Publications

At every level of government, officials contract for technical systems that employ machine learning—systems that perform tasks without using explicit instructions, relying on patterns and inference instead. These systems frequently displace discretion previously exercised by policymakers or individual front-end government employees with an opaque logic that bears no resemblance to the reasoning processes of agency personnel. However, because agencies acquire these systems through government procurement processes, they and the public have little input into—or even knowledge about—their design or how well that design aligns with public goals and values. This Article explains the ways that the decisions about goals, values, risk, and certainty, along with the elimination of case-by-case discretion, inherent in machine-learning system design create policies—not just once when they are designed, but over time as they adapt and change. When the adoption of these systems is governed by procurement, the policies they embed receive little or no agency or outside expertise beyond that provided by the vendor. Design decisions are left to private third-party developers. There is no public participation, no reasoned deliberation, and no factual record, which abdicates Government responsibility for policymaking. This Article then argues for a move from a procurement mindset to policymaking mindset. When policy decisions are made through system design, processes suitable for substantive administrative determinations should be used: processes that foster deliberation reflecting both technocratic demands for reason and rationality informed by expertise, and democratic demands for public participation and political accountability. Specifically, the Article proposes administrative law as the framework to guide the adoption of machine learning governance, describing specific ways that the policy choices embedded in machine-learning system design fail the prohibition against arbitrary and capricious agency actions absent a reasoned decision-making process that both enlists the expertise necessary for reasoned deliberation about, and justification for, such choices, and makes visible the political choices being made. Finally, this Article sketches models for machine-learning adoption processes that satisfy the prohibition against arbitrary and capricious agency actions. It explores processes by which agencies might garner technical expertise and overcome problems of system opacity, satisfying administrative law’s technocratic demand for reasoned expert deliberation. It further proposes both institutional and engineering design solutions to the challenge of policymaking opacity, offering process paradigms to ensure the “political visibility” required for public input and political oversight. In doing so, it also proposes the importance of using “contestable design”—design that exposes value-laden features and parameters and provides for iterative human involvement in system evolution and deployment. Together, these institutional and design approaches further both administrative law’s technocratic and democratic mandates.

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.

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.

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.

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.