Publications

Summary: - Introduzione, di Federica Cacciatore e Nicoletta Rangone - Le nuove indicazioni OCSE in tema di qualità della regolazione: i best practice principles sulla valutazione ex post, di Siriana Salvi - Doing Business 2020. L’importanza della qualità e della trasparenza regolatoria per lo sviluppo economico e imprenditoriale, di Simone Annaratone - Macchine “intelligenti” e discrezionalità delle decisioni pubbliche. Alcuni spunti di riflessione sulla regolazione europea e italiana, di Patrizia Calabrese - La Relazione al Parlamento sullo stato di applicazione di AIR e VIR nel 2018, di Simona Morettini - IAL Annual Conference: analisi della crisi di fiducia nella legge e nella regolazione, di Rossana Amoroso - Recensione. Chi partecipa alle consultazioni delle Agenzie europee? I fattori che influenzano la cattura del regolatore, nella ricerca di Jan Beyers e Sarah Arras, di Carolina Raiola

Complex machine learning models derived from personal data are increasingly used in making decisions important to peoples’ lives. These automated decision tools are controversial, in part because their operation is difficult for humans to grasp or explain. While scholars and policymakers have begun grappling with these explainability concerns, the debate has focused on explanations to decision subjects. This Essay argues that explainability has equally important normative and practical ramifications for decision-system design. Automated decision tools are particularly attractive when decisionmaking responsibility is delegated and distributed across multiple actors to handle large numbers of cases. Such decision systems depend on explanatory flows among those responsible for setting goals, developing decision criteria, and applying those criteria to particular cases. Inscrutable automated decision tools can disrupt all of these flows. This Essay focuses on explanation’s role in decision-criteria development, which it analogizes to rulemaking. It analyzes whether, and how, decision tool inscrutability undermines the traditional functions of explanation in rulemaking. It concludes that providing information about the many aspects of decision tool design, function, and use that can be explained can perform many of those traditional functions. Nonetheless, the technical inscrutability of machine learning models has significant ramifications for some decision contexts. Decision tool inscrutability makes it harder, for example, to assess whether decision criteria will generalize to unusual cases or new situations and heightens communication and coordination barriers between data scientists and subject matter experts. The Essay concludes with some suggested approaches for facilitating explanatory flows for decision-system design.

The EU Commission regularly consults with external actors when preparing policy proposals. This paper investigates possible bias in the voices the Commission listens to across all policy areas and consultation instruments. We map the full range of consultation instruments used by the Commission and analyse variation in group representation across types of consultations and policy areas. Our analysis draws on a dataset of more than 350 major Commission proposals between 2011 and 2016. We find that the Commission has established a consultation regime with the widespread use of standard consultation instruments, including open online consultations, stakeholder conferences and consultation in closed fora. The range of consulted actors depends on both consultation instruments and policy types. Open online-consultations are most inclusive. Our findings indicate that the Commission may be able to counteract the influence of business by selecting the most appropriate format for consultation.