Publications

Algorithms are among the most important technological drivers of the ongoing digitalization process. They are becoming more and more important, enabling firms to be more innovative and efficient. However, debate has arisen on whether and to what extent algorithms might also have detrimental effects on the competitive functioning of markets. In their joint conceptual project – Algorithms and Competition – the Autorité de la concurrence and the Bundeskartellamt studied potential competitive risks that might be associated with algorithms. They elaborated on the concept of algorithm as well as on different types and fields of application. In their study, the two authorities focused in particular on pricing algorithms and collusion, but also considered potential interdependencies between algorithms and the market power of the companies using them as well as practical challenges when investigating algorithms. Isabelle de Silva, President of the Autorité de la concurrence: “Algorithms are used constantly in the digital economy, and are at the very core of how some fast growing businesses operate: online travel agencies, e-commerce, online advertising, to name only a few. It is essential that we look into how these algorithms work. We need to determine if there is a risk that algorithms might facilitate or permit behaviours that are contrary to competition law. With this joint study with the Bundeskartellamt we aim at reaching a common view on these matters and at starting a debate with stakeholders.” Andreas Mundt, President of the Bundeskartellamt: “The joint study is another proof of the continuing cooperation between our agencies. As digital markets keep evolving, we expand our expertise on algorithms in an exchange with each other. This is in line with our efforts to devote more resources to the digital economy with the clear-cut aim to enforce competition law also in the era of platform economy and digital business models.” Algorithms and competition are also the topic of an accompanying conference hosted by the Autorité de la concurrence and the Bundeskartellamt that is taking place in Paris today. Several renowned speakers, including business representatives, researchers and competition enforcers, are discussing potential business applications for algorithms, pricing algorithms and the risk of horizontal collusion, as well as ways to address the challenges raised by algorithms.

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The world’s largest businesses must routinely police other businesses. By public mandate, Facebook monitors app developers’ privacy safeguards, Citibank audits call centers for deceptive sales practices, and Exxon reviews offshore oil platforms’ environmental standards. Scholars have devoted significant attention to how policy makers deploy other private sector enforcers, such as certification bodies, accountants, lawyers, and other periphery “gatekeepers.” However, the literature has yet to explore the emerging regulatory conscription of large firms at the center of the economy. This Article examines the rise of the enforcer-firm through case studies of the industries that are home to the most valuable companies, in technology, banking, oil, and pharmaceuticals. Over the past two decades, administrative agencies have used legal rules, guidance documents, and court orders to mandate that private firms in these and other industries perform the duties of a public regulator. More specifically, firms must write rules in their contracts that reserve the right to inspect third parties. When they find violations, they must pressure or punish the wrongdoer. This form of governance has important intellectual and policy implications. It imposes more of a public duty on the firm, alters corporate governance, and may even reshape business organizations. It also gives resource-strapped regulators promising tools. If designed poorly, however, the enforcer-firm will create an expansive area of unaccountable authority. Any comprehensive account of the firm or regulation must give a prominent role to the administrative state’s newest gatekeepers.

The draft OECD Best Practice Principles for Regulatory Policy: Reviewing the Stock of Regulation [the Principles] is hereby presented for public consultation. The aim of the Principles is to provide policymakers and civil servants in both OECD member and partner countries with a practical instrument to better design and operationalise their ex post evaluation systems. The objective is to complement the 2012 Recommendation on Regulatory Policy and Governance which, albeit clear on the importance of undertaking evaluations in the overall context of the regulatory lifecycle, is rather general on providing specific guidance. In these Principles, particular attention is paid to ensuring that regulation is comprehensively reviewed over time, and that the reviews include an assessment of the actual outcomes achieved against their rationales and objectives. The Principles further expand on these areas by covering both the overall governance of ex post evaluation systems and individual reviews, various methodologies that can be used as assessment tools, public consultation throughout the review process, as well as capacity building and leadership.

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Over the last decade the Open Government Data movement has successfully highlighted the value of data and encouraged governments to open up information for reuse both inside, and outside the public sector. This Working Paper argues that governments now need to go further and put the role and value of data at the core of thinking about the digital transformation of government. A data-driven public sector (DDPS) recognises that data are an asset, integral to policy making, service delivery, organisational management and innovation. The strategic approach governments take to building a DDPS can have a positive impact on the results they deliver by promoting evidence-led policy making and data-backed service design as well as embedding good governance values of integrity, openness and fairness in the policy cycle.

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Radical innovations and break-through technologies are desperately needed in solving to-day’s difficult societal challenges, such as those created by climate change or ageing demographics. However, addressing complex societal challenges requires elaborate systemic planning, determined investments and often also, visionary and brave decisions by the legislators and regulators. While radical innovation may bring much needed economic benefits and solutions to pressing societal challenges, they can also generate new risks and ethical dilemmas. Hence, today’s legislators are faced with difficult questions in trying to foresee an optimal legal framework, which would sufficiently leave space for and encourage new solutions, but at the same time would ensure safe conditions and fair benefits to everyone. In light of the above, increased attention is paid to developing innovation-friendly regulatory approaches and practices. The introduction of European Commission’s Innovation Principle, as well as several national initiatives (such as regulatory sandboxes and regulation roadmaps), are good examples of such development. So far, there has not been a common definition, nor a comprehensive framework to grasp the different aspects of innovation-friendly regulation approaches and practices. Developing such framework has been one of the main objectives in Finnish government commissioned study on “Impacts of regulation on innovation and new markets”. This Policy Brief presents some first findings and introduces a draft framework for innovation-friendly regulation.

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Frontline practitioners in the public sector – from social workers to police to custody officers – make important decisions every day about people’s lives. Operating in the context of a sector grappling with how to manage rising demand, coupled with diminishing resources, frontline practitioners are being asked to make very important decisions quickly and with limited information. To do this, public sector organisations are turning to new technologies to support decision-making, in particular, predictive analytics tools, which use machine learning algorithms to discover patterns in data and make predictions. While many guides exist around ethical AI design, there is little guidance on how to support a productive human-machine interaction in relation to AI. This report aims to fill this gap by focusing on the issue of human-machine interaction. How people are working with tools is significant because, simply put, for predictive analytics tools to be effective, frontline practitioners need to use them well. It encourages public sector organisations to think about how people feel about predictive analytics tools – what they’re fearful of, what they’re excited about, what they don’t understand. Based on insights drawn from an extensive literature review, interviews with frontline practitioners, and discussions with experts across a range of fields, the guide also identifies three key principles that play a significant role in supporting a constructive human-machine relationship: context, understanding, and agency.