Publications

Machine learning, or artificial intelligence, refers to a vast array of different algorithms that are being put to highly varied uses, including in transportation, medicine, social media, marketing, and many other settings. Not only do machine-learning algorithms vary widely across their types and uses, but they are evolving constantly. Even the same algorithm can perform quite differently over time as it is fed new data. Due to the staggering heterogeneity of these algorithms, multiple regulatory agencies will be needed to regulate the use of machine learning, each within their own discrete area of specialization. Even these specialized expert agencies, though, will still face the challenge of heterogeneity and must approach their task of regulating machine learning with agility. They must build up their capacity in data sciences, deploy flexible strategies such as management-based regulation, and remain constantly vigilant. Regulators should also consider how they can use machine-learning tools themselves to enhance their ability to protect the public from the adverse effects of machine learning. Effective regulatory governance of machine learning should be possible, but it will depend on the constant pursuit of regulatory excellence.

In this report, the National Board of Trade Sweden highlights the challenges that digital innovation creates for technical regulation. The report questions whether artificial intelligence and cyber vulnerabilities are changing the way industrial goods, such as some smartphones, medical devices, and vehicles with embedded digital technologies, are regulated.

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The COVID-19 pandemic has accelerated ongoing developments in decision-making, which bring the notion of risk and the agencies responsible for its assessment and management at the forefront of the government agenda. The current centrality of risk regulation is dictated by the commitment by governments to achieve ambitious yet imperative sustainability objectives, and by the need to manage the Fourth Industrial Revolution. It lays against the overall debate about the role of science in informing policy decisions and the level of societal acceptance of risk and uncertainty. This chapter briefly takes stock of the fundamentals of risk regulation literature and proposes six topical themes that affect the future position of regulatory agencies – spanning from global crisis management to scientific integrity; from the regulation-innovation interface to enhancing government effectiveness through algorithmic regulation. The chapter then presents ideas on how to shape “Better Risk Regulation”, which will allow governments to adequately address the challenges and meet the opportunities of contemporary society.

Rulemaking in market regulation is accompanied by very strong procedural guarantees (such as motivation, participation and access) and is subject to better regulation tools, such as ex ante and ex post impact assessment and consultations. Starting from the empirical analysis of soft regulations by market regulators (the Anti-Corruption Authority, Bank of Italy, the Italian Companies and Exchange Commission, the Institute for the Supervision of Insurance, the Energy Market and Environment Authority, and the Authority for Communications Guarantees), this paper highlights their regulatory nature on some occasions. Moreover, it examines the perspectives and the criticalities stemming from the hypothetical application of better regulation tools to soft regulation.

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