Publications

Cross-sectorial networks of regulators are starting to be adopted as a horizontal coordination mechanism. UKRN is one of the first horizontal regulators’ platform, starting its activity in 2014, and now focusing on extending sectorial disclosure-regulation by developing cross-sectorial performance scorecard aimed to compare and assess the customer experience of companies in different sectors (Antonio Manganelli, Rassegna Osservatorio Air n. 4/2019).

Effective antitrust enforcement requires that national authorities are empowered with strong intervention powers and real independence, both from the industry, as well as from the political power. In the field of European competition law, it is also necessary that the powers and the independence of national antitrust authoritiesare harmonised, in order to allow a real cooperation among them and between them and the European Commission. The Directive (EU) 2019/1 of the European Parliament and of the Council of 11 December 2018 is aimed at increasing the independence, resources and powers of NCAs and envisages more harmonisation of the national leniency programmes and reduced burdens on undertakings (Livia Lorenzoni, Rassegna Osservatorio Air n. 4/2019).

AGCOM, AGCM and the Italian Data Protection Authority jointly adopted policy recommendations to contain the power deriving from the use of big data in digital markets. The document, which is the result of a wide survey, not only proposes concrete solutions to counter the “power of data” of the great digital operators, but also outlines a stronger and more active role of the public sector in the regulation and free circulation of data (Matteo Falcone, Rassegna Osservatorio AIR n. 4/2019).

Introduzione. Strumenti (avanzati) di better regulation e realtà dei processi decisionali, di Gabriele Mazzantini e Nicoletta Rangone Capitolo primo. Il quadro internazionale ed europeo, di Laura Cavallo Capitolo secondo. L’AIR e la VIR, di Davide Zaottini Capitolo terzo. L’analisi d’impatto della regolazione sulla concorrenza, di Gabriele Mazzantini Capitolo quarto. Le consultazioni pubbliche, di Carolina Raiola Capitolo quinto. La trasparenza e l’accountability delle autorità indipendenti, di Emma Galli, Ilde Rizzo e Carla Scaglioni Capitolo sesto. La funzione di supervisione. Parlamento, autorità amministrative indipendenti e AIR: un connubio possibile, di Valerio Di Porto Capitolo settimo. Compliance ed enforcement della regolazione indipendente, di Livia Lorenzoni e Florentin Blanc Capitolo ottavo. Il contributo del Consiglio di Stato e dei TAR alla qualità della regolazione, di Simona Morettini

Artificial intelligence (AI) is becoming a part of our daily lives at a fast pace, offering myriad benefits for society. At the same time, there is concern about the unpredictability and uncontrollability of AI. In response, legislators and scholars call for more transparency and explainability of AI. This article considers what it would mean to require transparency of AI. It advocates looking beyond the opaque concept of AI, focusing on the concrete risks and biases of its underlying technology: machine-learning algorithms. The article discusses the biases that algorithms may produce through the input data, the testing of the algorithm and the decision model. Any transparency requirement for algorithms should result in explanations of these biases that are both understandable for the prospective recipients, and technically feasible for producers. Before asking how much transparency the law should require from algorithms, we should therefore consider if the explanation that programmers could offer is useful in specific legal contexts.

Also in the digital age, markets work properly as long as consumers are well informed. What is peculiar of the digital age is that consumers have become very fragile, also because firms can extensively manipulate the information that they produce and distribute to markets. Antitrust authorities may find their way to prosecute business manipulative conduct, as some rulings suggest. However, the enforcement of antitrust law is subject to precise circumstances and requires cumbersome proceedings, especially when dominant firms are involved. Therefore, a simpler and more widespread intervention is needed. Although over the years traditional disclosure regulation has showed its limits, today algorithmic analysis gives room to more effective forms of disclosure regulation. Therefore, the paper maintains that both regulators and antitrust authorities can use these new forms of disclosure regulation to perform better their functions. Thanks to algorithmic analysis, (a) regulators can provide consumers with targeted co-regulated disclosures; (b) while competition authorities, using their advocacy powers, can provide trustworthy rankings and reviews about firms’ ability to comply with antitrust and consumer protection laws.

The emergence of behavioral public administration has led to increasing calls for public managers and policy makers to consider predictable cognitive biases when regulating individual behaviors or market transactions. Recognizing that cognitive biases can also affect the regulators themselves, this article attempts to understand how the institutional environment in which regulators operate interacts with their cognitive biases. In other words, to what extent does the “choice architecture” that regulators face reinforce or counteract predictable cognitive biases? Just as knowledge of behavioral insights can help regulators design a choice architecture that frames individual decisions to encourage welfare‐enhancing choices, it may help governments understand and design institutions to counter cognitive biases in regulators that contribute to deviations from public interest policies. From these observations, the article offers some modest suggestions for improving the regulatory choice architecture.

Un’invisibile nuvola ci avvolge. Sono i dati e le informazioni che scambiamo online, un flusso continuo che qualcuno raccoglie, elabora e scambia. È vero che grazie ai big data accediamo a servizi sempre più ritagliati sulle nostre necessità, ma ogni nostra mossa, ogni acquisto, ogni comunicazione, ogni nostro momento pubblico e privato è osservato. Di ciascuno di noi esiste da qualche parte nell’etere un profilo. Utile a chi vuole influenzare le nostre scelte, di consumo ma anche politico-elettorali. E magari a chi vorrà approfittare delle nostre debolezze e dei nostri segreti.

The contributors come from a broad range of disciplinary perspectives including law, public administration, applied philosophy, data science, and artificial intelligence 1: Algorithmic Regulation: An Introduction, Karen Yeung and Martin Lodge Part I: Normative concerns 2: Why worry about decision-making by machine?, Karen Yeung 3: Machine decisions and human consequences, Teresa Scantamburlo, Andrew Charlesworth and Nello Cristianini 4: Digital discrimination, Natalia Criado and Jose M Such 5: The ethics of algorithmic outsourcing in everyday life, John Danaher Part II: Public sector applications 6: Administration by Algorithm? Public Management meets Public Sector Machine Learning, Michael Veale and Irina Brass 7: The Practical Challenges of Implementing Algorithmic Regulation for Public Services, Alex Griffiths 8: Reflecting on Public Service Regulation by Algorithm, Martin Lodge and Andrea Mennicken Part III: Governing algorithmic systems 9: Algorithms, regulation and governance readiness, Leighton Andrews 10: Legal practitioners' approach to regulating AI risks, Jason D Lohr, Winston J Maxwell and Peter Watts 11: Minding the machine v2.0: The EU General Data Protection Regulation and Automated Decision Making, Lee A Bygrave

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