We outline a theory of algorithmic attention rents in digital aggregator platforms. We explore the way that as platforms grow, they become increasingly capable of extracting rents from a variety of actors in their ecosystems—users, suppliers, and advertisers—through their algorithmic control over user attention. We focus our analysis on advertising business models, in which attention harvested from users is monetized by reselling the attention to suppliers or other advertisers, though we believe the theory has relevance to other online business models as well. We argue that regulations should mandate the disclosure of the operating metrics that platforms use to allocate user attention and shape the “free” side of their marketplace, as well as details on how that attention is monetized.

Data has become a critical trans-national and cross-border resource. Yet, the lack of a well-defined approach to using it poses challenges to harnessing its value. This article explores the increasing importance of global data governance due to the rapid growth of data, and the need for responsible data practices. The purpose of this paper is to compare approaches and identify patterns in the emergent data governance ecosystem within sectors close to the international development field, ultimately presenting key takeaways and reflections on when and why a global data governance framework may be needed. Overall, the paper provides information about the conditions when a more holistic, coordinated transnational approach to data governance may be needed to responsibly manage the global flow of data. The report does this by (a) considering conditions specified by the literature that may be conducive to global data governance, and (b) analyzing and comparing existing frameworks, specifically investigating six key elements: purpose, principles, anchoring documents, data description and lifecycle, processes, and practices. The article closes with a series of final recommendations, which include adopting a broader concept of data stewardship to reconcile data protection and promotion, focusing on responsible reuse of data to unlock socioeconomic value, harmonizing meanings to operationalize principles, incorporating global human rights frameworks to provide common North Stars, unifying key definitions of data, adopting a data lifecycle approach, incorporating participatory processes and collective agency, investing in new professions with specific roles, improving accountability through oversight and compliance mechanisms, and translating recommendations into practical tools.

Many international organisations have recently acknowledged the significance of whistleblowing in preventing institutional corruption, particularly in the public sector. Likewise, many countries have enacted whistleblower protection laws, though a robust whistleblower protection system certainly requires much more than legislation. One challenge in developing effective protection systems is finding empirical evidence to evaluate existing systems. Can we measure the effectiveness of whistleblower protection systems accross different countries? What conditions do we need to make the whistleblower protection system work effectively in the public sector? This paper investigates two cases: South Korea and the Republic of Kosovo. South Korean data comes from the Anti-Corruption and Civil Rights Commission of South Korea, while its counterpart from Kosovo comes from a survey of 400 public officials about whistleblower protection. By analysing both datasets, this paper creates a new index that evaluates the effectiveness of whistleblower protection. Composed of quantitative and qualitative sub-indices, the index serves as a digital comparison tool for assessing whistleblower protection systems across different countries and at different times. In addition to enacting high-quality laws, this index identifies several additional measures that can strengthen whistleblower protection systems in the public sector.

The use of artificial intelligence and algorithmic decision-making in public policy processes is influenced by a range of diverse drivers. This article provides a comprehensive view of 13 drivers and their interrelationships, identified through empirical findings from the taxation and social security domains in Belgium. These drivers are organized into five hierarchical layers that policy designers need to focus on when introducing advanced analytics in fraud detection: (a) trust layer, (b) interoperability layer, (c) perceived benefits layer, (d) data governance layer, and (e) digital governance layer. The layered approach enables a holistic view of assessing adoption challenges concerning new digital technologies. The research uses thematic analysis and interpretive structural modeling.

Explainability is highly desired in machine learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. While the field of explainable ML has expanded in recent years, much of this work has not taken real-world needs into account. A majority of proposed methods are designed with generic explainability goals without well-defined use cases or intended end users and evaluated on simplified tasks, benchmark problems/datasets, or with proxy users (e.g., Amazon Mechanical Turk). We argue that these simplified evaluation settings do not capture the nuances and complexities of real-world applications. As a result, the applicability and effectiveness of this large body of theoretical and methodological work in real-world applications are unclear. In this work, we take steps toward addressing this gap for the domain of public policy. First, we identify the primary use cases of explainable ML within public policy problems. For each use case, we define the end users of explanations and the specific goals the explanations have to fulfill. Finally, we map existing work in explainable ML to these use cases, identify gaps in established capabilities, and propose research directions to fill those gaps to have a practical societal impact through ML. The contribution is (a) a methodology for explainable ML researchers to identify use cases and develop methods targeted at them and (b) using that methodology for the domain of public policy and giving an example for the researchers on developing explainable ML methods that result in real-world impact.

Chronic food insecurity remains a challenge globally, exacerbated by climate change-driven shocks such as droughts and floods. Forecasting food insecurity levels and targeting vulnerable households is apriority for humanitarian programming to ensure timely delivery of assistance. In this study, we propose to harness a machine learning approach trained on high-frequency household survey data to infer the predictors of food insecurity and forecast household level outcomes in near real-time. Our empirical analyses leverage the Measurement Indicators for Resilience Analysis (MIRA) data collection protocol implemented by Catholic Relief Services (CRS) in southern Malawi, a series of sentinel sites collecting household data monthly. When focusing on predictors of community-level vulnerability, we show that a random forest model outperforms other algorithms and that location and self-reported welfare are the best predictors of food insecurity. We also show performance results across several neural networks and classical models for various data modeling scenarios to forecast food security. We pose that problem as binary classification via dichotomization of the food security score based on two different thresholds, which results in two different positive class to negative class ratios. Our best performing model has an F1 of 81% and an accuracy of 83% in predicting food security outcomes when the outcome is dichotomized based on threshold 16 and predictor features consist of historical food security score along with 20 variables selected by artificial intelligence explainability frameworks. These results showcase the value of combining high-frequency sentinel site data with machine learning algorithms to predict future food insecurity outcomes.

In this article, we explore the challenges of global governance and the particular challenge presented by global data governance. We discuss a range of challenges to developing meaningful global governance institutions for regulating how companies and governments around the world manage and utilize consumer data. These challenges are compounded by their global nature and the complexities of Internet-based technologies. We argue that the following gaps exist for effective global data governance: (a) there is no overarching global framework for protecting consumer data, and it is partial and incomplete; (b) there is a lack of data protection for international data transfers, as much of the regulation that is being developed is not global in scale; and (c) new areas of data collection and use compound concerns to effective data governance in a globalized digital world. Moreover, we highlight important needs in terms of both global governance and impending challenges related to current and new uses of data. Any global governance framework should recognize the need for an iterative process where communication is ongoing between the necessary stakeholders. Agreements should incorporate common goals to maximize the potential development of global data governance norms. However, goals must remain flexible to the different data environments across nation-states while maintaining a global scope to ensure data protection. In addition, any agreement should consider the emerging challenges in this area. These challenges include new methods of data collection and use, as well as protecting individuals from manipulation and undue influence based on how their data are being used, processed, and collected.

This article explores the role of interoperability in the development of digital public services in Europe, analyzing the effects of an European Union (EU)-level initiative (the European interoperability framework, EIF) and the development of e-Government services on how citizens interact online with public administrations. The EIF is a common EU framework providing guidance on public sector interoperability. EU countries are not mandated to follow the EIF, but they are encouraged to take up its guidance in their respective national interoperability frameworks (NIFs). Against this background, this article tests two hypotheses: (a) the introduction of NIFs facilitates the online interaction between citizens and public administrations and (b) better e-Government services encourage citizens to interact online with public administrations. Both hypotheses are confirmed by a panel data analysis covering 26 European countries over the period 2012–2019. The analysis relies on a dummy variable reflecting the adoption of NIFs, built by carefully examining official documents of the countries in the scope of the analysis. Based on the empirical results, this article puts forward two main policy recommendations. First, efforts to improve e-Government services across Europe should be intensified in order to support the overarching digital agenda of the EU and increase benefits for European citizens. Second, interoperability should become a central element when designing new digital public services. Therefore, the European Commission could foster a common approach to interoperability of digital public services across the EU by strengthening the governance of interoperability initiatives and encouraging the adoption of specific interoperability requirements.

Machine-generated artworks are now part of the contemporary art scene: they are attracting significant investments and they are presented in exhibitions together with those created by human artists. These artworks are mainly based on generative deep learning (GDL) techniques, which have seen a formidable development and remarkable refinement in the very recent years. Given the inherent characteristics of these techniques, a series of novel legal problems arise. In this article, we consider a set of key questions in the area of GDL for the arts, including the following: is it possible to use copyrighted works as training set for generative models? How do we legally store their copies in order to perform the training process? Who (if someone) will own the copyright on the generated data? We try to answer these questions considering the law in force in both the United States and the European Union, and potential future alternatives. We then extend our analysis to code generation, which is an emerging area of GDL. Finally, we also formulate a set of practical guidelines for artists and developers working on deep learning generated art, as well as some policy suggestions for policymakers.

This commentary looks at the use of corporate social responsibility (CSR) mechanisms for implementing responsible data use. The commentary offers an overview of CSR theory and the discourse on a growing phenomenon known as corporate digital responsibility (CDR). The commentary links these theories to the historical debates on the nature of technology, ethics, and society. The aim is to reflect on CSR and CDR mechanisms and ignite the discussion on their adequacy considering the pursuit of data responsibility. Through our discussion and brief case studies, the paper reveals the gaps in relying on CSR and CDR and the need for a broader societal and comprehensive approach.

A number of governmental and nongovernmental organizations have made significant efforts to encourage the development of artificial intelligence in line with a series of aspirational concepts such as transparency, interpretability, explainability, and accountability. The difficulty at present, however, is that these concepts exist at a fairly abstract level, whereas in order for them to have the tangible effects desired they need to become more concrete and specific. This article undertakes precisely this process of concretisation, mapping how the different concepts interrelate and what in particular they each require in order to move from being high-level aspirations to detailed and enforceable requirements. We argue that the key concept in this process is accountability, since unless an entity can be held accountable for compliance with the other concepts, and indeed more generally, those concepts cannot do the work required of them. There is a variety of taxonomies of accountability in the literature. However, at the core of each account appears to be a sense of “answerability”; a need to explain or to give an account. It is this ability to call an entity to account which provides the impetus for each of the other concepts and helps us to understand what they must each require.

Artificial intelligence (AI) systems are playing an overarching role in the disinformation phenomenon our world is currently facing. Such systems boost the problem not only by increasing opportunities to create realistic AI-generated fake content, but also, and essentially, by facilitating the dissemination of disinformation to a targeted audience and at scale by malicious stakeholders. This situation entails multiple ethical and human rights concerns, in particular regarding human dignity, autonomy, democracy, and peace. In reaction, other AI systems are developed to detect and moderate disinformation online. Such systems do not escape from ethical and human rights concerns either, especially regarding freedom of expression and information. Having originally started with ascending co-regulation, the European Union (EU) is now heading toward descending co-regulation of the phenomenon. In particular, the Digital Services Act proposal provides for transparency obligations and external audit for very large online platforms’ recommender systems and content moderation. While with this proposal, the Commission focusses on the regulation of content considered as problematic, the EU Parliament and the EU Council call for enhancing access to trustworthy content. In light of our study, we stress that the disinformation problem is mainly caused by the business model of the web that is based on advertising revenues, and that adapting this model would reduce the problem considerably. We also observe that while AI systems are inappropriate to moderate disinformation content online, and even to detect such content, they may be more appropriate to counter the manipulation of the digital ecosystem.

The COVID-19 pandemic has exposed the need for more contactless interactions, leading to an acceleration in the design, development, and deployment of digital identity tools and contact-free solutions. A potentially positive outcome of the current crisis could be the development of a more data privacy and human rights compliant framework for digital identity. However, for such a framework to thrive, two essential conditions must be met: (1) respect for and protection of data privacy irrespective of the type of architecture or technology chosen and (2) consideration of the broader impacts that digital identity can have on individuals’ human rights. The article draws on legal, technology-facing, and policy-oriented academic literature to evaluate each of these conditions. It then proposes two ways to leverage the process of digitalization strengthened by the pandemic: a data privacy-centric and a human rights-based approach to digital identity solutions fit for post-COVID-19 societies.

A lack of political legitimacy undermines the ability of the European Union (EU) to resolve major crises and threatens the stability of the system as a whole. By integrating digital data into political processes, the EU seeks to base decision-making increasingly on sound empirical evidence. In particular, artificial intelligence (AI) systems have the potential to increase political legitimacy by identifying pressing societal issues, forecasting potential policy outcomes, and evaluating policy effectiveness. This paper investigates how citizens’ perceptions of EU input, throughput, and output legitimacy are influenced by three distinct decision-making arrangements: (a) independent human decision-making by EU politicians; (b) independent algorithmic decision-making (ADM) by AI-based systems; and (c) hybrid decision-making (HyDM) by EU politicians and AI-based systems together. The results of a preregistered online experiment (n = 572) suggest that existing EU decision-making arrangements are still perceived as the most participatory and accessible for citizens (input legitimacy). However, regarding the decision-making process itself (throughput legitimacy) and its policy outcomes (output legitimacy), no difference was observed between the status quo and HyDM. Respondents tend to perceive ADM systems as the sole decision-maker to be illegitimate. The paper discusses the implications of these findings for (a) EU legitimacy and (b) data-driven policy-making and outlines (c) avenues for future research.

U.S., UK, and European municipalities are increasingly experimenting with data as an instrument for social policy. This movement pertains often to the design of municipal data warehouses, dashboards, and predictive analytics, the latter mostly to identify risk of fraud. This transition to data-driven social policy, captured by the term “digital welfare state,” almost completely takes place out of political and social view, and escapes democratic decision making. In this article, I zoom in on The Netherlands and show in detail how sound data governance is lacking at three levels: data experiments and practices take place in a so-called “institutional void” without any clear democratic mandate; moreover, they are often based on disputable quality of data and analytic models; and they tend to transgress the recent EU General Data Protection Regulation (GDPR) about privacy and data protection. I also assess that key stakeholders in this data transition, that is the citizens whose data are used, are not actively informed let alone invited to participate. As a result, a practice of top-down monitoring, containment and control is evolving despite the desire of civil servants in this domain to do “good” with data. I explore several data and policy alternatives in the conclusion to contribute to a higher quality and more democratic usage of data in the digital welfare state.