The European Legislation on AI: a Brief Analysis of its Philosophical Approach

The Cooperation Between Nurses and a New Digital Colleague "AI-Driven Lifestyle Monitoring" in Long-Term Care for Older Adults: Viewpoint

Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult's home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague.

Justice at the Forefront: Cultivating felt accountability towards Artificial Intelligence among healthcare professionals

The advent of AI has ushered in a new era of patient care, but with it emerges a contentious debate surrounding accountability for algorithmic medical decisions. Within this discourse, a spectrum of views prevails, ranging from placing accountability on AI solution providers to laying it squarely on the shoulders of healthcare professionals. In response to this debate, this study, grounded in the mutualistic partner choice (MPC) model of the evolution of morality, seeks to establish a configurational framework for cultivating felt accountability towards AI among healthcare professionals. This framework underscores two pivotal conditions: AI ethics enactment and trusting belief in AI and considers the influence of organizational complexity in the implementation of this framework. Drawing on Fuzzy-set Qualitative Comparative Analysis (fsQCA) of a sample of 401 healthcare professionals, this study reveals that a) focusing justice and autonomy in AI ethics enactment along with building trusting belief in AI reliability and functionality reinforces healthcare professionals' sense of felt accountability towards AI, b) fostering felt accountability towards AI necessitates ensuring the establishment of trust in its functionality for high complexity hospitals, and c) prioritizing justice in AI ethics enactment and trust in AI reliability is essential for low complexity hospitals.

Long short-term memory models of water quality in inland water environments

Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse and seasonal changes. Deep learning models have demonstrated predictive power of water quality due to the superior ability to automatically learn complex patterns and relationships from variables. Long short-term memory (LSTM), one of deep learning models for water quality prediction, is a type of recurrent neural network that can account for longer-term traits of time-dependent data. It is the most widely applied network used to predict the time series of water quality variables. First, we reviewed applications of a standalone LSTM and discussed its calculation time, prediction accuracy, and good robustness with process-driven numerical models and the other machine learning. This review was expanded into the LSTM model with data pre-processing techniques, including the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method and Synchrosqueezed Wavelet Transform. The review then focused on the coupling of LSTM with a convolutional neural network, attention network, and transfer learning. The coupled networks demonstrated their performance over the standalone LSTM model. We also emphasized the influence of the static variables in the model and used the transformation method on the dataset. Outlook and further challenges were addressed. The outlook for research and application of LSTM in hydrology concludes the review.

The AI Act Grand Challenge shows how autonomous robots will be regulated

One of the winning teams of the EU AI Act Grand Challenge analyzes how the AI Act will regulate robots.

Considerations on the regulation of AI systems in the financial sector by the AI Act

The proposal for the Artificial Intelligence regulation in the EU (AI Act) is a horizontal legal instrument that aims to regulate, according to a tailored risk-based approach, the development and use of AI systems across a plurality of sectors, including the financial sector. In particular, AI systems intended to be used to evaluate the creditworthiness or establish the credit score of natural persons are classified as "high-risk AI systems". The proposal, tabled by the Commission in April 2021, is currently at the center of intense interinstitutional negotiations between the two branches of the European legislature, the European Parliament and the Council. Without prejudice to the ongoing legislative deliberations, the paper aims to provide an overview of the main elements and choices made by the Commission in respect of the regulation of AI in the financial sector, as well as of the position taken in that regard by the European Parliament and Council.

The regulation of artificial intelligence

Before embarking on a discussion of the regulation of artificial intelligence (AI), it is first necessary to define the subject matter regulated. Defining artificial intelligence is a difficult endeavour, and many definitions have been proposed over the years. Although more than 70 years have passed since it was adopted, the most convincing definition is still nonetheless that proposed by Turing; in any case, it is important to be mindful of the risk of anthropomorphising artificial intelligence, which may arise in particular from its very definition. Once we have established the subject matter regulated, we must ask ourselves whether lawmakers should pursue an approach that seeks to regulate artificial intelligence as a whole, or whether by contrast they should regulate applications of artificial intelligence in specific sectors or individual areas. The proposal for a regulation on artificial intelligence published on 21 April 2021 implements the former approach whilst also pursuing geopolitical goals. After providing an initial overview of the notion of artificial intelligence, this article investigates the geopolitical context to the proposal for a regulation, and then goes on to illustrate the regulatory model embraced by the proposal as well as related critical aspects.

Approval and Certification of Ophthalmic AI Devices in the European Union

Artificial intelligence (AI)-based medical devices are already commercially available in Europe. The regulations surrounding the introduction and use of medical AI devices in the European Union (EU) are different to those in the USA, and the specifics of European legislature in medical AI are not commonly known. European law classifies medical devices into four classes: I, IIa, IIb, and III, depending on the perceived risk level of the device. Medical devices are certified under independent nongovernment bodies, and some can even self-certify their compliance with EU standards. The European "open" approach is vastly different from the strict perspective of the FDA, as reflected by the number of available medical AI devices. The EU is currently in a transitory period between two regulations, further complicating the legislative landscape. The devices in question deal with extremely sensitive data, collecting, processing, and sending images and diagnoses over the internet. The EU approach puts a large burden of verifying the effectiveness and integrity of the AI device on the consumer, without giving consumers many tools to do that effectively. This highlights the need for effective legislation and oversight from governing bodies, as well as the need for understanding the legalities and limitations of AI devices for those implementing them in clinical practice.

From EU Robotics and AI Governance to HRI Research: Implementing the Ethics Narrative

In recent years, the European Union has made considerable efforts to develop dedicated strategies and policies for the governance of robotics and AI. An important component of the EU’s approach is its emphasis on the need to mitigate the potential societal impacts of the expected rise in the interactive capacities of autonomous systems. In the quest to define and implement new policies addressing this issue, ethical notions have taken an increasingly central position. This paper presents a concise overview of the integration of this ethics narrative in the EU’s policy plans. It demonstrates how the ethics narrative aids the definition of policy issues and the establishment of new policy ideas. Crucially, in this context, robotics and AI are explicitly understood as emerging technologies. This implies many ambiguities about their actual future impact, which in turn results in uncertainty regarding effective implementation of policies that draw on the ethics narrative. In an effort to develop clearer pathways towards the further development of ethical notions in AI and robotics governance, this paper understands human-robot interaction (HRI) research as a field that can play an important role in the implementation of ethics. Four different complementary pathways towards ethics integration in (HRI) research are proposed, namely: providing insights for the improvement of ethical assessment, further research into the moral competence of artificial agents, engage in value-based design and implementation of robots, and participation in discussions on building ethical sociotechnical systems around robots.

Governing AI during a pandemic crisis: Initiatives at the EU level

After the outbreak of Covid-19, the European Commission (EC) promptly took the initiative to lead and coordinate a common European response. The actions unfolded in several directions, paving the way to the uptake of AI-related solutions and placing hope in these tools to face crises, namely of a public health and global nature. In this article, we focus on initiatives for the uptake of AI-related solutions from the experimental level towards implementation. The Repository of AI and Robotics solutions, launched in 2020, is an example of an initiative put forth to leverage and disseminate knowledge on AI, expanding the fields of application and fostering the development and adaptation of cutting-edge technologies to explore how they can assist in tackling specific tasks during a public health crisis. Using this database, the article outlines the promise of AI as a hope for handling specific needs and tasks and how the uptake of such technologies was accelerated during the Covid-19 pandemic. In extension, we frame initiatives for the uptake of AI-enabled solutions from a governance perspective, focusing on the establishment of frameworks for ethical and trustworthy AI by defining principles and standards that aim to protect the underlying values deemed fundamental.

Leading the Charge on Digital Regulation: The More, the Better, or Policy Bubble?

For about a decade, the concept of 'digital sovereignty' has been prominent in the European policy discourse. In the quest for digital sovereignty, the European Union has adopted a constitutional approach to protect fundamental rights and democratic values, and to ensure fair and competitive digital markets. Thus, 'digital constitutionalism' emerged as a twin discourse. A corollary of these discourses is a third phenomenon resulting from a regulatory externalisation of European law beyond the bloc's borders, the so-called 'Brussels Effect'. The dynamics arising from Europe's digital policy and regulatory activism imply increasing legal complexities. This paper argues that this phenomenon in policy-making is a case of a positive 'policy bubble' characterised by an oversupply of policies and legislative acts. The phenomenon can be explained by the amplification of values in the framing of digital policy issues. To unpack the policy frames and values at stake, this paper provides an overview of the digital policy landscape, followed by a critical assessment to showcase the practical implications of positive policy bubbles.

SAT: a methodology to assess the social acceptance of innovative AI-based technologies

Purpose

The purpose of this paper is to present the conceptual model of an innovative methodology (SAT) to assess the social acceptance of technology, especially focusing on artificial intelligence (AI)-based technology.

Design/methodology/approach

After a review of the literature, this paper presents the main lines by which SAT stands out from current methods, namely, a four-bubble approach and a mix of qualitative and quantitative techniques that offer assessments that look at technology as a socio-technical system. Each bubble determines the social variability of a cluster of values: User-Experience Acceptance, Social Disruptiveness, Value Impact and Trust.

Findings

The methodology is still in development, requiring further developments, specifications and validation. Accordingly, the findings of this paper refer to the realm of the research discussion, that is, highlighting the importance of preventively assessing and forecasting the acceptance of technology and building the best design strategies to boost sustainable and ethical technology adoption.

Social implications

Once SAT method will be validated, it could constitute a useful tool, with societal implications, for helping users, markets and institutions to appraise and determine the co-implications of technology and socio-cultural contexts.

Originality/value

New AI applications flood today’s users and markets, often without a clear understanding of risks and impacts. In the European context, regulations (EU AI Act) and rules (EU Ethics Guidelines for Trustworthy) try to fill this normative gap. The SAT method seeks to integrate the risk-based assessment of AI with an assessment of the perceptive-psychological and socio-behavioural aspects of its social acceptability.

The environmental challenges of AI in EU law: lessons learned from the Artificial Intelligence Act (AIA) with its drawbacks

Purpose

The paper aims to examine the environmental challenges of artificial intelligence (AI) in EU law that regard both illicit uses of the technology, i.e. overuse or misuse of AI and its possible underuses. The aim of the paper is to show how such regulatory efforts of legislators should be understood as a critical component of the Green Deal of the EU institutions, that is, to save our planet from impoverishment, plunder and destruction.

Design/methodology/approach

To illustrate the different ways in which AI can represent a game-changer for our environmental challenges, attention is drawn to a multidisciplinary approach, which includes the analysis of the initiatives on the European Green Deal; the proposals for a new legal framework on data governance and AI; principles of environmental and constitutional law; the interaction of such principles and provisions of environmental and constitutional law with AI regulations; other sources of EU law and of its Member States.

Findings

Most recent initiatives on AI, including the AI Act (AIA) of the European Commission, have insisted on a human-centric approach, whereas it seems obvious that the challenges of environmental law, including those triggered by AI, should be addressed in accordance with an ontocentric, rather than anthropocentric stance. The paper provides four recommendations for the legal consequences of this short-sighted view, including the lack of environmental concerns in the AIA.

Research limitations/implications

The environmental challenges of AI suggest complementing current regulatory efforts of EU lawmakers with a new generation of eco-impact assessments; duties of care and disclosure of non-financial information; clearer parameters for the implementation of the integration principle in EU constitutional law; special policies for the risk of underusing AI for environmental purposes. Further research should examine these policies in connection with the principle of sustainability and the EU plan for a circular economy, as another crucial ingredient of the Green Deal.

Practical implications

The paper provides a set of concrete measures to properly tackle both illicit uses of AI and the risk of its possible underuse for environmental purposes. Such measures do not only concern the “top down” efforts of legislators but also litigation and the role of courts. Current trends of climate change litigation and the transplant of class actions into several civil law jurisdictions shed new light on the ways in which we should address the environmental challenges of AI, even before a court.

Social implications

A more robust protection of people’s right to a high level of environmental protection and the improvement of the quality of the environment follows as a result of the analysis on the legal threats and opportunities brought forth by AI.

Originality/value

The paper explores a set of issues, often overlooked by scholars and institutions, that is nonetheless crucial for any Green Deal, such as the distinction between the human-centric approach of current proposals in the field of technological regulation and the traditional ontocentric stance of environmental law. The analysis considers for the first time the legal issues that follow this distinction in the field of AI regulation and how we should address them.

When Doctors and AI Interact: on Human Responsibility for Artificial Risks

A discussion concerning whether to conceive Artificial Intelligence (AI) systems as responsible moral entities, also known as “artificial moral agents” (AMAs), has been going on for some time. In this regard, we argue that the notion of “moral agency” is to be attributed only to humans based on their autonomy and sentience, which AI systems lack. We analyze human responsibility in the presence of AI systems in terms of meaningful control and due diligence and argue against fully automated systems in medicine. With this perspective in mind, we focus on the use of AI-based diagnostic systems and shed light on the complex networks of persons, organizations and artifacts that come to be when AI systems are designed, developed, and used in medicine. We then discuss relational criteria of judgment in support of the attribution of responsibility to humans when adverse events are caused or induced by errors in AI systems.

The Good, the Bad, and the Invisible with Its Opportunity Costs: Introduction to the ‘J’ Special Issue on “the Impact of Artificial Intelligence on Law”

Scholars and institutions have been increasingly debating the moral and legal challenges of AI, together with the models of governance that should strike the balance between the opportunities and threats brought forth by AI, its ‘good’ and ‘bad’ facets. There are more than a hundred declarations on the ethics of AI and recent proposals for AI regulation, such as the European Commission’s AI Act, have further multiplied the debate. Still, a normative challenge of AI is mostly overlooked, and regards the underuse, rather than the misuse or overuse, of AI from a legal viewpoint. From health care to environmental protection, from agriculture to transportation, there are many instances of how the whole set of benefits and promises of AI can be missed or exploited far below its full potential, and for the wrong reasons: business disincentives and greed among data keepers, bureaucracy and professional reluctance, or public distrust in the era of no-vax conspiracies theories. The opportunity costs that follow this technological underuse is almost terra incognita due to the ‘invisibility’ of the phenomenon, which includes the ‘shadow prices’ of economy. This introduction provides metrics for such assessment and relates this work to the development of new standards for the field. We must quantify how much it costs not to use AI systems for the wrong reasons.

Embedding artificial intelligence in society: looking beyond the EU AI master plan using the culture cycle

The European Union (EU) Commission’s whitepaper on Artificial Intelligence (AI) proposes shaping the emerging AI market so that it better reflects common European values. It is a master plan that builds upon the EU AI High-Level Expert Group guidelines. This article reviews the masterplan, from a culture cycle perspective, to reflect on its potential clashes with current societal, technical, and methodological constraints. We identify two main obstacles in the implementation of this plan: (i) the lack of a coherent EU vision to drive future decision-making processes at state and local levels and (ii) the lack of methods to support a sustainable diffusion of AI in our society. The lack of a coherent vision stems from not considering societal differences across the EU member states. We suggest that these differences may lead to a fractured market and an AI crisis in which different members of the EU will adopt nation-centric strategies to exploit AI, thus preventing the development of a frictionless market as envisaged by the EU. Moreover, the Commission aims at changing the AI development culture proposing a human-centred and safety-first perspective that is not supported by methodological advancements, thus taking the risks of unforeseen social and societal impacts of AI. We discuss potential societal, technical, and methodological gaps that should be filled to avoid the risks of developing AI systems at the expense of society. Our analysis results in the recommendation that the EU regulators and policymakers consider how to complement the EC programme with rules and compensatory mechanisms to avoid market fragmentation due to local and global ambitions. Moreover, regulators should go beyond the human-centred approach establishing a research agenda seeking answers to the technical and methodological open questions regarding the development and assessment of human-AI co-action aiming for a sustainable AI diffusion in the society.

Expectations for Artificial Intelligence (AI) in Psychiatry

PURPOSE OF REVIEW

Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine.

RECENT FINDINGS

For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.

Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation

The proposed European Artificial Intelligence Act (AIA) is the first attempt to elaborate a general legal framework for AI carried out by any major global economy. As such, the AIA is likely to become a point of reference in the larger discourse on how AI systems can (and should) be regulated. In this article, we describe and discuss the two primary enforcement mechanisms proposed in the AIA: the conformity assessments that providers of high-risk AI systems are expected to conduct, and the post-market monitoring plans that providers must establish to document the performance of high-risk AI systems throughout their lifetimes. We argue that the AIA can be interpreted as a proposal to establish a Europe-wide ecosystem for conducting AI auditing, albeit in other words. Our analysis offers two main contributions. First, by describing the enforcement mechanisms included in the AIA in terminology borrowed from existing literature on AI auditing, we help providers of AI systems understand how they can prove adherence to the requirements set out in the AIA in practice. Second, by examining the AIA from an auditing perspective, we seek to provide transferable lessons from previous research about how to refine further the regulatory approach outlined in the AIA. We conclude by highlighting seven aspects of the AIA where amendments (or simply clarifications) would be helpful. These include, above all, the need to translate vague concepts into verifiable criteria and to strengthen the institutional safeguards concerning conformity assessments based on internal checks.

The European Commission’s Proposal for an Artificial Intelligence Act—A Critical Assessment by Members of the Robotics and AI Law Society (RAILS)

On 21 April 2021, the European Commission presented its long-awaited proposal for a Regulation “laying down harmonized rules on Artificial Intelligence”, the so-called “Artificial Intelligence Act” (AIA). This article takes a critical look at the proposed regulation. After an introduction (1), the paper analyzes the unclear preemptive effect of the AIA and EU competences (2), the scope of application (3), the prohibited uses of Artificial Intelligence (AI) (4), the provisions on high-risk AI systems (5), the obligations of providers and users (6), the requirements for AI systems with limited risks (7), the enforcement system (8), the relationship of the AIA with the existing legal framework (9), and the regulatory gaps (10). The last section draws some final conclusions (11).