algorithm was programmed to collude. xii However, considering that such measures would require significant technological resources and competences, it is expected that they will be applied as a last instance measure.
A possibility of shifting the burden of proof
Currently, the burden of proof in anticompetitive concerted actions cases lies with the antitrust authorities. To make it easier for the competition authorities to prosecute anticompetitive behaviour through algorithmic pricing, German Monopoly Commission suggested reversing the burden of proof to entities using pricing algorithms.xiii It means that if the Commission finds indications of anticompetitive algorithmic price alignment in the market, the burden of proof that the use of analgorithm has not contributed to the alignment will be shifted to the entities using such algorithms.
Though reversal of the burden of proof is not something unheard of given that it is already applied in cases of private enforcement with regard to damages caused by cartels, it still remains an open question.xiv However, considering the recent suggestion by Commissioner Vestager to shift the burden of proof to big tech companies (not only in algorithmic pricing cases)xv, such reversal can be only a question of time.
Liability of software developers
To tackle the issues of tacit price alignment by pricing software, it was suggested to revise the liability of third parties to held software providers liable for anticompetitive concerted actions together with the market players which use the algorithm.xvi Art. 101 TFEU provides for a uniform liability of all parties to an agreement restricting competition, without prejudice to the special interests of individual parties, which allows ensuring liability of all participants of a hub-and-spoke cartel.xvii
In means that a software provider may be held liable if it provides the algorithm while being aware of and agreeing to the fact that later users can use the algorithm in the context of collusive pricing (i.e.there is acollusive agreement).xviii However, there might be liability gaps can if the algorithm provider knowingly sells a pricing algorithm producing a collusive market outcome while the buyers of the algorithm do not know and are not able to recognise the collusive market outcome themselves.xix However, it is not clear whether such scenario is likely to happen. Collusive contribution can be viewed as an advantage for users interested as profit maximation, which might encourage software providers to develop and sell such algorithms. And for this to work the buyers are to be aware of such advantage, which limits the possibilities for the above scenario, as far as software providers are only interested in marketing the algorithm as an attractive product but not in the profits associated with the collusion on other markets. In any case, such scenario “cannot or only with difficulty be addressed pursuant to Article 101 TFEU”.xx
To avoid such liability gaps, the German Monopoly Commission suggested that the current rules shall be revised to hold software providers liable irrespective of the behaviour of users of their algorithms to minimise risks of development and marketing of software which can contribute to collusion.
Potential liability solutions for tacit price alignment in the United States
Section 1 of the Sherman Act does not cover cases of parallel actions and can be applied only when there is evidence of collusion.xxi The FTC has emphasised that cases where entities unliterally apply pricing software in the absence of an agreement in any formand which result into price alignmentshall not be prosecuted as collusion.xxii
A solution might potentially be a prosecution under § 45 of the FTC Act, which does not require an agreement but rather a showing of “unfair practice”.xxiii According to the case law, to bring companies liable under this provision the FTC shall provide evidence that defendants either (i) tacitly or expressly agreed to a facilitating device to avoid competition, or (ii) evidence of defendants’ anticompetitive intent or purpose or (iii) the absence of an independent, legitimate business reason for defendants’ conduct.xxiv It means that to held entities liable under §5 of the FTC Act, the FTC shall prove that the engaged entities intended or were aware of potential anticompetitive consequences of the application of pricing algorithms.
So far, the FTC has not been successful in bringing this type of claims.xxv However,such possibility shall not be fully excluded, especially, given the wide range of information and documents which can be assessed by the FTC during dawn raids, such as specifications, manual adjustments, inputs and other data as explained above.
Given the absence of evidence of direct collusion, the issue of liability of business entities in case of tacit collusion by software is still an open question. There is still no case of liability of business entities for autonomous actions of algorithms, though the possibility of holding companies liable for prices aligned through independent use of self-learning algorithms cannot be ruled out.
So far, there no cases of investigation of pricing algorithms by the Antimonopoly Committee of Ukraine, however, Ukrainian competition law has all the basic concepts for prosecution of concerted actions and with time Ukrainian competition authority may well focus on analysing such matters.
*The article is for general guidance only and does not constitute a definitive legal advice.
i.Competition and Markets Authority, ‘Pricing Algorithms: Economic working paper on the use of algorithms to facilitate collusion and personalised pricing’ CMA94 8 October 2018 <https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/746353/Algorithms_econ_report.pdf> assessed 22 January 2019, para 2.7.
ii. Oxera, ‘When Algorithms Set Prices: Winners and Losers’ (2017), Discussion Paper 19 June 2017, 18, <https://www.oxera.com/wp-content/uploads/2018/07/When-algorithms-set-prices-winners-and-losers.pdf.pdf.> assessed 10 February 2020.
iii. Ariel Ezrachi and Maurice E. Stucke, ‘Artificial Intelligence & Collusion: When Computers Inhibit Competition’  University of Illinois Law Review 1775,1782-84.
iv. A. Ezrachi & M. E. Stucke,‘Algorithmic Collusion: Problems and Counter-Measures’(OECD Roundtable on Algorithms and Collusion 21-23 June 2017) para 71, <https://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DAF/COMP/WD%282017%2925&docLanguage=En> , assessed 10 February 2020; see also Article 6 of the Law of Ukraine “On Protection of Economic Competition” No No 2210-III as of 11 January 2001.
v. Case 48/69 ICI v Commission  ECLI:EU:C:1972:70, para 66.
vi. Monopolkommission, ‘Shaping Competition Policy in the Era of Digitisation’ (2018), para 29 <https://ec.europa.eu/competition/information/digitisation_2018/contributions/monopolkomission.pdf> assessed 10 February 2020.
vii. Ibid, para 28.
viii. Bundeskartellamt and Autorité de la Concurrence, ‘Algorithms and Competition’ (2019), 69, < https://www.autoritedelaconcurrence.fr/sites/default/files/algorithms-and-competition.pdf>, assessed 10 February 2020.
ix. Ibid, 65-66.
x. Ibid, 66.
xi. Charley Connor, ‘When robots collude’, 27 September 2019<https://globalcompetitionreview.com/insight/gcr-q3-2019/1202826/when-robots-collude>, assessed 10 February 2020
xii. Ibid, see also Bundeskartellamtand Autorité de la Concurrence (no 8), 67.
xiii. Monopolkommission, ‘Algorithms and Collusion’ (2018), para 239 <https://www.monopolkommission.de/images/HG22/Main_Report_XXII_Algorithms_and_Collusion.pdf>, assessed 12 March 2020.
xiv. Ibid, European Parliament and Council Directive 2014/104/EU on certain rules governing actions for damages under national law for infringements of the competition law provisions of the Member States and of the European Union  OJ L 349, Article 17(2).
xv. Emily Craig, ‘Vestager Considers Shifting Burden of Proof for Big Tech’, 31 October 2019 <https://www.lexology.com/library/detail.aspx?g=b7159a3d-ae2e-4e87-ba37-e59f9200c2c4>, assessed 12 March 2020.
xvi. Monopolkommission (no 13 para34.
xx. ibid, para 266.
xxi. Boise Cascade Corp. v. FTC, 637 F.2d 573.
xxii. Charley Connor (no 11).
xxiii. Federal Trade Commission Act 15 U.S.C. §§ 45; Ezrachi & Stucke (no 3) 1794.
xxiv. Boise Cascade Corp. v. FTC(no 20), 1148;Ethyl Corp. v. FTC, 729 F.2d 128, 142; Ezrachi & Stucke (no 3) 1794.