Outsmarting Pac-Man with Artificial Intelligence, or Why AI-Driven Cartel Screening is not a Silver Bullet

De Cooman, Jérôme

doi:10.1093/jeclap/lpad017

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Outsmarting Pac-Man with Artificial Intelligence, or Why AI-Driven Cartel Screening is not a Silver Bullet

De Cooman, Jérôme

2023 • In Journal of European Competition Law and Practice

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Keywords :

cartel screening; machine learning; duty to state reasons; dawn raid; AI Act

Abstract :

[en] AI-driven cartel screening seems to be the perfect candidate to modernise EU competition law proceedings. However, AI-driven cartel screening raises a data, an algorithmic and a human challenge. Useful insights may be gleaned from the Proposal for an AI Act to solve these drawbacks, i.e., data governance (art. 10), transparency (art. 14), and human oversight (art. 14). Developers and users of AI-driven cartel screening should voluntarily endorse the AI Act to avoid any challenges of EC’s decisions based on violation of the duty to state reasons.

Disciplines :

European & international law

Author, co-author :

De Cooman, Jérôme ; Université de Liège - ULiège > Cité

Language :

English

Title :

Outsmarting Pac-Man with Artificial Intelligence, or Why AI-Driven Cartel Screening is not a Silver Bullet

Publication date :

June 2023

Journal title :

Journal of European Competition Law and Practice

ISSN :

2041-7764

eISSN :

2041-7772

Publisher :

Oxford University Press, Oxford, United Kingdom

Special issue title :

Rubén Perea Award: 3rd Special Issue

Peer reviewed :

Peer Reviewed verified by ORBi

Commentary :

Rubén Perea Award: winner of the third edition

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since 11 April 2023

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Bibliography

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Report on the Functioning of Regulation 1/2003 (n 42) 14.
Report on the Functioning of Regulation 1/2003 (n 42) , 16.
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Harrington (n 68) 245.
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Harrington and Imhof (n 17).
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OECD (n 69) 38
Serena Cocciolo and others, ‘Using Data Analytics in Public Procurement: Operational Options and a Guiding Framework’ (2022) International Bank for Reconstruction and Development/The World Bank, 7 (red flags ‘can be useful in identifying unusual patterns in certain market’ but do not constitute ‘sufficient proof of collusion’). See also Juan M. Ortner and others, ‘Screening Adaptive Cartels’ (2022) NBER Working Paper, http://www.nber.org/papers/w30219 (qualifying ‘statistical screen’ as ‘statistical evidence’ but noting ‘formal prosecution cannot rely on statistical evidence alone’).
Harrington (n 68) 216 (adding at 237 that data ‘supporting collusion need not imply evidence against competition’).
Harrington (n 68), 215.
Ortner (n 78).
Harrington and Imhof (n 17) 135.
Albert Sanchez-Graells, ‘“Screening for Cartels” in Public Procurement: Cheating at Solitaire to Sell Fool’s Gold?’ (2019) 10 Journal of European Competition Law & Practice 199.
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Harrington (n 68) 215.
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David Lehr and Paul Ohm, ‘Playing with the Data: What Legal Scholars Should Learn About Machine Learning’ (2017) 51 UC Davis Law Review 653, 671.
Kevin P. Murphy, Machine Learning: A Probabilistic Perspective (The MIT Press 2012) 1.
Keith Frankish and William M. Ramsey (eds.), The Cambridge Handbook of Artificial Intelligence (Cambridge University Press 2014) 342.
M. Emre Celebi and Kemal Aydin, ‘Preface’ in M. Emre Celebi and Kemal Aydin (eds.), Unsupervised Learning Algorithms (Springer 2016) v.
Ulrich Laitenberger and Kai Hüschelrath, ‘The Adoption of Screening Tools by Competition Authorities’ (2011) 9 CPI Antitrust Chronicle 1
OECD (n 69)
Thibault Schrepel and Teodora Groza, ‘The Adoption of Computational Antitrust by Agencies: 2021 Report’ (2022) 2 Stanford Computational Antitrust 78.
Yuliya Bolotova, John M. Connor and Douglas J. Miller, ‘The Impact of collusion on price behavior: Empirical results from two recent cases’ (2008) 26 International Journal of Industrial Organization 1290 (noting at 1290 that the proposed methodology ‘may be used for antitrust screening and prosecution purposes’). Korbinian von Blanckenburg, Konstantin A. Kholodilin and Alexander Geist, ‘The Influence of Collusion on Price Changes: New Evidence from Major Cartel Cases’ (2011) 13 German Economic Review 245 (noting at 245 that the proposed methodology ‘may be used for antitrust screening and regulatory purposes’).
Huber and Imhof (n 16)
Giovanna Massarotto and Ashwin Ittoo, ‘Gleaning Insight from Antitrust Cases Using Machine Learning’ (2021) 1 Stanford Computational Antitrust; Douglas Silveira and others, ‘Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels’ (2022) 105 Energy Economics 105,711.
Coglianese and Lai (n 11) 8.
Anthony J. Casey and Anthony Niblett, ‘Micro-Directives and Computational Merger Review’ (2021) 1 Stanford Computational Antitrust 132, 133.
Nils Köbis, Christopher Starke and Iyad Rahwan, ‘The promise and perils of using artificial intelligence to fight corruption’ (2022) 4 Nature Machine Intelligence 418.
Rosa M. Abrantes-Metz, ‘Recent successes of screens for conspiracies and manipulations: Why are there still sceptics?’ (2014) CPI Antitrust Chronicle 1, 7
Schrepel (n 14).
von Bonin and Malhi (n 49)
Daryl Lim, ‘Can Computational Antitrust Succeed?’ (2021) 1 Stanford Computational Antitrust 38 (arguing computational antitrust might help forecast killer acquisitions).
Ke Wang and others, ‘An AI-Based Automated Continuous Compliance Awareness Framework (CoCAF) for Procurement Auditing’ (2021) 4 Big Data and Cognitive Computing 43.
Sanchez-Graells (n 52).
Harrington and Imhof (n 17) 154.
David Imhof and Hannes Walliman, ‘Detecting bid-rigging coalitions in different countries and auction formats’ (2021) 68 International Review of Law and Economics 106,016
Martin Huber, David Imhof and Rieko Ishii, ‘Transnational machine learning with screens for flagging bid-rigging cartels’ (2022) 185 Journal of the Royal Statistical Society Series A 1074.
Harrington and Imhof (n 17) 153–154.
Sanchez-Graells (n 52).
This explains why machine learning was not achievable before the advent of large digitised dataset. Ira S. Rubinstein, ‘Big Data: The End of Privacy or a New Beginning?’ (2013) 3 International Data Privacy Law 74
Pedro Domingos, Master Algorithm: How the Question for the Ultimate Learning Machine Will Remake Our World (Basic Books 2015).
von Bonin and Malhi (n 49).
von Bonin and Malhi (n 49)., 469.
See, e.g., Kirsi-Maria Halonen, Roberta Caranta and Albert Sanchez-Graells (eds), Transparency in EU Procurements: Disclosure Within Public Procurement and During Contract Execution (Edward Elgar 2019).
Timothy G. Conley and Francesco Decarolis, ‘Detecting Bidders Groups in Collusive Auctions’ (2016) 8 American Economic Journal: Microeconomics 1
David Imhof, ‘Empirical Methods for Detecting Bid-Rigging Cartels’ (PhD Thesis, Université de Franche-Comté and University of Fribourg 2018)
Sanchez-Graells (n 87)
Huber and Imhof (n 16)
David Imhof, ‘Detecting Bid-Rigging Cartels with Descriptive Statistics’ (2019) 15 Journal of Competition Law and Economics 427
Cocciolo (n 78)
Hannes Wallimann, David Imhof and Martin Huber, ‘A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels’ (2022) Computational Economics, available at: https://doi.org/10.1007/ s10614-022-10315-w.
Mihaly Fazekas and Gabor Kocsis, ‘Uncovering High-Level Corruption: Cross-National Objective Corruption Risk Indicators Using Public Procurement Data’ (2017) 50 British Journal of Political Science 155
Maurizio Lisciandra, Riccardo Milani, Emmanuele Millemaci, ‘A corruption risk indicator for public procurement’ (2022) 73 European Journal of Political Economy, 102,141
Köbis, Starke and Rahwan (n 104).
Marcela Mattiuzzo and Henrique F. Machado, ‘Algorithmic Governance in Computational Antitrust—A Brief Outline of Alternatives for policymakers’ (2022) 2 Stanford Computational Antitrust 23, 29–30.
Sanchez-Graells (n 87).
Sanchez-Graells (n 87)., 209.
Sanchez-Graells (n 87)., 210.
Albert Sanchez-Graells, ‘Data-Driven and Digital Procurement Governance: Revisiting Two Well-Known Elephant Tales’ (2019) 24 Communications Laws 157.
Frank H. Easterbrook, ‘Cyberspace and the Law of the Horse’ (1996) University of Chicago Legal Forum 207
Sanchez-Graells (n 52).
For a similar argument in concentration control, see Casey and Niblett (n 103) 144.
OECD (n 69) 42.
Coglianese and Lai (n 11) 15.
Lehr and Ohm (n 95) 711.
David Danks, ‘Learning’ in Keith Frankish and William M. Ramsey (eds.), The Cambridge Handbook of Artificial Intelligence (Cambridge University Press 2014)
John Zerilli and Adrian Weller, ‘The Technology’ in Matt Hervey and Matthew Lavy (eds.), The Law of Artificial Intelligence (Sweet & Maxwell 2021) 9.
Domingos (n 114).
Rashida Richardson, Jason Schultz and Kate Crowford, ‘Dirty data, bad predictions: how civil rights violations impact police data, predictive policing systems, and justice’ (2019) 94 New York University Law Review Online 192.
Abrantes-Metz and Sokol (n 88) 11.
Sanchez-Graells (n 125)
Ann Cavoukian and Michelle Chibba, ‘Start with Privacy by Design in All Big Data Applications’ in V. Srinivasan (ed.), Guide to Big Data Applications (Springer 2018) 37 (discussing ‘echo chambers’ problem).
Interestingly, it is the risk of type I and type II errors that motivates the qualification of screening as presumption rather than evidence. See Cocciolo (n 78).
Scherer (n 53) 132.
Type I and type II errors are also discussed in legal scholarship related abuse of dominance. See Yannis Katsoulacos and David Ulph, ‘Optimal Enforcement and Decision Structures for Competition Policy: Economic Considerations’ in Federico Etro and Ioannis Kokkoris, Competition Law and the Enforcement of Article 102 (Oxford University Press 2010) 75
Alan Devlin and Michael Jacobs, ‘Antitrust Error’ (2010) 52 William & Mary Law Review 75
Scherer (n 53) 131
Tirole (n 53) 240
George J. Stigler, ‘Theory of Oligopoly’ (1964) 72 Journal of Political Economy 44 (‘with oligopoly, everything is possible’), quoted in Nicolas Petit, ‘The Oligopoly Problem in EU Competition Law’ in Ioannis Lianos and Damien Geradin (eds.), Handbook on European Competition Law (Edward Elgar 2013).
Harrington (n 68) 233.
Daniel Herold and Johannes Paha, ‘Cartels as Defensive Devices: Evidence from Decisions of the European Commission 2001–2010’ (2018) 14 Review of Law & Economics 20,160,035, https://doi.org/10.1515/rle-2016-0035.
Animal Feed Phosphates (Case COMP/38866) Commission Decision of 20 July 2010.
A regression-based approach may overcome this pitfall. Carsten J. Crede, ‘A Structural Break Cartel Screen for Dating and Detecting Collusion’ (2019) 54 Review of Industrial Organization 543, 549
Symeonidis (n 62).
Harrington (n 68) 242.
Schrepel (n 14) 13.
Jan Tinbergen, On the Theory of Economic Policy (North-Holland 1952).
Abrantes-Metz and Sokol (n 88) 11.
Bolotova, Connor and Miller (n 100).
Ivaldi and others (n 63).
Low entry barriers allow the entrance of new competitors that decrease the incentives to collude (Joe S. Bain, Barriers to New Competition (Harvard University Press 1956)
H. Michael Mann, ‘Seller Concentration, Barriers to Entry, and Rates of Return in Thirty Industries, 1950–1960’ (1966) 48 The Review of Economics and Statistics 296
Tirole (n 53) 304–307
Jean J. Gabszewicz, Strategic Interaction and Markets (Oxford University Press 2000)
Harrington (n 58)). Reversely, high entry barrier reduces the probability of new entrants and therefore allows for a high supracompetitive price (Peter Asch and Joseph J. Seneca, ‘Characteristics of Collusive Firms’ (1975) 23 The Journal of Industrial Economics 223).
Groutt and Sonderegger (n 55).
Symeonidis (n 62) 45.
Groutt and Sonderegger (n 55)
Joseph E. Harrington, Jr. and Myong-Hun Chang, ‘Modeling the Birth and Death of Cartel with an Application to Evaluating Competition Policy’ (2009) 7 Journal of the European Economic Association 1400.
94 per cent from 1998 to 2014, 92 per cent from 2010 to 2017
Combe (n 74) 186
de Marcellis-Warin, Marty and Warin (n 50). For up-to-date statistics, see https://ec.europa.eu/competition-policy/system/files/2022-02/cartels_cases_statistics.pdf. Resource scarcity easily explains this prosecution bias. Competition authorities might be tempted to prosecute easy or driven-by a leniency application cases as they only have finite resources (Harrington (n 75)). Leniency programmes are resource efficient as cartelist gives competition authorities evidence on a silver platter by blowing the whistle (Joseph E. Harrington, Jr., ‘Optimal Corporate Leniency Programs’ (2008) 56 The Journal of Industrial Economics 215). Leniency reduces the investigation and prosecution costs by unearthing helpful information for competition authorities and reducing the duration of the procedure (Steffen Brenner, ‘An empirical study of the European leniency program’ (2009) 27 International Journal of Industrial Organization 639). The flip side of the coin is this. Leniency programmes are likely to affect ex officio investigation by taking up all the EC’s limited resources (Joseph E. Harrington, Jr. and Myong-Hun Chang, ‘When Can We Expect a Corporate Leniency Program to Result in Fewer Cartels?’ (2015) 58 The Journal of Law & Economics 417, 419).
Schinkel (n 3) 259.
Joseph E. Harrington, Jr. and Yanhao Wei, ‘What Can the Duration of Discovered Cartels Tell Us About the Duration of All Cartels?’ (2017) 127 The Economic Journal 1977
Casey and Niblett (n 103)
Köbis, Starke and Rahwan (n 104) 419.
William E. Kovacic, Robert C. Marshall and Michael J. Meurer, ‘Serial collusion by multi-product firms’ (2018) 6 Journal of Antitrust Enforcement 296, 298.
Goeffrey T. Fong, David H. Krantz and Richard E. Nisbett, ‘The Effects of Statistical Training on Thinking About Everyday Problems’ (1986) 18 Cognitive Psychology 253.
Ortner (n 78). For concrete example of asymptotically safe tests, see Silvain Chassang and others, ‘Robust screens for noncompetitive bidding in procurement auctions’ (2022) 90 Econometrica 315
Kei Kawai and others, ‘Using Bid Rotation and Incumbency to Detect Collusion: A Regression Discontinuity Approach’ (2022) NBER Working Paper, https://www.nber.org/papers/w29625.
Ortner (n 78).
These observations were first made about automated alarm systems but have been generalised to decision-support systems. Raja Parasuraman and Dietrich Manzey, ‘Complacency and bias in human use of automation: An attentional integration’ (2010) 52 Human Factors 381.
Devlin and Jacobs (n 140).
Stephen R. Dixon and Christopher D. Wickens, ‘Automation reliability in unmanned aerial vehicle flight control: A Reliance compliance model of automation dependence in high workload’ (2006) 48 Human Factors 474.
Huber and Imhof (n 16) 278
Gary T. Marx, ‘The Iron Fist and the Velvet Glove: Totalitarian Potentials within Democratic Structures’ in James E. Short, The Social Fabric: Dimensions and Issues (Sage Publications 1986).
Stephen R. Dixon, Christopher D. Wickens and Jason S. McCarley, ‘On the independence of compliance and reliance: Are automation false alarms worse than misses?’ (2007) 49 Human Factors 564.
Combe (n 74) 182–183
OECD (n 69) 39 (emphasis not added).
Harrington (n 68) 214.
Harrington and Imhof (n 17) 135.
Hans Friederiszick and Frank P. Maier-Rigaud, ‘Triggering inspections ex officio: Moving beyond passive EU cartel policy’ (2008) 4 Journal of Competition Law and Economics 89.
Rosa M. Abrantes-Metz, ‘Ex Officio Cartel Investigations and the Use of Screens to Detect Cartels’ in Directorate for financial and enterprise affairs—Competition committee, ‘Ex Officio cartel investigations and the use of screens to detect cartels’, OECD Competition Law & Policy Roundtables, DAF/COMP(2013)27, 7 July 2014, https://www.fne.gob.cl/wp-content/uploads/2014/07/2013-Ex-officio-cartels-investigatio n-3569-KB1.pdf, 233.
Arthur Conan Doyle, ‘A Scandal in Bohemia’ (July 1989) Strand Magazine, reproduced in Arthur Conan Doyle, The Adventures of Sherlock Holmes (George Newnes Ltd 1892) 7.
Sanchez-Graells (n 125).
Albert Sanchez-Graells and Michael Lewis, ‘Digital Technologies, Hype, and Public Sector Capability’ (How to Crack a Nut, 13 July 2022), available at https://www.howtocrackanut.com/blog/2022/7/13/digital-te chnologies-hype-and-public-sector-capability.
Sanchez-Graells (n 125).
Bob Heyman, ‘The concept of risk’ Bob Heyman and others (eds.), Risk, Safety and Clinical Practice: Health Care through the Lens of Risk (Oxford University Press 2008) 19 (albeit not discussing cartel screening but risk assessment).
Sanchez-Graells (n 125).
OECD, ‘Using Leniency to Fight Hard Core Cartels’ (2001) OECD Policy Brief, available at https://www.oecd.org/daf/ca/1890449.pdf.
Peter G. Bryant and E. Woodrow Eckard, ‘Price Fixing: The Probability of Getting Caught’ (1991) 73 The Review of Economics and Statistics 531.
Combe (n 74) 181. For the record, the probability was between 12.9 and 13.3 per cent in 2007 (Emmanuel Combe, Constance Monnier and Renaud Legal, ‘Cartels: The Probability of Getting Caught in the European Union’ (20 September 2007), available at https://papers.ssrn.com/abstract=1015061), and less than 20 per cent in 2013 (Peter L. Ormosi, ‘A Tip of the Iceberg? The Probability of Catching Cartels’ (2013) 29 Journal of Applied Econometrics 549).
Mario Monti, ‘Fighting Cartels How and Why? (Third Nordic Competition Policy Conference, Stockholm, 11–12 September 2000).
See, e.g., Kuang Chen and others, ‘USHER: Improving data quality with dynamic forms’ (2010) IEEE 26th International Conference on Data Engineering (ICDE), available at https://ieeexplore.ieee.org/document/5447832.
Commission, Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain Union legislative acts, COM (2021) 206 final. At the time of writing, the AI Act is still discussed by the European Parliament and the Council.
Michael Veale and Frederik Zuiderveen Borgesius, ‘Demystifying the Draft EU Artificial Intelligence Act: Analysing the good, the bad, and the unclear elements of the proposed approach’ (2021) 4 Computer Law Review International 97, 103.
Nathalie Smuha and others, ‘How the EU can achieve Legally Trustworthy AI: A Response to the European Commission’s Proposal for an Artificial Intelligence Act’ (5 August 2021), available at http://dx.doi.org/10.2139/ssrn.3899991.
AI Act (n 194) Explanatory Memorandum, 4.
Jerome De Cooman, ‘Humpty Dumpty and High-Risk AI Systems: The Ratione Materiae Dimension of the Proposal for an EU Artificial Intelligence’ (2022) 6 Market and Competition Law Review 49
Albert Sanchez-Graells, ‘Where does the proposed EU AI Act place procurement?’ (How to Crack a Nut, 21 October 2021), available at https://www.howtocrackanut.com/blog/2021/10/21/where-does-the-pro posed-eu-ai-act-place-procurement.
Charter of Fundamental Rights of the European Union, OJ C326, 26 October 2012, 391–407.
T-217/11 Claire Staelen v European Ombudsman, EU:T:2015:238, para 84.
European Ombudsman, ‘The European Code of Good Administrative Behaviour’ available at https://www.ombudsman.europa.eu/pdf/en/3510.
Ian S. Forrester, ‘Due process in EC competition cases: A distinguished institution with flawed procedures’ (2009) 34 European Law Review 817
Herwig C.H. Hofmann and Bucura C. Mihaescu-Evans, ‘The Relation between the Charter’s Fundamental Rights and the Unwritten General Principles of EU Law: Good Administration as the Test Case’ (2013) 9 European Constitutional Law Review 73, 92. See also Hanns Peter Nehl, ‘Good Administration as Procedural Right and/or General Principle?’ in Herwig C.H. Hofmann and Alexander H. Türk eds.), Legal Challenges in EU Administrative Law: Towards an Integrated Administration (Edward Elgar 2009)
Rhita Bousta, ‘Who Said There is a “Right to Good administration”? A Critical Analysis of Article 41 of the Charter of Fundamental Rights of the European Union’ (2013) 19 European Public Law 481
Joana Mendes, ‘Discretion, care and public interests in the EU administration: Probing the limits of law’ (2016) 53 Common Market Law Review 419.
Joined Cases C-141/12 and C-372/12 YS v Minister voor Immigratie, Integratie en Asiel and Minister voor Immigratie, Integratie and Asiel v M and S, ECLI:EU:C:2014:2081, para 68.
European Ombudsman (n 203).
Melanie Fink and Michèle Finck, ‘Reasoned A(I)dministration: Explanation Requirements in EU Law and the Automation of Public Administration’ (2022) 3 European Law Review 376, 383.
Nehl (n 205).
Case C-277/11 M. M. v Minister for Justice, Equality and Law Reform and Others, ECLI:EU:C:2012:744, para 88
Case C-249/13 Khaled Boudjlida v Préfet des Pyrénées-Atlantiques, ECLI:EU:C:2014:2431, para 38
Case C-230/18 PI v Landespolizidirektion Tirol, ECLI:EU:C:2019:383, para 57
Joined Cases C-225/19 and C-226/19 R.N.N.S. and K.A. v. Minister van Buitenlandse Zaken, ECLI:EU:C:2020:951, para 43.
Case T-67/11 Martinair Holland NV v. European Commission, ECLI:EU:T:2015:984, para 31.
Joined cases C-46/87 and C-227/88 Hoechst AG v Commission of the European Communities, ECLI:EU:C:1989:337, para 15
Case C-94/00, Roquette Frères, EU:C:2002:603, paras 44–50; Case C-583/13 P Deutsche Bahn AG and Others v European Commission, ECLI:EU:C:2015:404.
Case T-11/89 Shell International Chemical Company Ltd v Commission of the European Communities, ECLI:EU:T:1992:33, para 39
Case T-25/95 Cimenteries CBR and Others v. Commission of the European Communities, ECLI:EU:T:2000:77, para 718
Case T-9/99 HFB Holding für Fernwärmetechnik Beteiligungsgesellschaft mbH & Co. KG and Others v Commission of the European Communities, ECLI:EU:T:2002:70, para 391
Case T-138/07 Schindler v. European Commission, ECLI:EU:T:2011:362, para 54.
Valico S.r.l. v. Italy App no. 70074/01 (ECtHR, 21 mars 2006) 20.
Schindler (n 214) para 55. See similarly Joined cases 100 to 103/80 SA Musique Diffusion française and others v. Commission of the European Communities, ECLI:EU:C:1983:158
Cases T-56/09 and T-73/09 Saint Gobain v. European Commission, ECLI:EU:T:2014:160
Case C-702/19 P Silver Plastics GmbH & Co. KG and Johannes Reifenhäuser Holding GmbH & Co. KG v. European Commission, ECLI:EU:C:2020:857
Case C-311/18 Data Protection Commissioner v. Facebook Ireland Ltd and Maximilian Schrems, ECLI:EU:C:2020:559.
Hoechst (n 213), para 15. See also Imran Aslam and Michael Ramsden, ‘EC Dawn Raids: A Human Rights Violation?’ (2008) 5 The Competition Law Review, 61
Riina Autio, ‘Explaining Dawn Raids: A Soft Law Perspective into European Competition Authorities’ Explanatory Notes on Unannounced Inspections’ (2020) 11 Journal of European Competition Law & Practice, 475
Jérémie Jourdan and Henry Gafsen, ‘Competition Law and Fundamental Rights: Survey of Recent Developments Concerning the Right to Good Administration, Dawn Raids, Legal Professional Privilege, and Other Procedural Rights’ (2022) 13 Journal of European Competition Law & Practice, 304.
Roquette Frères (n 213), EU:C:2002:603, para 61; Case T-340/04, France Télécom SA v Commission of the European Communities, EU:T:2007:81, para 53. The concept of reasonable suspicion is similarly found in national competition law. See Jorge G. Contreras Condezo, Annabel Kingma and Miroslava Scholten, ‘Putting Dawn Raids under Control’ (2020) 22 Yearbook of Antitrust and Regulatory Studies, 145.
Case C-37/13 P, Nexans and Nexans France v Commission, EU:C:2014:2030, para 34
Case C-247/14 P HeidelbergCement AG v European Commission, ECLI:EU:C:2016:149, para 19.
Joined cases C-97/87 to C-99/87 Dow Chemical Ibérica and Others v Commission, EU:C:1989:380, para 45
Case 85/87 Dow Benelux v Commission ECLI:EU:C:1989:379, para 10.
Case T-339/04 France Télécom v Commission, EU:T:2007:80, paras 58–59
Joined cases T-289/11, T-290/11 and T-521/11 Deutsche Bahn and Others v Commission, EU:T:2013:404, paras 75–77.
Hoechst (n 213) para 29.
Dow Benelux (n 221), para 11
Case T-249/17, Casino and Achats Marchandises Casino SAS (AMC) v European Commission, ECLI:EU:T:2020:458, para 112.
Case T-135/09 Nexans France and Nexans v Commission, EU:T:2012:596, paras 52–59
Case T-140/09 Prysmian SpA and Prysmian Cavi e Sistemi Energia Srl v European Commission, ECLI:EU:T:2012:597, paras 43–52.
Nexans France (n 225), para 91
Prysmian (n 225), para 89.
HeidelbergCement (n 220), para 39. This case concerned a request for information under Article 18(3) of Regulation 1/2003 but it has been argued its conclusion may be applied mutatis mutandis to dawn raid. Helene Andersson, Dawn Raids Under Challenge: Due Process Aspects on the European Commission’s Dawn Raid Practices (Hart Publishing 2018) 86.
Fink and Finck (n 209) 385.
Frank Pasquale, ‘Inalienable Due Process in an Age of AI: Limiting the Contractual Creep toward Automated Adjudication’ in Hans-W. Micklitz and others, Constitutional Challenges in the Algorithmic Society (Cambridge University Press 2021). See also Bryce Goodman and Seth Flaxman, ‘European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”’ (2017) 38 AI Magazine 50; Jenifer Cobbe, ‘Administrative law and the machines of government: judicial review of automated public-sector decision-making’ (2019) 39 Legal Studies 636.
Finale Doshi-Velez and others, ‘Accountability of AI Under the Law: The Role of Explanation’ (2019) arXiv working paper, available at: https://arxiv.org/abs/1711.01134.
Karen Yeung, ‘Why Worry about Decision-Making by Machine?’ in Karen Yeung and Martin Lodge (eds.), Algorithmic Regulation (Oxford University Press 2019)
Fink and Finck (n 209).
Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information (Harvard University Press 2016)
Henrik P. Olsen, Jacob L. Slosser and Thomas T. Hildebrandt, ‘What’s in the Box? The Legal Requirement of Explainability in Computationally Aided Decision-Making in Public Administration’ in Hans-W. Micklitz and Others (eds.), Constitutional Challenges in the Algorithmic Society (Cambridge University Press 2022).
Norbert Wiener, God and Golem, Inc.: A Comment on Certain Points where Cybernetics Impinges on Religion (The MIT Press 1964) 42.
Cary Coglianese and David Lehr, ‘Regulating by Robot: Administrative Decision Making in the Machine Learning Era’ (2017) 105 Georgetown Law Review 1147, 1167; Lehr and Ohm (n 95).
PI v Landespolizidirektion Tirol (n 211) para 57.
For a discussion on the effectiveness of explicability, see Sandra Wachter, Brent Mittelstadt and Luciano Floridi, ‘Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation’ (2017) 7 International Data Privacy Law 76
Lilian Edwards and Michael Veale, ‘Slave to the Algorithm? Why a “Right to an Explanation” Is Probably Not the Remedy You Are Looking For’ (2017) 16 Duke Law & Technology Review Journal 18.
Independent High-Level Expert Group on Artificial Intelligence set up by the European Commission, ‘Ethics Guidelines for Trustworthy AI’ (8 April 2019) 13.
Aslam and Ramsden (n 218) 61.
Costanza Nicolosi, ‘No fishing at dawn (raids): defining the scope of the commission’s inspection power in antitrust proceedings’ (2016) 7 Queen Mary Law Journal, 53, 54.
Contreras Condezo, Kingma and Scholten (n 229) 145.
Adam Steene, ‘Nexans, Deutsche Bahn, and the ECJ’s Refusal to Follow ECHR Case Law on Dawn Raids’ (2016) 7 Journal of European Competition Law & Practice, 180.
HLEG (n 240) 12.
Louis D. Brandeis, Other People’s Money and How the Bankers Use It (Stokes 1914) 92.
Huber and Imhof (n 16)
Supreeth P. Shashikumar and others, ‘Artificial intelligence sepsis prediction algorithm learns to say “I don’t Know”’ (2021) 4 npj Digital Medicine https://www.nature.com/articles/ s41746-021-00504-6#citeas.
HLEG (n 240) 16.
Jurimetrics is the discipline that use probability and statistics to answer legal question. See Lee Loevinger, ‘Jurimetrics: Science and Prediction in the Field of Law’ (1961) 46 Minnesota Law Review 255, 275.
Coglianese and Lai (n 1(n1) 18; Harrington and Imhof (n 17) 144.
European Group on Ethics in Science and New Technologies, ‘Statement on Artificial Intelligence, Robotics and “Autonomous” Systems’ (March 2018) available at https://op.europa.eu/en/publication-detail/-/publication/dfebe62e-4ce9-11e8-be1d-01aa75ed71a1/language-en/format-PDF/ source-78120382.
Danielle Keats Citron, ‘Technological Due Process’ (2008) 85 Washington University Law 1249, 1252.
Sanchez-Graells (n 52).
Madalina Busuioc, ‘Accountable Artificial Intelligence
Holding Algorithms to Account’ (2022) 81 Public Administration Review 825.
Cass R. Sunstein, ‘Governing by Algorithm? No Noise and (Potentially) Less Bias’ (2022) 71 Duke Law Journal 1175.
Michel Callon and Bruno Latour, ‘Le grand Léviathan s’apprivoise-t-il?’ in Madeleine Akrich, Michel Callon and Bruno Latour, Sociologie de la traduction: Textes fondateurs (Presses des Mines 2006) 16 (free translation). The black box argument was updated to fit the philosophy of technology. See Langdon Winner, ‘Social constructivism: Opening the black box and finding it empty’ (1993) 3 Science as Culture 427
Langdon Winner, ‘Upon Opening the Black Box and Finding It Empty: Social Constructivism and the Philosophy of Technology’ (1993) 18 Science, Technology, & Human Values 362
Noemi Waight and others, ‘Towards Equitable, Social Justice Criticality: Re-Constructing the “Black” Box and Making it Transparent for the Future of Science and Technology in Science Education’ (2022) Science & Education, https://doi.org/10.1007/ s11191-022-00328-0.
Lim (n 106)
Cary Coglianese and Alicia Lai, ‘Algorithm vs. Algorithm’ (2022) 72 Duke Law Journal 1281
Köbis, Starke and Rahwan (n 104).
Mattiuzzo and Machado (n 120) 31.
Busuioc (n 259) 832.
Brian J. Fogg, Persuasive Technology: Using Computers to Change What We Think and Do (Elsevier 2003).
Karen Yeung, ‘Algorithmic regulation: A critical interrogation’ (2018) 12 Regulation and Governance 505.
I.e., is not an automated enforcement system. See Gary T. Marx, ‘The Engineering of Social Control: The Search for the Silver Bullet’ in John Hagan and Ruth Peterson (eds.), Crime and Inequality (Stanford University Press 1995).
Yeung (n 266) 516; Simon Chesterman, We, the Robots? Regulating Artificial Intelligence and the Limits of the Law (Cambridge University Press 2021).
Sanchez-Graells (n 52).
Nicolas Petit, ‘Artificial Intelligence and Automated Law Enforcement: A Review Paper’ (21 March 2018) available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3145133. For the usefulness of administrative discretion, see Sofia Ranchordas, ‘Empathy in the Digital Administrative State’ (2022) 71 Duke Law Journal 1341.
Citron (n 257) 1254.
Nils Köbis, Jean-François Bonnefon, and Iyad Rahwan, ‘Bad machines corrupt good morals’ (2021) 5 Nature Human Behavior 679.
Raja Parasuraman and Victor Riley ‘Humans and automation: Use, misuse, disuse, abuse’ (1997) 39 Human Factors 230
Kathleen Mosier and others, ‘Automation bias and errors: Are teams better than individuals?’ (1998) 42 Proceedings of the Human Factors and Ergonomics Society Annual Meeting 201
Linda J. Skikta, Kathleen L. Mosier and Mark D. Burdick, ‘Does automation bias decision-making?’ (1999) 51 International Journal of Human-Computer Studies 99
Linda J. Skikta, Kathleen L. Mosier and Mark D. Burdick, ‘Accountability and automation bias’ (2000) 52 International Journal of Human-Computer Studies 701
Linda J. Skikta and others, ‘Automation bias and errors: Are crews better than individuals?’ (2000) 10 The International Journal of Aviation Psychology 85
Kathleen Mosier and others, ‘Aircrews and automation bias: the advantages of teamwork?’ (2001) 11 The International Journal of Aviation Psychology 1
Dixon and Wickens (n 173)
Mary L. Cummings, ‘Automation and accountability in decision support system interface design’ (2006) 32 The Journal of Technology Studies 23
Dixon, Wickens and McCarley (n 175)
Parasuraman and Manzey (n 171)
Kate Goddard, Abdul Roudsari and Jeremy C. Wyatt, ‘Automation bias: a systemic review of frequency, effect mediators, and mitigators’ (2012) 19 Journal of the American Medical Informatics Association 121
Christopher D. Wickens and others, ‘Complacency and automation bias in the use of imperfect automation’ (2015) 57 Human Factors and Ergonomics Society 728
David Lyell and Enrico Coiera, ‘Automation bias and verification complexity: a systematic review’ (2017) 24 Journal of the American Medical Informatics Association 423
Saar Alon-Barkat and Madalina Busuioc, ‘Human-AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice’ (2022) Journal of Public Administration Research and Theor available at https://doi.or /10.1093/jopart/muac007.
Sanchez-Graells (n 52)
Wickens and others (n 273).
Daniel Kahneman, Thinking Fast and Slow (Farrar, Straus and Giroux 2011)
Richard H. Thaler, Misbehaving: The Making of Behavioral Economics (W.W. Norton & Company, Inc. 2015)
Daniel Kahneman, Olivier Sibony and Cass R. Sunstein, Noise: A Flaw in Human Judgement (Little, Brown Spark 2021).
Cristina Teleki, Due Process and Fair Trial in EU Competition Law: The Impact of Article 6 of the European Convention on Human Rights (Brill 2021) 264.
Eva Lachnit, Alternative Enforcement of Competition Law (Eleven International Publishing 2016) 88.
Wouters. P. J. Wils, ‘The Combination of the Investigative and Prosecutorial Function and the Adjudicative Function in EC Antitrust Enforcement: A Legal and Economic Analysis’ (2004) 27 World Competition, 201.
Wouters. P. J. Wils, ‘The Combination of the Investigative and Prosecutorial Function and the Adjudicative Function in EC Antitrust Enforcement: A Legal and Economic Analysis’ (2004) 27 World Competition, 216.
Teleki (n 280) 266.
Dan L. Burk, ‘Algorithmic Fair Use’ (2019) 86 The University of Chicago Law Review 283, 306.
Fogg (n 265).
Nathalie De Marcellis-Warin and Thierry Warin, ‘Les mégadonnées entre possibilités et risques: à la recherche d’un nouvel équilibre’ (2017) 42 Gestion 72.
David Imhof, Yavuz Karagoek and Samuel Rutz, ‘Screening for bid-rigging: does it work?’ (2018) 14 Journal of Competition Law and Economics 235.
Wallimann, Imhof and Hubert (n 118).
Peter Busch and Helle Henriksen, ‘Digital discretion: A systematic literature review of ICT and street-level discretion’ (2018) 23 Information Polity, 3
Rik Peeters, ‘The agency of algorithms: Understanding human-algorithm interaction in administrative decision-making’ (2020) 25 Information Polity 507
Busuioc (n 259)
Noortje de Boer and Nadine Raaphorst, ‘Automation and discretion: Explaining the effect of automation on how street-level bureaucrats enforce’ (2021) Public Management Review, available at https://doi.org/10.1080/14719037.2021.1937684.
Thomas Nachbar, ‘Algorithmic Fairness, Algorithmic Discrimination’ (2020) Virginia Public Law and Legal Theory Research Paper No. 2020-11, available at https://ssrn.com/abstract=3530053.
Marjolein Lanzing, ‘“Strongly Recommended” Revisiting Decisional Privacy to Judge Hypernudging in Self-Tracking Technologies’ (2019) 32 Philosophy & Technology 549, 562. See also Xavier Amatriain and Justin Basilivo, ‘Recommender Systems in Industry: A Netflix Case Study’ in Francesco Ricci, Lior Rokach and Bracha Shapira (eds), Recommender Systems Handbook (2nd ed., Springer 2015) 391
Jonathan Cohn, The Burden of Choice: Recommendations Subversion, and Algorithmic Culture, New Brunswick, Rutgers University Press, 2019.
Casey and Niblett (n 103) 145
Isabelle Adam and Mihaly Fazekas, ‘Are emerging technologies helping win the fight against corruption? A review of the state of evidence’ (2021) 57 Information Economics and Policy https://doi.org/10.1016/j.infoecopol.2021.100950.
Nachbar (n 294).
Matthias Leese, ‘The New Profiling: Algorithms, Black Boxes, and the Failure of Antidiscriminatory Safeguards in the European Union’ (2014) 45 Security Dialogue 494
Brent Mittelstadt and others, ‘The Ethics of Algorithms: Mapping the Debate’ (2016) 3 Big Data & Society 1.
Commission notice on best practices (n 38) 13.
von Bonin and Malhi (n 49) 469.
George Alexandru Adam and others, ‘Hidden Risks of Machine Learning Applied to Healthcare: Unintended Feedback Loops Between Models and Future Data Causing Model Degradation’ (2020) 126 Proceedings of Machine Learning Research 710, 722. This solution is in line with art. 15(3) AI Act.
Smuha (n 196).
Fink and Finck (n 209) 387 n 69.
Frederic M. Scherer, Competition Policies for an Integrated World Economy (The Brookings Institution 1994) 92.
Dirk Vandermeersch, ‘De mededingingsregels en hun handhaving: de hervorming van 2013’, in Alex Tallon (ed.), Le Nouveau Code de droit économique—Het Nieuwe Wetboek van Economisch Recht, Brussels, Larcier, 2014, pp. 53–54
Norman Neyrinck, Manuel de droit belge de la concurrence. Les pratiques restrictives de concurrence (Larcier 2021). Belgian Competition Authority separates investigation (the Auditorat) and fining (Collège de la concurrence).
Loi de modernisation de l’économie n◦ 2008–776 du 4 août 2008
Ordonnance n◦ 2008–1161 du 13 novembre 2008. French Competition Authority separates investigation (Service d’Instruction) and fining (College).
Bruno Lasserre, ‘The new French competition law enforcement regime’, Competition Law International, Vol. 5, No. 3, 2009, pp. 15–20.
Wouter Devroe, Bruno Van den Bosch and Frances Van den Bogaert, ‘Antitrust Enforcement in Belgium’ in Tihamer Toth (ed.), The Cambridge Handbook of Competition Law Sanctions (Cambridge University Press 2022) 328.
Lachnit (n 281) 42.
Lachnit (n 281), 87.
Lachnit (n 281) 342.
Decision of the President of the European Commission of 13 October 2011 on the function and terms of reference of the hearing officer in certain competition proceedings, OJ L 275, 20 October 2011, pp. 29–37.
Ibid., Article 4.
For a similar argument, see Fink and Finck (n 209) 387.