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Abstract
This article enquires into the current prospects for data intermediaries in the context of competition and innovation policies. It asks what the conditions for and means to fulfil these promises are. This requires looking at the evolving legal framework – including the recently enacted Regulation (EU) 2022/686 'Data Governance Act', which affects the incentives of data intermediaries and market actors. In particular, this article explores the obstacles for the establishment of data intermediaries, the context for their activities and the necessary conditions to be set, and complimentary measures to be taken to make them work. The overall goal is to discuss how the findings would translate into viable policy options to advance the regulatory framework that would contribute to an effective market design for data sharing.**

This Position Paper contains statements drafted by several Research Groups at the Weizenbaum Institute concerning the Data Governance Act (DGA) Proposal. Each statement is followed by a short explanation. The purpose of this Paper is to highlight a number of important aspects of the DGA Proposal and stimulate the debate around it with a special emphasis on the part that concerns regulation of data sharing services (Chapter III, DGA Proposal). The Paper touches upon a number of selected matters without the ambition to cover all the important issues the DGA legislation raises. The statements address the potential risks in creating a centralized architecture for data intermediaries, the problem of imposing a duty on data sharing services to offer data on a non-discriminatory basis, the role and expertise supervision authorities will need to assume and exercise and questions regarding the interface between the anticipated DGA and existing data protection law in the EU. The Paper includes a number of specific recommendations regarding the formulation of several DGA provisions, specifically in connection with its intersection points with the GDPR.

Abstract
The EU Data Act proposal (DA) seeks to introduce new rights for the users of internet of things (IoT) devices regarding access, use and sharing of the data generated through their use of these devices. This paper presents the results of a first analysis of the effectiveness of this 'user rights' mechanism from a 'law and economics' perspective. It concludes that the DA will not achieve its objectives of (a) empowering the users of IoT devices (especially the consumers), (b) unlocking large amounts of IoT data for innovation (for IoT-related services and across sectors), and (c) contributing to a fair sharing of the value from the generated IoT data. Although the DA correctly identifies the main problem arising from manufacturers' technical design of IoT devices, which grants them exclusive de facto control over the generated IoT data, its proposals for solving it are not close to being sufficient: (1) The proposed user rights mechanism suffers from manifold serious problems which will make it weak and largely ineffective (insufficient scope of data; lacking technical interoperability; high transaction costs, especially through the need for a negotiated contract with FRAND conditions; unclarity regarding data markets). (2) Also, the option for users to gain more control over the use of the IoT data, which operates through the requirement that the data holders can only use the IoT data on the basis of a contract with the users, will not work due to unsolved serious market failure problems in B2C situations. Therefore, all the rights to use the IoT data will end up with the data holders (and leave the consumers with only these weak user rights). The main reason for this negative assessment of the DA is its overemphasis on the protection of the exclusive de facto control position of the data holders. However, it is very doubtful whether in the case of IoT devices, whose price can cover the costs of data generation, any general incentive problem for investing in data-generating IoT devices exists. Therefore, a far-reaching rebalancing in favor of easier and increased data sharing and user empowerment is necessary, especially for enabling more innovation in the data economy.

To make Artificial Intelligence (AI) systems and services accountable and regulated in the European Union market, in April 2021, the European Union Parliament published a proposal `Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act)', widely known as Artificial Intelligence Act (AI Act). Since then, many concerns have been raised in terms of compliance and whether the regulations are enforceable. However, to the best of our knowledge, none of them provided an explicit technical analysis of the challenges in enforcing the regulation. Among 85 Articles in the AI Act, we emphasize on the Article 10, the central regulatory requirement for data and data governance. In this paper, we have analyzed a specific requirement, the data quantity, to show the challenges of enforcing this requirement in a principled way. In our analysis, we have used deep learning modeling and machine learning generalization theory.