Automated decision-making in the EU Member States: The right to explanation and other “suitable safeguards” in the national legislations

Malgieri, Gianclaudio

doi:10.1016/j.clsr.2019.05.002

Cited by 77 publications

(50 citation statements)

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“…We discuss requirements in more detail in Section IV-A. This type of requirements in automatic decision-making are already recognized in the General Data Protection Regulation (GDPR) and also considered to some extent in some EU Member States' laws [79] though the emphasis is generally on services using data instead of intelligent systems performing actions in the environment.…”

Section: A Intelligencementioning

confidence: 99%

Research and Education Towards Smart and Sustainable World

Riekki

Mämmelä

2021

IEEE Access

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We propose a vision for directing research and education in the field of information and communications technology (ICT). Our Smart and Sustainable World vision targets prosperity for the people and the planet through better awareness and control of both human-made and natural environments. The needs of society, individuals, and industries are fulfilled with intelligent systems that sense their environment, make proactive decisions on actions advancing their goals, and perform the actions on the environment. We emphasize artificial intelligence, feedback loops, human acceptance and control, intelligent use of basic resources, performance parameters, mission-oriented interdisciplinary research, and a holistic systems view complementing the conventional analytical reductive view as a research paradigm, especially for complex problems. To serve a broad audience, we explain these concepts and list the essential literature. We suggest planning research and education by specifying, in a step-wise manner, scenarios, performance criteria, system models, research problems, and education content, resulting in common goals and a coherent project portfolio as well as education curricula. Research and education produce feedback to support evolutionary development and encourage creativity in research. Finally, we propose concrete actions for realizing this approach.

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Section: A Intelligencementioning

confidence: 99%

Research and Education Towards Smart and Sustainable World

Riekki

Mämmelä

2021

IEEE Access

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“…Hence, it seems more probable that a set of different legal safeguards which can be applied in particular EU countries will be developed in order to ensure that the dynamic development of technology-including the spread of ADM-does not adversely affect the area of fundamental rights. This trend is already being observed today (Malgieri, 2019), and the problem of implementing the right to explanation of decisions taken automatically is one of the main areas of legislative activity.…”

Section: Discussionmentioning

confidence: 88%

Black box algorithms and the rights of individuals: no easy solution to the “explainability” problem

Gryz¹,

Rojszczak²

2021

Internet Policy Review

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Over the last few years, the interpretability of classification models has been a very active area of research. Recently, the concept of interpretability was given a more specific legal context. In 2016, the EU adopted the General Data Protection Regulation (GDPR), containing the right to explanation for people subjected to automated decision-making (ADM). The regulation itself is very reticent about what such a right might imply. As a result, since the introduction of the GDPR there has been an ongoing discussion about not only the need to introduce such a right, but also about its scope and practical consequences in the digital world. While there is no doubt that the right to explanation may be very difficult to implement due to technical challenges, any difficulty in explaining how algorithms work cannot be considered a sufficient reason to completely abandon this legal safeguard. The aim of this article is twofold. First, to demonstrate that the interpretability of "black box" machine learning algorithms is a challenging technical problem for which no solutions have been found. Second, to demonstrate how the explanation task should instead be completed using well-known and well-trialled IT solutions, such as event logging or statistical analysis of the algorithm. Based on the evidence exposed in this paper, the authors find that the most effective solution would be to benchmark the automated decision-making algorithms using certification frameworks, thus balancing the need to ensure adequate protection of individuals' rights with the understandable expectations of AI technology providers to have their intellectual property rights protected.

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“…The EU General Data Protection Regulation has sought to address some of the risks of automated decision-making. However, the national implementation of Article 22 GDPR on the right to explanation, that is, the right to receive specific information and the right to get an explanation of the decision reached after such assessment and to challenge it, has resulted in the emergence of different legal solutions for the need for transparency in automated decision-making (Malgieri & Commande 2017;Malgieri 2019). Many unsolved questions remain and existing legislative frameworks and instruments (e.g., algorithmic impact assessments) (Kaminski & Malgieri 2020) only provide partial answers to the need for enhanced transparency and accountability (Wachter, Mittelstadt & Floridi 2017).…”

Section: Regulating Ai: Key Concernsmentioning

confidence: 99%

Experimental Regulations for AI: Sandboxes for Morals and Mores

Ranchordás¹

2021

Morals & Machines

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Recent EU legislative and policy initiatives aim to offer flexible, innovation-friendly, and future-proof regulatory frameworks. Key examples are the EU Coordinated Plan on AI and the recently published EU AI Regulation Proposal which refer to the importance of experimenting with regulatory sandboxes so as to balance innovation in AI against its potential risks. Originally developed in the Fintech sector, regulatory sandboxes create a test bed for a selected number of innovative projects, by waiving otherwise applicable rules, guiding compliance, or customizing enforcement. Despite the burgeoning literature on regulatory sandboxes and the regulation of AI, the legal, methodological, and ethical challenges of these anticipatory or, at times, adaptive regulatory frameworks have remained understudied. This exploratory article delves into the some of the benefits and intricacies of allowing for experimental instruments in the context of the regulation of AI. This article’s contribution is twofold: first, it contextualizes the adoption of regulatory sandboxes in the broader discussion on experimental approaches to regulation; second, it offers a reflection on the steps ahead for the design and implementation of AI regulatory sandboxes.

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Automated decision-making in the EU Member States: The right to explanation and other “suitable safeguards” in the national legislations

Cited by 77 publications

References 0 publications

Research and Education Towards Smart and Sustainable World

Research and Education Towards Smart and Sustainable World

Black box algorithms and the rights of individuals: no easy solution to the “explainability” problem

Experimental Regulations for AI: Sandboxes for Morals and Mores

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