What you don’t know can’t hurt you: this seems to be the current approach for responding to disinformation by public regulators across the world. Nobody is able to say with any degree of certainty what is actually going on. This is in no small part because, at present, public regulators don’t have the slightest idea how disinformation actually works in practice. We believe that there are very good reasons for the current state of affairs, which stem from a lack of verifiable data available to public institutions. If an election board or a media regulator wants to know what types of digital content are being shared in their jurisdiction, they have no effective mechanisms for finding this data or ensuring its veracity. While there are many other reasons why governments would want access to this kind of data, the phenomenon of disinformation provides a particularly salient example of the consequences of a lack of access to this data for ensuring free and fair elections and informed democratic participation. This chapter will provide an overview of the main aspects of the problems associated with basing public regulatory decisions on unverified data, before sketching out some ideas of what a solution might look like. In order to do this, the chapter develops the concept of auditing intermediaries. After discussing which problems the concept of auditing intermediaries is designed to solve, it then discusses some of the main challenges associated with access to data, potential misuse of intermediaries, and the general lack of standards for the provision of data by large online platforms. In conclusion, the chapter suggests that there is an urgent need for an auditing mechanism to ensure the accuracy of transparency data provided by large online platform providers about the content on their services. Transparency data that have been audited would be considered verified data in this context. Without such a transparency verification mechanism, existing public debate is based merely on a whim, and digital dominance is likely to only become more pronounced.
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De landbouwsector wordt steeds digitaler. Sensoren, machines en software verzamelen dagelijks waardevolle gegevens over gewassen, bodem en bedrijfsvoering. Maar wie heeft toegang tot deze data? En hoe behoudt de agrarisch ondernemer zelf de controle?Nieuwe Europese regels geven meer rechten en beschermenu tegen misbruik.In deze brief, opgesteld namens het DigiAgro-project in samenwerking met de Hanzehogeschool Groningen en het Instituut voor Rechtenstudies, leest u hoe deze wetgeving de positie van de agrarisch ondernemer versterkt, wat hun rechten en plichten zijn. En hoe een agrarisch ondernemer gebruik kan maken van de kansen die de regels bieden.Disclaimer:Deze opdracht is uitgevoerd door studenten in het kader van hun opleiding bij het Instituut voor Rechtenstudies. De studenten leveren een juridisch beroepsproduct op en doen daartoe onderzoek. De studenten wordt tijdens de uitvoering van de opdracht begeleid door een coach. De inspanningen van de studenten en de coach zijn erop gericht om een zo goed mogelijk beroepsproduct op te leveren. Dit moet opgevat worden als een product van (derdejaars)studenten en niet van een juridische professional. Mocht ondanks de geleverde inspanningen de informatie of de inhoud van het beroepsproduct onvolledig en/of onjuist zijn, dan kunnen de Hanzehogeschool Groningen, het Instituut voor Rechtenstudies, individuele medewerkers en de studenten daarvoor geen aansprakelijkheid aanvaarden.
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The AI Act, effective from August 1, 2024, introduces EU-wide standards to ensure the ethical and safe use of artificial intelligence (AI). Dutch municipalities must be fully compliant by 2026, navigating complex legal and operational challenges. This study assesses their readiness through survey data and interviews with municipal representatives. Although awareness of the Act is high, significant gaps remain between regulatory knowledge and implementation. Municipalities face issues such as limited legal capacity, uneven digital transformation, and ethical uncertainty—resulting in fragmented AI governance. While compliance frameworks are emerging, most approaches remain reactive. This study identifies key barriers and recommends measures to strengthen AI literacy, clarify regulations, and improve ethical oversight. A coordinated national strategy is essential to align local governance with policy goals. Drawing on theories of symbolic versus substantive compliance, policy-implementation gaps, and regulatory adaptation, the study contextualises the findings and calls for further research on best practices, intergovernmental collaboration, and long-term governance strategies.
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In this paper, we explore the design of web-based advice robots to enhance users' confidence in acting upon the provided advice. Drawing from research on algorithm acceptance and explainable AI, we hypothesise four design principles that may encourage interactivity and exploration, thus fostering users' confidence to act. Through a value-oriented prototype experiment and value-oriented semi-structured interviews, we tested these principles, confirming three of them and identifying an additional principle. The four resulting principles: (1) put context questions and resulting advice on one page and allow live, iterative exploration, (2) use action or change oriented questions to adjust the input parameters, (3) actively offer alternative scenarios based on counterfactuals, and (4) show all options instead of only the recommended one(s), appear to contribute to the values of agency and trust. Our study integrates the Design Science Research approach with a Value Sensitive Design approach.
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Recent years have seen a massive growth in ethical and legal frameworks to govern data science practices. Yet one of the core questions associated with ethical and legal frameworks is the extent to which they are implemented in practice. A particularly interesting case in this context comes to public officials, for whom higher standards typically exist. We are thus trying to understand how ethical and legal frameworks influence the everyday practices on data and algorithms of public sector data professionals. The following paper looks at two cases: public sector data professionals (1) at municipalities in the Netherlands and (2) at the Netherlands Police. We compare these two cases based on an analytical research framework we develop in this article to help understanding of everyday professional practices. We conclude that there is a wide gap between legal and ethical governance rules and the everyday practices.
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Over the past few years, there has been an explosion of data science as a profession and an academic field. The increasing impact and societal relevance of data science is accompanied by important questions that reflect this development: how can data science become more responsible and accountable while also responding to key challenges such as bias, fairness, and transparency in a rigorous and systematic manner? This Patterns special collection has brought together research and perspective from academia, the public and the private sector, showcasing original research articles and perspectives pertaining to responsible and accountable data science.
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Abstract Despite the numerous business benefits of data science, the number of data science models in production is limited. Data science model deployment presents many challenges and many organisations have little model deployment knowledge. This research studied five model deployments in a Dutch government organisation. The study revealed that as a result of model deployment a data science subprocess is added into the target business process, the model itself can be adapted, model maintenance is incorporated in the model development process and a feedback loop is established between the target business process and the model development process. These model deployment effects and the related deployment challenges are different in strategic and operational target business processes. Based on these findings, guidelines are formulated which can form a basis for future principles how to successfully deploy data science models. Organisations can use these guidelines as suggestions to solve their own model deployment challenges.
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During the past two decades the implementation and adoption of information technology has rapidly increased. As a consequence the way businesses operate has changed dramatically. For example, the amount of data has grown exponentially. Companies are looking for ways to use this data to add value to their business. This has implications for the manner in which (financial) governance needs to be organized. The main purpose of this study is to obtain insight in the changing role of controllers in order to add value to the business by means of data analytics. To answer the research question a literature study was performed to establish a theoretical foundation concerning data analytics and its potential use. Second, nineteen interviews were conducted with controllers, data scientists and academics in the financial domain. Thirdly, a focus group with experts was organized in which additional data were gathered. Based on the literature study and the participants responses it is clear that the challenge of the data explosion consist of converting data into information, knowledge and meaningful insights to support decision-making processes. Performing data analyses enables the controller to support rational decision making to complement the intuitive decision making by (senior) management. In this way, the controller has the opportunity to be in the lead of the information provision within an organization. However, controllers need to have more advanced data science and statistic competences to be able to provide management with effective analysis. Specifically, we found that an important skill regarding statistics is the visualization and communication of statistical analysis. This is needed for controllers in order to grow in their role as business partner..
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In May 2018, the new Dutch Intelligence and Security Services Act 2017 (Wet op de Inlichtingen- en veiligheidsdiensten, Wiv) will enter into force. It replaces the previous 2002 Act and incorporates many reforms to the information gathering powers of the two intelligence and security services as well as to the accountability and oversight mechanisms. Due to the technologyneutral approach, both the civil and the military intelligence services are now authorized to, for example, intercept communications in bulk, hack third parties, decrypt files, store DNA or use any other future innovative technology. Also, the national security legislation extends the possibilities for the indiscriminate collection of data, and for the processing, storage and analysis thereof. The process leading to the law includes substantial criticism from the various stakeholders involved. Upon publication of this report, an official consultative referendum is being organized on the new act. The aim of this policy brief is to provide an international audience with a comprehensive overview of the most relevant aspects of the act and its context. In addition, there is considerable focus on the checks and balances as well as the bottlenecks of the Dutch intelligence gathering reform. The selection of topics is based on the core issues addressed during the parliamentary debate and on the authors’ insights.
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Full tekst beschikbaar voor gebruikers van Linkedin. Driven by technological innovations such as cloud and mobile computing, big data, artificial intelligence, sensors, intelligent manufacturing, robots and drones, the foundations of organizations and sectors are changing rapidly. Many organizations do not yet have the skills needed to generate insights from data and to use data effectively. The success of analytics in an organization is not only determined by data scientists, but by cross-functional teams consisting of data engineers, data architects, data visualization experts, and ("perhaps most important"), Analytics Translators.
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