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o Data collection for industry purposes (Happa et al. 2021) and the potential use of brain data (Behnke et al. 2022; Tricomi et al. 2023; Susser & Cabrera 2023) further complicate privacy considerations.

* **Dignity issues**

o Instances of harassment, hate speech, violent content, and XR pornography underscore the challenges surrounding user dignity and respect within virtual environments.

* **Violence in XR**, by projection (Grinbaum and Adomaitis 2022)

o Concerns about sexual assault and rape of avatars (Hartmann et al. 2010) raise questions about virtual crimes and their impact on users' emotional well-being.

o Issues like killing, murder, or unwanted disappearance of avatars, as well as child crime in XR, demand ethical scrutiny regarding the depiction and consequences of violence in these digital spaces.

o The involvement of non-human agents, including AI, in violent or criminal acts complicates the attribution of responsibility in virtual environments.

* **Issues related to health or mental health**

o Cybersickness (Hughes et al. 2020) poses immediate health concerns for XR users, potentially leading to discomfort and disorientation.

o The potential for virtually created stimulations, such as flashing lights triggering seizures (Fisher et al. 2022), has critical safety implications.

o Psychological impacts, including addiction and the need for a resting period after extended XR use, underscore the mental health dimensions associated with immersive technologies (Basu 2021).

o In psychotherapeutic settings, XR can worsen dissociative disorders.

* **Educational or developmental effects**

o The possibility of negative transfer in education, where virtual learning interferes with or replaces real-world knowledge, raises concerns about the effectiveness of XR in educational settings.

o Impacts on psychological and visual development in children necessitate careful examination to ensure the healthy growth of young XR users.

* **Energy and resource consumption** (including Rare Earth Elements (REE))

o The environmental impact of XR technologies is significant, particularly in the extraction of Rare Earth Elements (REE) required for headsets, highlighting the need for sustainable practices.

o XR's energy consumption in public spaces (Feldman 2018) and the power consumption associated with cloud gaming server farms further contribute to environmental concerns.

### 4.2.2 Core scientific concepts that must appear in a short lecture

The main concepts concerning XR are technical or practical rather than scientific, and describe user experiences or objects and environments with which the user interacts in XR:
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* **Metaverse:** a recent concept (it has existed since 1992 in science fiction) describing a shared, persistent, real-time 3D, digital model environment. Metaverse is the contraction of the Greek prefix meta ("beyond" or "transcending") and the word universe.

* **Immersion:** first-person phenomenal experience of being in a virtual environment (Adomaitis et al. 2022). For a more thorough and philosophical account of the notion of **immersive experience**, see: Chasid 2021; Langland-Hassan 2020; Liao 2018; Schellenberg 2013.

* **Immersive technology:** XR is always mediated by immersive technology.

* **Presence:** first-person impression of attending to events or agents in a virtual world (Suzuki et al. 2023). Lived experience hard to represent by a third person narrative.

* **Interaction:** "interaction refers to the natural interaction between the user and the virtual scene. It provides the users with the same feeling as the real world through feedback" (Yang et al. 2019). AR and VR are modalities of extended reality interaction.

* **Interoperability:** interoperability is a decisive technical concept for thinking towards a universal, or at least global, metaverse: it can define as "the ability to deliver an immersive and persistent virtual experience seamlessly across multiple networked platforms or interconnected virtual spaces" (Zhu 2022). It is a technical prerequisite for the metaverse and the other immersive experiences we could be making in it.

This list is not likely to change much, but it may well depend on:

* The popularity of the metaverse.

* Technical **progress** (headsets or other) to make the interaction more realistic: for instance, passthrough and remote passthrough technologies.

* The **affordability** and the market of XR devices (headsets, haptic systems, etc.): economic inequalities are likely to jeopardize the achievement of a unified, global metaverse.

### 4.2.3 Additional highly relevant topics for ethical analysis in a full-day training course

* **Experiential training/First person experience:** experiential knowledge is considered of primary importance because the whole purpose of this technology is to simulate a human experience of another environment than the physical reality. The main concepts above describe such subjective feelings or experiences (immersion and presence, in particular) provoked by XR.

* A full-day training course could include experiential training: trainees could try for themselves a headset and experience a virtual or augmented environment.
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o As XR devices can be very costly, especially if there are many trainees, the virtual reality or metaverse experience need not be fully immersive. VR can be experienced by other means, or with DIY headsets (usually made of cardboard, into which a smartphone is inserted). The following means can give an idea to trainees of what it is like to experience virtual reality:

* National Geographic Youtube 360° channel: https://youtube.com/ playlist?list=PLivjPDlt6ApRq22sn082ZCC9893XtV8xc

* 360 Cities: https://www.360cities.net/

* For instance: https://www.360cities.net/video/tranquil-river-journey/vr

* Games (free or opensource):

* https://konterball.com/

* Create a virtual environment: https://gurivr.com/

* Phone apps:

* Cardboard (google)

* Fulldive VR

* Cardboard VR headsets can be purchased for around 3 to 5 euros a piece (or can be homemade)

* **Generative AI in XR:**

o How do ethical issues around generative AI translate when considered with XR?

o Applications of the AI-XR combination: autonomous cars, robotics, military, medical training, cancer diagnosis, entertainment, gaming applications, advanced visualization methods, smart homes, affective computing, driver education and training, etc.

o See Adomaitis et al. 2022; Reiners et al. 2021

* **Cross-cutting topics** (see also Techethos (Adomaitis et al. 2022))

o With biotechnology:

* Nanotechnology

* Organoids

* Add-ons/suits/implants interfaces

o Modes of interaction:

* Parasocial interaction: in this type of interaction, the viewer develops a sense of familiarity, attachment, or even a one-sided friendship with the media figure, even though there is no actual personal relationship or direct communication between them.

* Real human interaction in a virtual setting.

o Labour and economics in the metaverse:

* Telecommuting/remote work

* NFTs

* Tokenization

* Property in the metaverse

* **Philosophical (metaphysical and epistemological) distinctions between virtual, and actual, or physical, reality:**
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o Virtual fictionalism (McDonnel &Wildman): the idea that virtual objects are not real but fictional (Walton).

o Virtual realism (Chalmers 2022): the thesis that virtual reality is genuine reality.

o Other **reflections on the ontological and ethical status of objects and beings** in the metaverse:

* To address common concerns and/or **avoid trivial matters in evaluating a project as a REC member**, such as: is it worth considering, issues like the dignity or responsibility of artificial beings (humanoid AIs, for instance) themselves in the metaverse?

#### 4.2.4 Key uncertainties concerning extended reality at this time

* **Distributive justice**: the main uncertainty is about the number of users. As some XR devices/hardware become relatively inexpensive or accessible to the public (currently between around 500€ for Meta's headset to 3500€ for Apple's headset), this might encourage many mainstream users to become metaverse players: the fee to access the metaverse will be decisive (and a distributive justice issue).

* **The full impact of generative AI on the metaverse** remains uncertain, encompassing aspects such as avatars and environments.

* **Inclusivity issues**:

o **Disabilities**: Addressing the needs of individuals with disabilities is crucial for creating an accessible and welcoming metaverse.

o **A less attractive market (for industry)**: Industries might perceive the market of people with disabilities as less lucrative, potentially leading to a lack of tailored offerings.

o **VR headsets are unadapted to certain disabilities**: The design of VR headsets can inadvertently exclude individuals with specific disabilities, limiting their participation in the metaverse.

o **Inclusive development**: Prioritizing inclusive development ensures that the metaverse accommodates diverse needs, fostering equal access and participation for everyone.

#### 4.2.5 Fundamental dilemmas related to extended reality

* Is there a **preference for material reality**? (Adomaitis et al. 2022 [TechEthos])

o Are experiences mediated via XR equivalent to experiences gained in the real world?

o "This debate raises important questions regarding the status of experiences in XR. Are they the same as they would be in material reality, given a high immersion and resolution? Is there a preference for material reality despite pleasures offered in virtual environments? Is the preference constant or evolving with time? Is it based on desire or rationality? How do the ethics of real environments relate to the ethics of virtual environments?" (TechEthos)

* What **ontological status** should be given to **virtual objects**? (Adomaitis et al. 2022 [TechEthos])
* Are objects and environments in extended reality of the same nature as material objects and environments?

* "If simulated objects are real objects, and if our beliefs about these objects are true, then from an ethical point of view real and virtual environments are equivalent."

* What ontological status should be given to virtual beings (for example animals, but also AI generated humans, for instance)?

* If we cannot distinguish between an actual and a virtual being in extended reality, should we grant a (moral and ontological) quality of being sentient to everything that looks like it in the environment generated by XR?

* Do we have an indirect duty to humanity to treat sentient-like beings as sentient beings? (Kant, *Groundwork for the Metaphysics of Morals*, 1785)

* **Equivalence between virtual and material actions or beliefs** (Adomaitis et al. 2022 [TechEthos])

* If we keep the distinction between virtual objects and material objects, consequences of actions in material reality certainly do not equal the consequences of actions in virtual reality. But if we don't, moral principles and reflections in actual reality should apply in extended reality.

* Cognitive equivalence (Adomaitis et al. 2022 [TechEthos]): "For the transfer of behavior from virtual to material realities to work, an equivalence needs to hold between virtual and material actions and beliefs. To establish the equivalence, some scholars claim that VR produces stimuli equivalent to the material ones, while others argue that it induces the same sense of immersive presence."

* Emotional projection (Adomaitis et al. 2022 [TechEthos]): "Despite the fact that cognitive equivalence is not expected to hold, there are emotional effects in XR that do not require cognitive equivalence. They are already functioning in the current state of the art. These effects depend on people anthropomorphizing virtual subjects as having psychological, emotional and moral traits."

* **Cross-cutting topics** (previously known dilemma that also apply): **justice, equity, and accessibility**

* Accessing the metaverse requires resources not available to most humans:

* a steady electrical supply

* high speed internet connection

* headset and/or haptic devices for an immersive experience

* a powerful computer

* Inclusion of people with disabilities:

* How to adapt the metaverse and the technical means of navigating it to visually or hearing impaired (or other disabilities) persons?

### 4.2.6 Useful resources to build training modules

#### Reports/Grey literature
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* IEEE SA - The IEEE Global Initiative on Ethics of Extended Reality: https://standards.ieee.org/industry-connections/ethics-extended-reality/

* Techethos website (page on Digital extended reality): https://www.techethos.eu/digital-extended-reality/

* Zhu, Ling. « The Metaverse: Concepts and Issues for Congress ». Congressional Research Service, 26 August 2022. https://sgp.fas.org/crs/misc/R47224.pdf

* INRIA's Website: https://www.inria.fr/en/how-does-virtual-reality-works

* Basdevant, Adrien, Camille François, et Ronfard. « Mission exploratoire sur les métavers ». Rapport interministériel. France, octobre 2022. https://www.economie.gouv.fr/files/files/2022/Rapport-interministeriel-metavers.pdf

* The Metaverse Standards Forum: https://metaverse-standards.org/

## Video

* Kent Bye's XR Ethics Manifesto:

* https://www.youtube.com/watch?v=CXgY3YXxqJ8

* Masterclass on XR in the classroom (Central Queensland University, Australia):

* https://www.studyaustralia.gov.au/english/masterclasses/extended-reality-xr-in-the-classroom

## EU Research projects involving XR:

* Empower Refugee Women through XR supported Language learning (XRWomen): https://www.motion-digital.eu/post/project-xr-women

* Volumetric 3D Teachers in Educational reality: https://vol3dedu.eu/

* REalisation of Virtual rEality LearnING Environments (VRLEs) for Higher Education (REVEALING): https://revealing-project.eu/

* Extended Reality For DisasteR management And Media plAnning (xR4DRAMA): https://xr4drama.eu/_project_/

* Augmented Reality Instructional Design for Language Learning – ARIDLL project: https://aridll.eu/

* XR4HUMAN — Responsible development and uptake of XR technologies: https://xr4human.eu/

### 4.2.7 Applicability of the concept of compliance to extended reality

Compliance currently only covers the issue of privacy and potential health applications. No overarching framework exists.

### 4.2.8 Applicability of the concept of ethics-by-design to extended reality

The concept of ethics by design applies to XR, but it is rarely applied for lack of operational translation of the values and principles.

### 4.2.9 Most relevant EU or international guidelines or standards related to extended reality

* General Data Protection Regulation (GDPR)

* There are **no straightforward EU or international guidelines governing XR per se**.

* The main contemporary issue is to regulate the metaverse.
<figure><img src="image_102.png" /></figure>

* A not-for-profit organization, The **Metaverse Standards Forum** brings together most of the industrial players involved in the metaverse, with the aim of creating the conditions for its worldwide interoperability: https://metaverse-standards.org/

* **AI Act: the EU AI Act of April 2021** included in its annex the following statement:

* Annex III, article 1: "Biometric identification and categorization of natural persons: (a) AI systems intended to be used for the 'real-time' and 'post' remote biometric identification of natural persons;"

* This statement applies in part to XR, to the collection of biometric data during the user's real-time 3D experience.

* **Council of the European Union**, Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts, paragraph 16, 2022:

* "AI systems deploy subliminal components such as audio, image, video stimuli that persons cannot perceive as those stimuli are beyond human perception or other subliminal techniques that subvert or impair person's autonomy, decision-making or free choices in ways that people are not consciously aware of, or even if aware not able to control or resist, for example in cases of machine-brain interfaces or **virtual reality**. "

**Other Guidelines/Codes of conduct**

* The Open AR Cloud Code of Conduct (CoC) : https://www.openarcloud.org/documents/code-of-conduct

* XR Safety Initiative (XRSI) ethical guidelines for developers, users, and organizations : https://xrsi.org/xrsi-code-of-conduct

## 4.3 Recommendations

### 4.3.1 Establish “digital subcommittees” in RECs

To address the challenge of understanding technical aspects, policymakers should create dedicated "digital subcommittees" within existing RECs or, if no RECs are competent, then establish dedicated "Digital Ethics Committees" (DECs). These bodies should combine XR technical experts with specialists in research ethics, AI regulation, and other related areas. Providing mutual learning time, appropriate training and resources to REC members will enhance the quality of ethics appraisal. Whenever there exists relevant sectorial or professional regulation, it should be taken into account, for example in the medical sector or in security applications of XR.

### 4.3.2 Ensure that AI-generated content in XR can be identified by users

To mitigate risks related to misinformation and manipulation, it is crucial to clearly distinguish between AI-generated and human-generated content, particularly regarding provenance and control of avatars in XR environments. Mandating the use of easily identifiable watermarks in all AI-produced outputs, including avatars, text, images, audio, and video used in XR environments, can promote transparency and inform users about the authenticity and source of the content. Establishing guidelines at the regulatory level will ensure consistent practices across platforms. Ethics experts should consistently check the application of this principle in research projects that
<figure><img src="image_103.png" /></figure>

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include content production by AI systems. Technical solutions are needed to ensure
that provenance of XR content can always be identified.

4.3.3 Consider surveillance capabilities of XR, in particular in virtual work environments

As XR becomes integral to remote work and collaboration, privacy concerns arise due to the potential for increased surveillance capabilities. Policymakers should develop sectorial regulations to protect individuals' privacy and data in virtual reality, including virtual environments. Ethics experts and researchers should consider the possibility of surveillance in XR environments and analyze the implementation of limits on using data for surveillance. Consent documents should be adapted accordingly in order to safeguard user rights. Encouraging the adoption of privacy-enhancing technologies and best practices in data handling will further mitigate privacy risks associated with XR in the workplace.
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# 5. Cross-cutting recommendations

## 5.1 Build appropriate scientific expertise in RECs, particularly in AI ethics

A common conclusion drawn from the study of the four selected technologies and discussions with experts is the need for ethics assessment processes to rely more on scientific expertise. While some RECs consider research ethics a matter of common knowledge, assuming it requires no special training and relies solely on moral intuition, **it is necessary for the ethical evaluation of a research project to be based on precise scientific and technical knowledge** related to the research or development being assessed. In essence, this highlights the importance of a reciprocal relationship between ethics and scientific expertise, promoting a more comprehensive assessment of complex scientific endeavors and fostering a deeper understanding of their ethical dimensions.

An outstanding example is the evaluation of projects in computer science and artificial intelligence. It has become clear from iRECS work that **there are not enough experts in AI ethics in Europe today** with sufficient knowledge of scientific and technological context (see Annex 1 and 2). Integrating AI experts in RECs is a pressing need across RECs. To effectively analyze ethical issues in AI-related projects, one possible strategy is to establish specialized digital ethics committees with a particular emphasis on AI expertise. Another, equally important aspect is the need for a self-evaluation procedure allowing ethics committees to evaluate their own scientific and ethical competence in a lucid and responsible fashion. To avoid overstepping their area of expertise, committees should develop a procedure to seek additional knowledge when needed, e.g., by **inviting external experts** or consulting with other ethics committees. Finally, training trainers remains a priority, particularly for AI ethics.

## 5.2 Move away from one-time compliance checks toward ongoing evaluation and ethics-by-design

Ethics committees frequently find themselves burdened with a heavy workload, leading to extended evaluation timelines for projects. They often struggle to perform medium-term or long-term monitoring of research projects they had previously assessed. At the same time, complex research projects in areas such as artificial intelligence frequently require regular ethical oversight during the lifetime of the project. The evolving landscape of societal changes and norms also necessitates continuous vigilance in monitoring research ethics. One-time compliance checks, especially in the domain of AI, may no longer mean that the result of the project is aligned with new regulation. In essence, the combination of administrative constraints with the dynamic nature of ethical concerns highlights the importance of ongoing ethics oversight.

Adopting an **ethics-by-design approach** is one possible solution, even if RECs typically lack training and resources to implement it. Ethics-by-design emphasizes early integration of ethical reflection in scientific research. It ensures that ethical considerations are not merely an afterthought but are woven into the fabric of the design process. This approach results in technologies and systems that are not only effective but also better aligned with human values and rights. By combining these strategies — embracing ethics-by-design and limiting purely legal compliance checks — ethics committees can effectively foster the culture of ethical excellence and integrity in science and technology. The implementation of this recommendation also
means that the REC model will evolve from the committee form towards a
permanent team or laboratory devoted to research ethics.

5.3 Ethics experts should not merely scrutinize research projects but also advise on new, better reflected and anticipatory research directions

Ethics evaluation is often tasked with preventing or mitigating harm or transgressions while ensuring that scientific and technical endeavors remain within legal boundaries. As a consequence, ethics evaluation is often perceived as a “necessary nuisance” to research and RECs are seen as another body for “policing” science. This is obviously an exaggeration; however, such attitudes do exist across many disciplines and need to be addressed explicitly.

Ethics experts ought to produce reports that are focused on ethics, yet these reports
may **contain scientifically interesting material**, e.g. suggestions concerning new
open questions, advice on addressing ethical issues, etc. Proving that ethics is not only
a matter of checks or controls, but can also inform scientists in an interesting way, is
essential for keeping a good connection between research ethics and scientific
enquiry.
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# Annex 1. Report from EUA leadership roundtable
"Institutional approaches to research ethics and integrity"

## The EUA leadership roundtable

The European University Association hosted on 31 May 2023 an [online leadership roundtable](#). The event was entitled “Institutional approaches to research ethics and integrity: let's talk about new technologies,” and open to rectors and vice-rectors from European universities. It was divided into two parts: a panel discussion with experts, followed by open discussions among participants.

A total of 26 participants were present at the meeting. They were all rectors or vice-rectors from universities in Albania, Andorra, Austria, Belgium, Cyprus, Czechia, France, Georgia, Germany, Ireland, Italy, Portugal, Romania, Slovenia, Spain, Sweden, Switzerland, and Ukraine. Members of EUA secretariat and the iRECS project consortium attended the meeting as observers.

This section collects the main ideas voiced during the event, following the Chatham House Rule: "participants are free to use the information received, but neither the identity nor the affiliation of the speaker(s), nor that of any other participant, may be revealed."

## Key messages and considerations

As a summary of the ideas gathered during the panel and breakout discussions, a list of key messages is presented below along with linked considerations to be made when tackling research ethics and integrity:

* Challenges posed by new technologies need to be constantly reconsidered, and their paradigm-changing consequences should not be underestimated. Universities should embrace such changes and re-define and re-assess the existing structures accordingly, both externally (through scientific research) and internally (university governance). For the latter, an example could be the exploration of XR and AI towards improving science communication.

* **Consideration 1:** embrace technological change as an opportunity and explore its potential to solve their related challenges.

* Ethics and integrity should be understood as best practice within any research endeavor. Current risks regarding over-publication and the debates on authorship might have been increased by the development of AI and Natural Language Processing models, but they existed before and go beyond the use of AI. Structures and practices need to be re-assessed. Research Ethics Committees (RECs) can raise the alarm about malpractice, but getting to the root of a problem remains complicated both for RECs and university leaders alone, and additional legal support should be provided.

* **Consideration 2:** understand integrity challenges as a whole and research their causes beyond the mere use of new technologies for malpractice.

* The structure and role of RECs varies not only between countries, but also at sub-national level and even at institutional level depending on the discipline.
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<figure><img src="image_110.png" /></figure>

Different regulatory levels apply, which might endanger academic freedom. It must also be noted that research ethics and integrity are not always separated within universities, and bodies in charge of them collaborate on a regular basis in some cases.

o **Consideration 3:** Always consider academic freedom as an indispensable element of the academic and research endeavor, acknowledging the need of a balance with different levels of regulation and monitoring regarding research ethics and integrity. The ultimate responsibility of conducting ethical research lies with individual researchers and research teams, and positive reinforcement (e.g., "responsible AI" licenses) could be a useful tool.

* University governance bodies do consider the opinions of RECs. RECs are both mediators and enablers of research: transmitting general concerns to researchers and helping them adapt their research design accordingly. Additionally, in view of the growing ethical challenges and limited resources, RECs face a capacity limit problem.

o **Consideration 4:** improve how research ethics procedures are communicated internally, along with making the role of RECs clear and understandable to the research community. RE&I is not a barrier to research but a channel of good practice.

o **Consideration 5:** increase the support for RECs, not only in terms of resources, but also regarding administration and digitalization.

* The public is increasingly contesting the validity of scientific work. Several factors apply, but potential solutions include universities positioning themselves in cases of public misinformation, carefulness when sharing research results, and expectations management regarding scientific work.

o **Consideration 6:** acknowledge science communication as part of research ethics efforts. There is a bridge-building role of researchers between academia and society. An ethical approach from researchers' side to communicating their scientific results must consider making such communication clear and easy to understand to the public, as well as wisely choosing the debates in which they take part according to their expertise. Training opportunities for researchers are needed in this respect.

o **Consideration 7:** foster research on science communication, including the potential role of storytelling while keeping objectivity at its core, and empower universities to position themselves and take responsibility for adequately informing the public when fake information is spread.

* Research ethics training becomes a priority when talking about early-stage researchers. It is not only a matter of researchers' embracing ethical and responsible values, but also of managing their expectations towards research ethics assessment, as well as those of the university towards them.

o **Consideration 8:** include anticipation skills in research ethics training, as well as practical details on the interaction with RECs and what to expect from research ethics assessment processes.
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* **Consideration 9:** find a balance between mentoring, advising, and providing diversified training for early-stage researchers so that their core activity remains the conduct of research.
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# Annex 2. Report from the EUA focus group on research ethics and integrity

## Introduction

The European University Association hosted on 13 November 2023 an [online focus group](#) in the context of the iRECS project (improving Research Ethics Expertise to Ensure Reliability and Trust in Science). The event was entitled “[New Technologies and Training Needs for Research Ethics Committees,](#)” and open to university leaders, directors of research and innovation offices, members and chairs of research ethics committees, directors of doctoral schools, research ethics and integrity officers, research managers, open science delegates and researchers from European universities.

A total of 41 participants from universities in 19 countries were present at the meeting (Austria, Belgium, Croatia, Czech Republic, France, Germany, Ireland, Kosovo, Lithuania, Malta, Netherlands, Nigeria, Poland, Portugal, Spain, Switzerland, Turkey, Ukraine, United Kingdom). The focus group comprised members and chairs of research ethics committees, university leaders (vice-rectors) and heads of departments in charge of research ethics and integrity, support officers and advisers on research ethics and integrity. Members of EUA secretariat and the iRECS project consortium attended the meeting as observers. Three representatives of EUA members were also invited to share initial reflections and to prompt discussions on the draft recommendations:

- Filip Colson, Policy Advisor at the Flemish Interuniversity Council VLIR. As part of his portfolio, Filip deals with research ethics and integrity. He organised a seminar with experts on the possible use of generative AI in university policies.

- Senena Corbalán, Vice-Rector for Research, Professor of Biochemistry and Molecular Biology at the University of Murcia. Senena is a member of the EUA R&I Strategy Group, championing the research ethics and integrity dossier.

- Peter Hanenberg, Vice-Rector for Research and Innovation at the Catholic University of Portugal. Peter is a member of the Steering Committee of the EUA Council for Doctoral Education (EUA-CDE)

## Invited reflections

### Filip Colson, Policy Advisor at the Flemish Interuniversity Council VLIR

Filip Colson welcomed the proposed recommendations, appreciating that they are very well structured, balanced, thorough, and reflect the high level of expertise and experience of contributors in the four selected technologies. He presented a selection of basic principles developed by VLIR to support researchers and research groups when it comes to AI in research, generative AI in particular. Inter-university dialogue and institutional learning are key in this respect. Although those principles target individual researchers, they are also relevant in the institutional context, because there is an overlap in training needs:

- Transparency is essential when it comes to the use of AI, in particular generative AI. The correctness of output should be verified, to ensure correct sources.

- Researchers should respect copyright, personal data, and be mindful of undetected/unprotected IP.

- Researchers must be responsible for the output they publish. This includes awareness training regarding ethics in AI.
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Filip Colson then identified two possible caveats in the recommendations:

* Synthetic data is not addressed – which mimics existing datasets. It has limitations, but these can be overcome. Care must be taken when evaluating these data for quality and biases. The output is highly dependent on the data quality.

* Sustainability: increasing energy demands for AI should be factored into ethical use (Nature Machine Intelligence recently published an article on this issue).

Finally, he mentioned the 'VLIR Mind the GAP (for Good Academic Practices)' initiative: English podcast on research integrity, with episode on AI (forthcoming, summer 2024). Filip Colson also shared extensive feedback in a separate document (provided in annex 1).

**Senena Corbalán, Vice-Rector for Research, Professor of Biochemistry and Molecular Biology at the University of Murcia**

Senena Corbalán congratulated the authors on this very complete and impactful document and shared her reflections on the proposed recommendations (her detailed feedback is provided in annex 2):

* The general message is that all recommendations (i.e. for the four technologies and cross-cutting recommendations) are extremely useful but their implementation in universities will not be easy. This would require extra effort that we need to consider. For instance, universities would need financial support. The experts should also be incentivised to contribute to RECs, because it is time consuming.

* The concepts of interest for RECs trainings are well defined, but in many cases, more than a full-day training course will be needed. In any case, since the topics are well defined, the extension of the course can be handled depending on the needs of each REC (this should be commented in the text).

* Digital Ethics committees as independent committees may not be able to address inter- and multi-disciplinary aspects of the research about or using these technologies. Data protection matters are also to be considered.

* To mitigate the risk of heterogenous evaluations, it is important for RECs to come to a uniform opinion, by working together and continuously learning together. Bigger committees at EU level could be helpful to solve the new issues that will arise.

* Biobanking: very good recommendations. Would be good to consider an EU-level standardised consent model, to be adapted.

* Genome editing: very appropriate recommendations.

* It is indeed necessary to build scientific expertise before evaluating.

* The idea of ethics-by-design makes sense but will complicate the functioning of the RECs, with continuous follow-up needed. This needs many resources, both time and financial. This statement should be included in the recommendation.

**Peter Hanenberg, Vice-Rector for Research and Innovation at the Catholic University of Portugal**

Peter Hanenberg shared key considerations on research ethics, in the context of doctoral education for early career researchers (ECRs):
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<figure><img src="image_118.png" /></figure>

* Maybe we should not consider ethics dumping but focus on ethics championing instead. AI is about human intelligence.

* Norms: necessary to have norms and regulations but it may not be enough. No need to wait for norms to start training. Need to raise awareness on those issues, beyond normative checklists. We need debate on these issues and to include ECRs in these debates. An integral and transversal approach to training is needed, for RECs but also by RECs themselves.

* RECs should be partners in research and not the police. They should be included as much as possible, as part of the whole research process, in all research groups. Having AI specialists in RECs may not be the solution. It is important to reflect on the purpose and not on the feasibility only.

* Embedding ethics process is a good approach.

* Mentorship by senior researchers can be a way to bring this awareness into life and to keep it alive.

## Focus group discussion

Overall, participants welcomed the proposed recommendations. They highlighted the need for such comprehensive recommendations, appreciated the clarity of the document and noted that it will be impactful. This was further confirmed by the written feedback to a questionnaire sent to all participants, detailed in the next section.

## Considerations

The following considerations were made:

* It might be worth defining what is meant by “AI experts”. Are they AI developers coming from IT disciplines, or more generally people with expertise in AI, including experts in the ethical and social impacts of AI?

* Whether AI specialists should be integrated in existing RECs or ad hoc RECs or dedicated subcommittees for AI should be built may be context-dependent. The first option is discussed in the recommendations, but not the second. In France, there is a separation between RECs dedicated to health (CPP) and RECs that deal with research that does not involve health (CER, etc.). Autonomous committees could be an option.

* Instead of dedicated committees, laboratories of digital ethics could be set up to help raise awareness and have a debate on those issues.

* The implementation of some recommendations (e.g. the integration of AI experts in RECs) may be challenging, because they are very busy and not necessarily motivated. The lack of AI specialists might create a problem, especially for interdisciplinary research. The implementation is also particularly challenging in smaller universities, with a limited pool of potential experts. How can they assess each other or themselves? What do we do when those experts are not available? What about other technologies – when there are no experts? How do we ensure that those who contribute to technology, if not expert in technology, can still provide feedback? External guidance and training on issues may be critical.

* The idea of establishing a pool of experts at EU level or even at international level was envisaged, using a cooperative approach between neighbouring universities. On the one hand, it was assessed positively, because in many cases, there is a unique reference at EU level. On the other hand, from a more
<figure><img src="image_119.png" /></figure>

practical perspective, there may be some problems in terms of intellectual
property (NDAs etc).

* To extend the pool of potential AI experts, they could also be considered as consultants, working in hospitals and in other settings.

* One of the main challenges for the future lies in the traceability of content (i.e. what is human vs. Al generated), and transparent communication about it. This is important for Recommendation 4.2. 'Ensure that Al-generated content in XR can be identified by users'.

* XR could be specific (compared to AI, Biobank and genome editing) in that some research in this area does not seem to (necessarily) involve heavy funding. Ethics-by-design could be more complicated to implement for small(er) projects.

* Issues related to AI/emergent technology research also arise outside of healthcare, so they should not be automatically considered in the context of bioethics. It may depend more on the institution and its REC structure.

* No one-size-fits-all approach, need to adapt to the needs of the university, and the structure of its committee(s).

* It is important to give sufficient bandwidth for RECs, to give them time and space to reflect.

## Suggestions

The following suggestions were made:

* The target group(s) for those recommendations should be clearly identified: Are we speaking more to researchers, policymakers, etc.?

* The recommendation on unified criteria for reporting incidental findings to subjects could be extended beyond biobanking. This exercise would also be beneficial for researchers in SSH.

* In terms of training, best practices, special training, and recommendations (or even guidelines) are considered very useful. Several complementary options were discussed:

* A "Train-the-trainer" initiative could be added to the recommendations, as a preamble or additional recommendation.

* Peer to peer support can be an option for smaller universities. There is a need of researcher pioneers who can drive the ethical debate from the bottom up. At the University of Antwerp, researchers working on AI came together on the [Antwerp Center of Responsible AI](https://www.responsible-ai.be). This is a good example of the advantages of smaller scale organisations.

## Questionnaire

The draft recommendations on training needs for research ethics committees were shared with focus group participants on the event platform. Participants were invited to complete a brief questionnaire to gather their feedback on the recommendations. The questionnaire was launched on 10 November and remained open until 15 November. 21 participants completed the questionnaire. The results are detailed below.

### Are the proposed recommendations relevant?

21/21 YES, with the following additional comments:

* The cross-cutting recommendations are of special relevance.

* They target contemporary issues that RECs are facing.
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* I think that the recommendations on four selected new technologies are helpful and highly relevant.

* The questions raised are important, and the proposals formulated are coherent. However, they raise important questions of feasibility.

* The recommendations are relevant considering the rapid evolution of technologies tested on plants, animals and humans that need expertise in RECs and scientific committees. These committees usually do not have expertise in digital technologies, nor do they have harmonised guidance.

* AI, biobanking, genome editing and XR experts are clearly needed to support ethical committees.

* The recommendations for establishing Digital Sub committees look very important to me, but also recognizing AI content. Also inviting external experts or consulting with other ethics committees is very much needed.

**Are the proposed recommendations clearly formulated?**

20/21 YES. The person who said NO specified as follows: "Probably, there is a need for some more specific recommendations in which way ethical expertise and expertise in AI, biobanking, genome editing and XR are interactively combined to reach valid judgements, recommendations and the like."

Other comments are listed below:

* Yes, since they could help to the development of solutions preventing possible problems and establishment of good practise framework.

* They clearly articulated different complex issues in science and technology and how these could be managed by providing the needed expertise in RECs and scientific committees.

**Are the proposed recommendations applicable in the short term?**

14 YES / 7 NO, with the following comments from respondents who said NO:

* They need to be discussed and "democratized" within the institutions. And for those at EU level, everybody knows how slow these procedures are.

* I feel that there is a gap between the recommendations and actually applying them.

* The number of committees, and the availability of their members, do not always make it possible to combine ethical expertise with research specialization of the type that is expected here.

* We need a clear framework and standards for ethical clearance.

Additional comments are listed below:

* Cross-cutting recommendations no. 5 could be applicable in short term since they are related to Ethics committees acting on research organizations.

* Since ChatGPT and other generative AI found their way in late 2022, there has been lots of discussion and reflection leading to local and international immediate recommendations. The same concerns are topical for other medical devices that cut across plants, animals and humans.

* As the issues do not be dealt with are there right now, the present recommendations are a welcome guide until more specific recommendations are being developed.
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* It doesn't take so much time to form the sub-committees, and the inviting external experts or consulting with other ethics committees.

**Are the proposed recommendations applicable in the medium term?**

19 YES / 2 NO, with the following comments from respondents who said NO:

* Not sure I think some are however others are not.

* As stated before, more specific and perhaps extended recommendations should be developed in the medium term.

Additional comments are listed below:

* For some recommendations, the establishment of network of ethics experts and specialists could take some time.

* If states and institutions give RECs the means to achieve these ambitions.

* We hope the recommendations in the short term will provide directions for review and opportunity for medium-term recommendations.

* Move away from one-time compliance checks toward ongoing evaluation and ethics-by-design will be in the medium and permanent basis.

**Are the proposed recommendations comprehensive? Would you add any elements?**

17 YES / 4 NO, with the following comments from respondents who said NO:

* Rather vague at times.

* I think copyright and data protection issues need also be specifically addressed.

Additional comments are listed below:

* It can be assumed that some new recommendations will arise during implementation process.

* Some points raise questions, but the length of the document seems appropriate to me as it stands. Lengthening it would harm the possibility of it being read.

* Some examples and explanations.

* We need to add the following elements: 1. Developing ethics expertise (consultants) in the global South where international collaborative research between EUA/EU and other continents (e.g. Africa) involves the implementation of the recommendations between committees that oversee such studies.

**In your view, what are the most urgent or pressing recommendations?**

Participants identified the most urgent or pressing recommendations. The three most selected recommendations are listed below and detailed scores are provided in the figure below:

* 5.1 Build appropriate scientific expertise before evaluating

* 3.3 Highlight policy differences between countries, including EU member states

* 2.1 Implement a standard consent model across EU member states
<figure><img src="image_124.png" /><figcaption>Figure 3: Chart of Priority Recommendations</figcaption></figure>

Do you want to add any other comment on a specific recommendation? Please select the recommendation and use the text box to provide feedback.

* 1.1 Adapt the composition of RECs to include AI experts: (1) RECs should include AI & biobank ethics experts (consultants). These experts may not be limited to EU member states but internationally, where there is a possibility for multinational collaborative research involving AI, Biobanking, and gene editing/genome research across continents. (2) Depending on the frequency with which AI is encountered by a committee, and where resource is limited, it may be possible to alternatively have expert members who are called in when required?

* 1.2 Set uniform and coherent 'AI in healthcare' guidelines across EU member states: The idea of establishing specific Digital Ethics Committees (DECs) is clear but sounds like an independent committee and given the multi and inter-disciplinarity of these techniques, I think all experts should be working together in the REC to facilitate the continuous learning process.

* 2.1 Implement a standard consent model across EU member states (no feedback added)

* 2.3 Refine and homogenize the scope of reportable incidental findings: This question seems to me to go beyond the case of biobanking. I am not aware of any harmonization around the issue of incidental findings generally, but I may have missed something.

* 4.2 Ensure that AI-generated content in XR can be identified by users: The highlighted issue is important to address as touches copyright issues in many aspects.

* 5.1 Build appropriate scientific expertise before evaluating (no feedback added)
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## Anything else you would like to add?

* The focus in the recommendations seems to be only on healthcare. It may be more appropriate to focus on, for example, 'AI in research' as many Universities will conduct non-healthcare research where issues around AI also need to be considered.

## Detailed feedback on AI and generative AI, from Filip Colson

Personal background: VLIR, Flemish Interuniversity Council, 5 research intensive universities + Institute of Tropical Medicine, + in our working groups: Institute of Biotechnology (VIB), imec (Interuniversity Microelectronics Centre), and other strategic research centres.

AI in research policy: topic of monthly debate at VLIR level:

* Research & Innovation WG (vice-rectors and research directors), and

* WG on Science, Ethics and Integrity (universities' ethics committees secretaries, and central services);

* VLIR Event in June 2023 on use of generative AI in research

Current version of recommendations: very well structured, balanced, thorough, and shows the amount of work, expertise and experience in these technologies of the people involved.

My main focus is the adequate support of researchers and research groups, and the interuniversity dialogue and institutional learning on evolving topics (such as AI)

* So, no critique on the Recommendations, just pointing out that this document is not aimed at (and therefore not useful for) individual researchers, who are usually my focus group and audience when it comes to capacity building exercises.

* But all well explained on p. 13 of the Recommendations, background: training needs for research ethics committees' members and EU ethics appraisal scheme experts.

With the above in mind, two caveats in the Recommendations: (1) synthetic data and (2) sustainability.

(1) Synthetic data: generation of synthetic data that mimic existing dataset (limitations such as privacy issues and confidentiality of real datasets can be overcome), but one has to carefully evaluate the generated synthetic data for quality and possible bias (as the output is highly dependent on the quality of the data on which the models are trained) + be transparent (mention explicitly, including reference)

Synthetic data is artificial data that is generated from original data and a model that is trained to reproduce the characteristics and structure of the original data. This means that synthetic data and original data should deliver very similar results when undergoing the same statistical analysis. The degree to which synthetic data is an accurate proxy for the original data is a measure of the utility of the method and the model.

The generation process, also called synthesis, can be performed using different techniques, such as decision trees, or deep learning algorithms. Synthetic data can be classified with respect to the type of the original data: the first type employs real datasets, the second employs knowledge gathered by the analysts instead, and the third type is a combination of these two. Generative Adversarial Networks (GANs) were introduced recently and are commonly used in the field of image recognition. They are
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generally composed of two neural networks training each other iteratively. The
generator network produces synthetic images that the discriminator network tries to
identify as such in comparison to real images.

A privacy assurance assessment should be performed to ensure that the resulting synthetic data is not actual personal data. This privacy assurance evaluates the extent to which data subjects can be identified in the synthetic data and how much new data about those data subjects would be revealed upon successful identification.

Synthetic data is gaining traction within the machine learning domain. It helps training machine learning algorithms that need an immense amount of labelled training data, which can be costly or come with data usage restrictions. Moreover, manufacturers can use synthetic data for software testing and quality assurance. Synthetic data can help companies and researchers build data repositories needed to train and even pre-train machine learning models, a technique referred to as transfer learning.

Positive foreseen impacts on data protection:

* Enhancing privacy in technologies: from a data protection by design approach, this technology could provide, upon a privacy assurance assessment, an added value for the privacy of individuals, whose personal data does not have to be disclosed.

* Improved fairness: synthetic data might contribute to mitigate bias by using fair synthetic datasets to train artificial intelligence models. These datasets are manipulated to have a better representativeness of the world (to be less as it is, and more as society would like it to be). For instance, without gender-based or racial discrimination.

Negative foreseen impacts on data protection:

* Output control could be complex: especially in complex datasets, the best way to ensure the output is accurate and consistent is by comparing synthetic data with original data, or human-annotated data. However, for this comparison again access to the original data is required.

* Difficulty to map outliers: synthetic data can only mimic real-world data; it is not a replica. Therefore, synthetic data may not cover some outliers that original data has. However, outliers in the data can be more important than regular data points for some applications.

* Quality of the model depends on the data source: the quality of synthetic data is highly correlated with the quality of the original data and the data generation model. Synthetic data may reflect the biases in original data. Also, the manipulation of datasets to create fair synthetic datasets might result in inaccurate data.

## (2) Sustainability

1. Harvard Business Review, Ajay Kumar and Tom Davenport, 20 July 2023 (https://hbr.org/2023/07/how-to-make-generative-ai-greener):

Almost all of the best-known generative AI models are generated by “hyperscale” (very large) cloud providers with thousands of servers that produce major carbon footprints; in particular, these models run on graphics processing unit (GPU) chips. These require 10–15 times the energy a traditional CPU needs because a GPU uses more transistors in the arithmetic logic units. Currently, the three main hyperscale cloud providers are Amazon AWS, Google Cloud, and Microsoft Azure.

If we try to understand the environmental impact of ChatGPT through the lens of carbon footprint, we should understand the carbon footprint lifecycle of machine
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learning (ML) models first. That's the key to beginning to make generative AI greener through lower energy consumption.

2. Nature Machine Intelligence, Charlotte Debus et al., 10 November 2023 (https://www.nature.com/articles/s42256-023-00750-1) (Debus, C., Piraud, M., Streit, A. et al. Reporting electricity consumption is essential for sustainable AI. Nat Mach Intell (2023). https://doi.org/10.1038/s42256-023-00750-1)

The rise of artificial intelligence (AI) has relied on an increasing demand for energy, which threatens to outweigh its promised positive effects. To steer AI onto a more sustainable path, quantifying and comparing its energy consumption is key.

Even though current AI algorithms can tackle many applications with stunning results, we must ask what the cost of these achievements is in terms of consumed resources. Already, only a few companies and research institutions can provide the necessary compute resources to train, fine-tune and deploy state-of-the-art models, leaving behind most AI researchers, as well as small and medium enterprises. This hinders wide implementation of AI methods and points to a broader concern: especially in light of climate change and geopolitical resource availability, the price of AI advances in terms of their environmental footprint must be assessed. Substantial efforts are required to improve the energy efficiency of modern AI algorithms. Before energy consumption can be optimized, however, it must be quantified.

Here, we discuss recent efforts to monitor AI carbon footprint and energy consumption and issue an urgent call for action for the AI developer community to measure and report the environmental impact of methodological advances in a standardized, quantifiable and comparable manner to advance the goal of environmentally sustainable AI research.

Flemish universities' AI in research/education policies:
https://www.kuleuven.be/english/education/student/educational-tools/generative-artificial-intelligence

Tips and tricks for responsible use of GenAI (for students)

**Be transparent about the use of GenAI:** If the use of AI is allowed, you should be transparent about it. Read more specific information about this adapted to [teaching staff](https://www.arts.cuohl.be/en/arts-and-culture/arts-teaching-staff), [students](https://www.arts.cuohl.be/en/arts-and-culture/arts-teaching-staff) or [researchers](https://www.arts.cuohl.be/en/arts-and-culture/arts-teaching-staff).

**Never import (privacy)sensitive or confidential information (including unprotected discoveries):** Often, there is no insight in what the owners of AI applications do with imported data. In many cases, data saved on a non-European cloud is turned into applications that are not GDPR compliant. This is the case for personal data as well as new discoveries concerning scientific research. The entry of a discovery can be equivalent to disclosure and thus prevents the possibility of filing a patent for that discovery. So make sure you do not enter personal data or confidential information into these GenAI-applications. Check whether you have the necessary permission or license to enter copyrighted material. In case of doubt about the confidential nature of information, you can inquire about this with the information provider.

**Make us of the 'opt out'-option of ChatGPT:** Note that ChatGPT can link your information to you directly when it generates new texts (for others). It is thus possible that you - unknowingly - become a source of information for third parties by simply using the tool. ChatGPT does allow opting for "disable chat history and model"
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**training**" in settings (Data Controls FAQ), so that imported data cannot be used for the models' training and consequently not for generating new texts (for others). The possibility "to keep my history on but disable model training" also exists if you fill in a form for a "user content opt out request". In this last case, it is still possible to retrieve old conversations with ChatGPT. Attention: the opt out-possibility only ensures your entered data will not be used for the models' training but does not necessarily mean you should enter your privacy sensitive, IP-protected or copyrighted material. After all, entered info is often saved by the tool's owner and there is usually no transparency about what will happen with that data.

**Verify GenAI:** Use GenAI if permitted, but do not blindly trust technology. The more responsibility you place on the system, the more verification, control and justification is needed. Sometimes, AI output can seem very convincing, but it is perfectly possible that the answer is incorrect or even made up ('hallucinating AI'). In most cases it is impossible to retrieve the way algorithms have achieved a result and there is no transparency about sources used ('black box'). Check thoroughly what you use and look for existing source material you can cite. After all, you are the one responsible for the content of your delivered work.

**Avoid plagiarism or any infringement of copyright:** It is known that the generated references from the current version of ChatGPT and other text generating AI-tools are sometimes fictitious. In GenAI's output, transparency about the used sources is sometimes absent, increasing your risk of plagiarizing. GenAI builds on work of others and a correct citation is always needed. The risk at copyright infringements is also apparent. The databases these tools use contain a lot of source material. So far, it is not clear if the authors have granted their permission for this or that copyright is being respected.

**Double check the most recent state of affairs:** The tools do not always use the most recent data available, resulting in outdated output. The free version of ChatGPT does not have access to information of recent years.

**Be prepared for bias by GenAI:** AI-applications are trained based on certain datasets, which sometimes are not representative. Transparency about the filters they apply does not exist. This raises a lot of ethical questions. With uncritical use, you risk further dissemination of incorrect information, certain stereotypes or prejudices.

**Do not be satisfied with the first output:** Asking a simple question rarely produces useful GenAI output. The more information and instructions you offer the tool, also called prompt engineering, the more useful the output becomes. On top of that, you should be aware of the output's bad reproducibility. Whatever ChatGPT (or similar tools) generate can differ with every attempt and lead to totally different answers. Another answer is also generated when you press "Regenerate response" after receiving an answer.

**Give meaning to GenAI:** When people hear a sentence, they link significance to it, connecting the utterance to reality in that way. A generative language model only has access to the form but can in no way establish the link to reality. As a result, you have no guarantee that the system's texts are indeed correct. The system's only concern is generating texts that seem as plausible as possible. Truth or reality count for nothing. By attaching meaning to GenAI's output, you create added value as a user.
**Do not humanize GenAI tools:** AI applications (chatbots in particular) are not human, even though it may seem so because of their interactive reactions. They are not influenced by personal experiences or their proximate environment. Via data they can be taught ethical principles. It is important to be aware at all times that the AI applications are mere technological resources. Refer to it as a thing ("it reacts"), not as 'he' or a 'she'.

**Surpass GenAI:** GenAI-application can sometimes be a resource for assignments (visual, writing and programming tasks). We expect students to add an extra layer: regarding argumentation or foundation, critical analysis, complete and correct citation, creative input, personal perspective or reflection, innovative character, connection to societal context ... As a student, you should be able to have a conversation or conduct a debate about your assignment.

**Limit GenAI's energy usage:** Do not forget that the energy costs of AI application's servers are very high. That is why you should only use them if they can add value.

**VLIR Mind the GAP (Good Academic Practices):** English podcast on research integrity, with episode on AI (summer 2024)

**Detailed feedback on the implementation of the proposed recommendations, from Senena Corbalan**

The document presents a very good study on the training needs for research ethics committees, concerning these 4 new technologies: AI in health and healthcare, biobanking, Genome-editing, and Extended reality. The gaps, challenges and grey areas have been detected for each technology. After the analysis, three recommendations have been proposed for each technology and another three cross-cutting recommendations.

Related to the concepts to study in the courses, I would say they are also well defined, but in many cases, more than a full-day training course will be needed. In any case, since the topics are well defined, the extension of the course can be handle depending on the needs of each REC (this should be commented in the text).

Concerning AI in health and healthcare, I agree that RECs need to include AI experts in the committee. The idea of establishing specific Digital Ethics Committees (DECs) is clear but sounds like an independent committee and given the multi and inter-disciplinarity of these techniques, I think all experts should be working together in the REC to facilitate the continuous learning process for all the members.

I find very difficult to recommend that to every REC since the infrastructure needed is complex and it might be very difficult to implement in many universities. This would increase the heterogeneity among RECs.

Because of that, the idea of setting uniform and coherent AI in healthcare guidelines across EU member states is fundamental, to address discrepancies and to advice in many of the cases that RECs are finding difficult to solve. The EU HLEG guidelines and the HLEG ALTAI check list are very useful, and I would suggest the High Level Expert Group should be reinforced to be the reference for the RECs in this matters.
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The idea of ETHICS-BY-DESIGN makes a lot of sense but complicates a lot the working system of the RECs and implies a continuous follow-up that needs many resources, both time and financial. This statement should be included in the recommendation.

Related to Extended Reality, I think it is not very different from AI, in the issues to take into account for evaluation by the RECs, given the multi- and inter-disciplinary nature and the data protection matters.

Respect to Biobanking, I think the 3 recommendations make sense, given the actual situation. The standardization of the consent model across EU member states would enormously facilitate the process. However, the idea of dynamic might probably generate many delays. Perhaps, it is better to act in two pathways:

- One, preparing a consent model that specifies with details the implications of the donation of the samples for the patient. This can be updated annually.

- Second, the intervention of the REC is crucial to decide whether the research is appropriate or not, and this is given a security to the patients.

Refining and homogenizing the scope of reportable incidental findings is essential too and the gene list proposed is adequate, but it is important to include in the recommendation that the list should be annually updated.

Genome editing, the three recommendations are correct. Training the ethics evaluators is fundamental to reach a homogeneous evaluation. Due to the implication the case-by-case approach is important to minimize the potential adverse environmental effect. Unification of standards is necessary too.

Cross-cutting recommendations:

- Necessary to build the scientific expertise before evaluating.

- Ethics-by-design makes sense but will complicate a lot the functioning of the RECs, additional support is necessary. This should be mentioned specifically.

- Function of Advice by the RECs, not only at the time of the evaluation but also by designing continuous training for the committees and researchers.

- All four subjects are new matters still in development, so a centralized unit of analysis that also dictates recommendations and continuous training is crucial to have a standard way of acting based on science and experience. It should be complemented with the implementation of expert researchers in these areas by each REC, but they should have a standard reference to have homogenous decision-making.

General message: all recommendations are extremely useful but their implementation in the universities will need an extra effort that we need to consider to be able to design policies that help research ethics committees to accomplish all of them, mainly considering that it is a matter in continuous evolution.
# References

## AI in health and healthcare

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Alzubaidi, Laith, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie, et Laith Farhan. « Review of deep learning: concepts, CNN architectures, challenges, applications, future directions ». *Journal of Big Data* 8, n° 1 (31 mars 2021): 53. https://doi.org/10.1186/s40537-021-00444-8.

Aroyo, Alexander M., Jan de Bruyne, Orian Dheu, Eduard Fosch-Villaronga, Aleksei Gudkov, Holly Hoch, Steve Jones, et al. « Overtrusting Robots: Setting a Research Agenda to Mitigate Overtrust in Automation ». *Paladyn, Journal of Behavioral Robotics* 12, n° 1 (1 janvier 2021): 423-36. https://doi.org/10.1515/pjbr-2021-0029.

Balthazar, Patricia, Peter Harri, Adam Prater, et Nabile M. Safdar. « Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics ». *Journal of the American College of Radiology, Data Science: Big Data Machine Learning and Artificial Intelligence*, 15, n° 3, Part B (1 mars 2018): 580-86. https://doi.org/10.1016/j.jacr.2017.11.035.

Bernstein, Michael S., Margaret Levi, David Magnus, Betsy A. Rajala, Debra Satz, et Quinn Waeiss. « Ethics and society review: Ethics reflection as a precondition to research funding ». *Proceedings of the National Academy of Sciences* 118, n° 52 (28 décembre 2021): e2117261118. https://doi.org/10.1073/pnas.2117261118.

Bertuzzi, L., 'AI Act: EU Countries Headed to Tiered Approach on Foundation Models amid Broader Compromise', *Www.Euractiv.Com*, October 17, 2023.
https://www.euractiv.com/section/artificial-intelligence/news/ai-act-eu-countries-headed-to-tiered-approach-on-foundation-models-amid-broader-compromise/.

Blasimme, Alessandro, et Effy Vayena. « The Ethics of AI in Biomedical Research, Patient Care, and Public Health ». In *The Oxford Handbook of Ethics of AI*, édité par Markus D. Dubber, Frank Pasquale, et Sunit Das, 0. Oxford University Press, 2020.
https://doi.org/10.1093/oxfordhb/9780190067397.013.45.

Bommasani, R. et al. (2022) 'On the Opportunities and Risks of Foundation Models'. arXiv. Available at: https://doi.org/10.48550/arXiv.2108.07258.

Castellino, Ronald A. « Computer aided detection (CAD): an overview ». *Cancer Imaging* 5, n° 1 (23 août 2005): 1719. https://doi.org/10.1102/1470-7330.2005.0018.

CCNE & CNPEN. Medical Diagnosis and Artificial Intelligence: Ethical Issues. Joint opinion of the CCNE and CNPEN, CCNE Opinion 141, CNPEN Opinion 4. November 2022
https://www.ccne-ethique.fr/sites/default/files/2023-05/Opinion%20No.141.pdf

Challen, Robert, Joshua Denny, Martin Pitt, Luke Gompels, Tom Edwards, et Krasimira Tsaneva-Atanasova. « Artificial Intelligence, Bias and Clinical Safety ». *BMJ Quality & Safety* 28, n° 3 (mars 2019): 231-37. https://doi.org/10.1136/bmjqs-2018-008370.

Chan, Heang-Ping, Lubomir M. Hadjiiski, et Ravi K. Samala. « Computer-Aided Diagnosis in the Era of Deep Learning ». *Medical Physics* 47, n° 5 (juin 2020): e218-27.
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Chapman, Benjamin P., Feng Lin, Shumita Roy, Ralph H. B. Benedict, et Jeffrey M. Lyness. « Health risk prediction models incorporating personality data: Motivation, challenges, and illustration ». *Personality Disorders: Theory, Research, and Treatment* 10, n° 1 (2019): 46-58.
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Chin-Yee, Benjamin, et Ross Upshur. « Three Problems with Big Data and Artificial Intelligence in Medicine ». *Perspectives in Biology and Medicine* 62, n° 2 (2019): 237-56.
https://doi.org/10.1353/pbm.2019.0012.

Dainow, Brandt, et Philip Brey. « Ethics By Design and Ethics of Use Approaches for Artificial Intelligence, Version 1.0 ». European Commission, 25 novembre 2021.
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https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/ethics-by-design-and-ethics-of-use-approaches-for-artificial-intelligence_he_en.pdf.

Davenport, Thomas, et Ravi Kalakota. « The potential for artificial intelligence in healthcare ». *Future Healthcare Journal* 6, n° 2 (juin 2019): 94-98. https://doi.org/10.7861/futurehosp.6-2-94.

—. « The potential for artificial intelligence in healthcare ». *Future Healthcare Journal* 6, n° 2 (juin 2019): 94-98. https://doi.org/10.7861/futurehosp.6-2-94.

ENERI Classroom. « Research ethics committees: main tasks and challenges ». ENERI Classroom: Training and Capacity-Building Resource, 17 juillet 2023. https://classroom.eneri.eu/research-ethics-committees-main-tasks-and-challenges.

European Commission. Proposal for a DIRECTIVE OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on adapting non-contractual civil liability rules to artificial intelligence (AI Liability Directive) (2022). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52022PC0496.

European Commission. « How to complete your ethics self-assessment, Version 2.0 », 13 juillet 2021. https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/common/guidance/how-to-complete-your-ethics-self-assessment_en.pdf.

European Commission - High-Level Expert Group on AI. « Ethics Guidelines for Trustworthy AI | Shaping Europe's Digital Future », 8 avril 2019. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai.

European Parliament. Directorate General for Parliamentary Research Services. *Understanding Algorithmic Decision-Making: Opportunities and Challenges*. LU: Publications Office, 2019. https://data.europa.eu/doi/10.2861/536131.

Ferretti, Agata, Marcello Ienca, Mark Sheehan, Alessandro Blasimme, Edward S. Dove, Bobbie Farsides, Phoebe Friesen, et al. « Ethics review of big data research: What should stay and what should be reformed? » *BMC Medical Ethics* 22, n° 1 (30 avril 2021): 51. https://doi.org/10.1186/s12910-021-00616-4.

Floridi, Luciano. Ethics, Governance, and Policies in Artificial Intelligence. Springer Nature, 2021. —. « Translating Principles into Practices of Digital Ethics: Five Risks of Being Unethical ». *Philosophy & Technology* 32, n° 2 (1 juin 2019): 185-93. https://doi.org/10.1007/s13347-019-00354-x.

Friesen, Phoebe, Rachel Douglas-Jones, Mason Marks, Robin Pierce, Katherine Fletcher, Abhishek Mishra, Jessica Lorimer, et al. « Governing AI-Driven Health Research: Are IRBs Up to the Task? » *Ethics & Human Research* 43, n° 2 (2021): 35-42. https://doi.org/10.1002/eahr.500085.

Gaonkar, Bilwaj, Kirstin Cook, et Luke Macyszyn. « Ethical Issues Arising Due to Bias in Training A.I. Algorithms in Healthcare and Data Sharing as a Potential Solution ». *AI Ethics Journal* 1 (16 septembre 2020): 1-9. https://doi.org/10.47289/AIEJ20200916.

Harerimana, Gaspard, Beakcheol Jang, Jong Wook Kim, et Hung Kook Park. « Health Big Data Analytics: A Technology Survey ». *IEEE Access* 6 (2018): 65661-78. https://doi.org/10.1109/ACCESS.2018.2878254.

Harrison, Conrad J., et Chris J. Sidey-Gibbons. « Machine learning in medicine: a practical introduction to natural language processing ». *BMC Medical Research Methodology* 21, n° 1 (31 juillet 2021): 158. https://doi.org/10.1186/s12874-021-01347-1.

High Level Expert Group on AI. « Ethics Guidelines for Trustworthy AI », 8 avril 2019. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai.

Kerasidou, Angeliki, et Ruth Horn. « Trustworthy Ethics Review of Medical AI Research Protocols: Developing a Research Ethics Practical Guide for Ethics Review Committees », 17 juillet 2023. https://www.ndph.ox.ac.uk/study-with-us/dphil-population-health/dphil-research-projects/2023-dphil-projects/trustworthy-ethics-review-of-medical-ai-research-protocols-developing-a-research-ethics-practical-guide-for-ethics-review-committees.

Khullar, Dhruv, Lawrence P. Casalino, Yuting Qian, Yuan Lu, Enoch Chang, et Sanjay Aneja. « Public vs Physician Views of Liability for Artificial Intelligence in Health Care ». *Journal of the*
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American Medical Informatics Association: JAMIA 28, n° 7 (14 juillet 2021): 1574-77.
https://doi.org/10.1093/jamia/ocab055.

Kohli, Marc, et Raym Geis. « Ethics, Artificial Intelligence, and Radiology ». *Journal of the American College of Radiology: JACR* 15, n° 9 (septembre 2018): 1317-19.
https://doi.org/10.1016/j.jacr.2018.05.020.

Kop, Mauritz. « EU Artificial Intelligence Act: The European Approach to AI ». Stanford Law School, 17 juillet 2023. https://law.stanford.edu/publications/eu-artificial-intelligence-act-the-european-approach-to-ai/.

Kuo, Wen-Jia, Ruey-Feng Chang, Dar-Ren Chen, et Cheng Chun Lee. « Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images ». *Breast cancer research and treatment* 66 (2001): 51-57.

Lekstutiene, Jurate, Julia Maria Mönig, Tom Lindemann, Bud P. Bruegger, Paul de Hert, Giancaudio Malgieri, Andrés Chomczyk Penedo, et al. « Report on the governance of data protection ELI in ICT research and innovation », 22 juillet 2022.
https://cordis.europa.eu/project/id/788039/results.

Mahomed, S. « Healthcare, artificial intelligence and the Fourth Industrial Revolution : ethical, social and legal considerations ». *South African Journal of Bioethics and Law* 11, n° 2 (novembre 2018): 93-95. https://doi.org/10.7196/SAJBL.2018.v11i2.664.

McLennan, Stuart, Amelia Fiske, Daniel Tigard, Ruth Müller, Sami Haddadin, et Alena Buyx. « Embedded ethics: a proposal for integrating ethics into the development of medical AI ». *BMC Medical Ethics* 23, n° 1 (26 janvier 2022): 6. https://doi.org/10.1186/s12910-022-00746-3.

Morley, Jessica, Caio C. V. Machado, Christopher Burr, Josh Cowls, Indra Joshi, Mariarosaria Taddeo, et Luciano Floridi. « The ethics of AI in health care: A mapping review ». *Social Science & Medicine* 260 (1 septembre 2020): 113172.
https://doi.org/10.1016/j.socscimed.2020.113172.

——. « The ethics of AI in health care: A mapping review ». *Social Science & Medicine* 260 (1 septembre 2020): 113172. https://doi.org/10.1016/j.socscimed.2020.113172.

Morley, Jessica, Lisa Murphy, Abhishek Mishra, Indra Joshi, et Kassandra Karpathakis. « Governing Data and Artificial Intelligence for Health Care: Developing an International Understanding ». *JMIR Formative Research* 6, n° 1 (31 janvier 2022): e31623.
https://doi.org/10.2196/31623.

Powell, John, et Ulrike Deetjen. « Characterizing the digital health citizen: mixed-methods study deriving a new typology ». *Journal of Medical Internet Research* 21, n° 3 (2019): e11279.

Reddy, Sandeep, Sonia Allan, Simon Coghlan, et Paul Cooper. « A Governance Model for the Application of AI in Health Care ». *Journal of the American Medical Informatics Association: JAMIA* 27, n° 3 (1 mars 2020): 491-97. https://doi.org/10.1093/jamia/ocz192.

Schönberger, Daniel. « Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications ». *International Journal of Law and Information Technology* 27 (1 juin 2019): 171-203. https://doi.org/10.1093/ijlit/eaz004.

Shaikhina, Torgyn, et Natalia A. Khovanova. « Handling limited datasets with neural networks in medical applications: A small-data approach ». *Artificial Intelligence in Medicine* 75 (1 janvier 2017): 51-63. https://doi.org/10.1016/j.artmed.2016.12.003.

Sordo, Margarita. « Introduction to neural networks in healthcare ». Consulté le 15 août 2023.
https://www.academia.edu/20719514/Introduction_to_neural_networks_in-healthcare.

Stahl, Bernd Carsten. *Artificial Intelligence for a Better Future: An Ecosystem Perspective on the Ethics of AI and Emerging Digital Technologies*. SpringerBriefs in Research and Innovation Governance. Cham: Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-69978-9.

Sutton, Richard S., et Andrew G. Barto. *Reinforcement learning: an introduction*. Second edition. Adaptive computation and machine learning series. Cambridge, Massachusetts: The MIT Press, 2018.
Ullrich, Daniel, Andreas Butz, et Sarah Diefenbach. « The Development of Overtrust: An Empirical Simulation and Psychological Analysis in the Context of Human-Robot Interaction ». *Frontiers in Robotics and AI* 8 (2021). https://www.frontiersin.org/articles/10.3389/frobt.2021.554578.

Vayena, Effy, et Alessandro Blasimme. « Health Research with Big Data: Time for Systemic Oversight ». *The Journal of Law, Medicine & Ethics* 46, n° 1 (2018): 119-29. https://doi.org/10.1177/1073110518766026.

World Health Organization. « Ethics and Governance of Artificial Intelligence for Health: WHO Guidance ». World Health Organization, 2021. https://www.who.int/publications-detail-redirect/9789240029200.

## Biobanking

Aarts, N, E M Bunnik, et M Boeckhout. « Guide to the Detection, Management and Communication of Incidental Findings for Biobanks in BBMRI-NL », s. d.

Berg, Jonathan S., Ann Katherine M. Foreman, Julianne M. O'Daniel, Jessica K. Booker, Lacey Boshe, Timothy Carey, Kristy R. Crooks, et al. « A Semiquantitative Metric for Evaluating Clinical Actionability of Incidental or Secondary Findings from Genome-Scale Sequencing ». *Genetics in Medicine: Official Journal of the American College of Medical Genetics* 18, n° 5 (mai 2016): 467-75. https://doi.org/10.1038/gim.2015.104.

Berkman, Benjamin E. « Refuting the Right Not to Know » 19 (s. d.).

Berkman, Benjamin E., Dana Howard, et David Wendler. « Reconsidering the Need for Reconsent at 18 ». *Pediatrics* 142, n° 2 (août 2018): e20171202. https://doi.org/10.1542/peds.2017-1202.

Biopankki, Juha Mäkelä, Auria. « Auria Biobank - For Citizens ». Auria Services. Accessed August 16, 2023. https://www.auria.fi/biopankki/en/kansalaisille/.

Blasimme, Alessandro, Caroline Brall, et Effy Vayena. « Reporting Genetic Findings to Individual Research Participants: Guidelines From the Swiss Personalized Health Network ». *Frontiers in Genetics* 11 (11 décembre 2020): 585820. https://doi.org/10.3389/fgene.2020.585820.

Bledsoe, Marianna J. « Ethical Legal and Social Issues of Biobanking: Past, Present, and Future ». *Biopreservation and Biobanking* 15, n° 2 (avril 2017): 142-47. https://doi.org/10.1089/bio.2017.0030.

Bredenoord, Annelien L., N. Charlotte Onland-Moret, et Johannes J.M. Van Delden. « Feedback of Individual Genetic Results to Research Participants: In Favor of a Qualified Disclosure Policy ». *Human Mutation* 32, n° 8 (août 2011): 861-67. https://doi.org/10.1002/humu.21518.

Broekstra, Reinder, Judith L. Aris-Meijer, Els L. M. Maeckelberghe, Ronald P. Stolk, et Sabine Otten. « Motives for Withdrawal of Participation in Biobanking and Participants' Willingness to Allow Linkages of Their Data ». *European Journal of Human Genetics* 30, n° 3 (mars 2022): 367-77. https://doi.org/10.1038/s41431-021-00997-5.

Budimir, Danijela, Ozren Polašek, Ana Marušić, Ivana Kolčić, Tatijana Zemunik, Vesna Boraska, Ana Jerončić, Mladen Boban, Harry Campbell, et Igor Rudan. « Ethical aspects of human biobanks: a systematic review ». *Croatian Medical Journal* 52, n° 3 (juin 2011): 262-79. https://doi.org/10.3325/cmj.2011.52.262.

Casati, Sara, B Ellul, S Mezinska, Isabelle Budin-Ljösne, Gillian Martin, Olga Tzortzatou, H Bentzen, et al. *Mapping European RECs & ethics review processes for biobank-based research*, 2021. https://doi.org/10.5281/zenodo.4573811.

Casati, Sara, Bridget Ellul, Michaela Th Mayrhofer, Marialuisa Lavitrano, Elodie Caboux, et Zisis Kozlakidis. « Paediatric Biobanking for Health: The Ethical, Legal, and Societal Landscape ». *Frontiers in Public Health* 10 (2022): 917615. https://doi.org/10.3389/fpubh.2022.917615.

Chalmers, Don, Dianne Nicol, Jane Kaye, Jessica Bell, Alastair V. Campbell, Calvin W. L. Ho, Kazuto Kato, et al. « Has the Biobank Bubble Burst? Withstanding the Challenges for Sustainable Biobanking in the Digital Era ». *BMC Medical Ethics* 17, n° 1 (décembre 2016): 39. https://doi.org/10.1186/s12910-016-0124-2.

Coppola, Luigi, Alessandra Cianflone, Anna Maria Grimaldi, Mariarosaria Incoronato, Paolo Bevilacqua, Francesco Messina, Simona Baselice, Andrea Soricelli, Peppino Mirabelli, et
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Marco Salvatore. « Biobanking in Health Care: Evolution and Future Directions ». *Journal of Translational Medicine* 17, n° 1 (22 mai 2019): 172. https://doi.org/10.1186/s12967-019-1922-3.

Dove, Edward S, Yann Joly, et Bartha M Knoppers. « Power to the People: A Wiki-Governance Model for Biobanks ». *Genome Biology* 13, n° 5 (2012): 158. https://doi.org/10.1186/gb-2012-13-5-158.

Fritzsche, Marie-Christine, Kaya Akyüz, Mónica Cano Abadía, Stuart McLennan, Pekka Marttinen, Michaela Th. Mayrhofer, et Alena M. Buyx. « Ethical layering in AI-driven polygenic risk scores—New complexities, new challenges ». *Frontiers in Genetics* 14 (2023). https://www.frontiersin.org/articles/10.3389/fgene.2023.1098439.

Gefenas, Eugenijus, J. Lekstutiene, V. Lukaseviciene, M. Hartlev, M. Mourby, et K.Ó Cathaoir. « Controversies between Regulations of Research Ethics and Protection of Personal Data: Informed Consent at a Cross-Road ». *Medicine, Health Care and Philosophy* 25, n° 1 (mars 2022): 23-30. https://doi.org/10.1007/s11019-021-10060-1.

Gille, Felix, Effy Vayena, et Alessandro Blasimme. « Future-proofing biobanks’ governance ». *European Journal of Human Genetics* 28, n° 8 (août 2020): 989-96. https://doi.org/10.1038/s41431-020-0646-4.

Gramatiuk, Svetlana, et Berthold Huppertz. « Types of Biobanks ». In *Biobanks in Low- and Middle-Income Countries: Relevance, Setup and Management*, édité par Karine Sargsyan, Berthold Huppertz, et Svetlana Gramatiuk, 17-20. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-030-87637-1_3.

Green, Robert C., Jonathan S. Berg, Wayne W. Grody, Sarah S. Kalia, Bruce R. Korf, Christa L. Martin, Amy McGuire, et al. « ACMG Recommendations for Reporting of Incidental Findings in Clinical Exome and Genome Sequencing ». *Genetics in medicine: official journal of the American College of Medical Genetics* 15, n° 7 (juillet 2013): 565-74. https://doi.org/10.1038/gim.2013.73.

Hansson, Mats G, Joakim Dillner, Claus R Bartram, Joyce A Carlson, et Gert Helgesson. « Should Donors Be Allowed to Give Broad Consent to Future Biobank Research? » *The Lancet Oncology* 7, n° 3 (mars 2006): 266-69. https://doi.org/10.1016/S1470-2045(06)70618-0.

Hofman, Paul, Christian Bréchot, Kurt Zatloukal, Georges Dagher, et Bruno Clément. « Public-Private Relationships in Biobanking: A Still Underestimated Key Component of Open Innovation ». *Virchows Archiv* 464, n° 1 (janvier 2014): 3-9. https://doi.org/10.1007/s00428-013-1524-z.

Kaye, Jane. « The Tension Between Data Sharing and the Protection of Privacy in Genomics Research ». *Annual Review of Genomics and Human Genetics* 13, n° 1 (22 septembre 2012): 415-31. https://doi.org/10.1146/annurev-genom-082410-101454.

Lekstutiene, Jurate, Søren Holm, et Eugenijus Gefenas. « Biobanks and Individual Health Related Findings: From an Obstacle to an Incentive ». *Science and Engineering Ethics* 27, n° 4 (11 août 2021): 55. https://doi.org/10.1007/s11948-021-00330-9.

Lin, Jui-Chu, Wesley Wei-Wen Hsiao, et Chien-Te Fan. « Managing “Incidental Findings” in Biobank Research: Recommendations of the Taiwan Biobank ». *Computational and Structural Biotechnology Journal* 17 (1 janvier 2019): 1135-42. https://doi.org/10.1016/j.csbj.2019.07.006.

Mamo, Nicholas, Gillian M. Martin, Maria Desira, Bridget Ellul, et Jean-Paul Ebejer. « Dwarna: A Blockchain Solution for Dynamic Consent in Biobanking ». *European Journal of Human Genetics* 28, n° 5 (mai 2020): 609-26. https://doi.org/10.1038/s41431-019-0560-9.

Miller, David T., Kristy Lee, Noura S. Abul-Husn, Laura M. Amendola, Kyle Brothers, Wendy K. Chung, Michael H. Gollob, et al. « ACMG SF v3.1 List for Reporting of Secondary Findings in Clinical Exome and Genome Sequencing: A Policy Statement of the American College of Medical Genetics and Genomics (ACMG) ». *Genetics in Medicine: Official Journal of the American College of Medical Genetics* 24, n° 7 (juillet 2022): 1407-14. https://doi.org/10.1016/j.gim.2022.04.006.
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Nicol, Dianne, Christine Critchley, Rebekah McWhirter, et Tess Whitton. « Understanding Public Reactions to Commercialization of Biobanks and Use of Biobank Resources ». *Social Science & Medicine* 162 (août 2016): 79-87. https://doi.org/10.1016/j.socscimed.2016.06.028.

Ploug, Thomas, et Søren Holm. « Meta Consent – A Flexible Solution to the Problem of Secondary Use of Health Data ». *Bioethics* 30, n° 9 (novembre 2016): 721-32. https://doi.org/10.1111/bioe.12286.

Salokannel, Marjut. « NordForsk, Stensberggata 25, N-0170 Oslo, Norway Www.Nordforsk.Org Org.Nr. 971 274 255 ». *DATA PROTECTION*, s. d.

Serepkaite, Jurate, Zivile Valuckiene, et Eugenijus Gefenas. « "Mirroring" the Ethics of Biobanking: What Should We Learn from the Analysis of Consent Documents[Corrected]? » *Science and Engineering Ethics* 20, n° 4 (décembre 2014): 1079-93. https://doi.org/10.1007/s11948-013-9481-0.

Shabani, Mahsa, Louise Bezuidenhout, et Pascal Borry. « Attitudes of Research Participants and the General Public towards Genomic Data Sharing: A Systematic Literature Review ». *Expert Review of Molecular Diagnostics* 14, n° 8 (novembre 2014): 1053-65. https://doi.org/10.1586/14737159.2014.961917.

Steinsbekk, Kristin Solum, Bjørn Kåre Myskja, et Berge Solberg. « Broad Consent versus Dynamic Consent in Biobank Research: Is Passive Participation an Ethical Problem? » *European Journal of Human Genetics* 21, n° 9 (septembre 2013): 897-902. https://doi.org/10.1038/ejhg.2012.282.

Strech, Daniel. « Ethical review of biobank research: Should RECs review each release of material from biobanks operating under an already-approved broad consent and data protection model? » *European Journal of Medical Genetics* 58, n° 10 (1 octobre 2015): 545-49. https://doi.org/10.1016/j.ejmg.2015.09.008.

Sudlow, Cathie, John Gallacher, Naomi Allen, Valerie Beral, Paul Burton, John Danesh, Paul Downey, et al. « UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age ». *PLOS Medicine* 12, n° 3 (31 mars 2015): e1001779. https://doi.org/10.1371/journal.pmed.1001779.

Thorogood, Adrian, Gratien Dalpé, et Bartha Maria Knoppers. « Return of Individual Genomic Research Results: Are Laws and Policies Keeping Step? » *European Journal of Human Genetics* 27, n° 4 (avril 2019): 535-46. https://doi.org/10.1038/s41431-018-0311-3.

Ursin, Lars, et Maria Stuifbergen. « Ethics of Dead Participants: Policy Recommendations for Biobank Research ». *Journal of Medical Ethics* 44, n° 10 (1 octobre 2018): 695-99. https://doi.org/10.1136/medethics-2017-104241.

Williams, Hawys, Karen Spencer, Caroline Sanders, David Lund, Edgar A Whitley, Jane Kaye, et William G Dixon. « Dynamic Consent: A Possible Solution to Improve Patient Confidence and Trust in How Electronic Patient Records Are Used in Medical Research ». *JMIR Medical Informatics* 3, n° 1 (13 janvier 2015): e3. https://doi.org/10.2196/medinform.3525.

## Web pages and other references on biobanking

* « 688/2012 Biobank Act finland ». Accessed August 16, 2023. https://www.finlex.fi/en/laki/kaannokset/2012/en20120688.pdf

* « Dwarna ». Accessed August 16, 2023. https://bio.tools/Dwarna.

* « Global Alliance for Genomics and Health, "2021 Policy on Clinically Actionable Genomic Research Results", https://www.ga4gh.org/wp-content/uploads/2021-Policy-on-Clinically-Actionable-Genomic-Research-Results.pdf, 24 June 2021 ». Accessed August 16, 2023. https://www.ga4gh.org/wp-content/uploads/2021-Policy-on-Clinically-Actionable-Genomic-Research-Results.pdf

* « Estonia, Inimgeeniuuringute seadus (Human Genes Research Act), 15 March 2019. ». Accessed August 16, 2023. https://www.riigiteataja.ee/en/eli/508042019001/consolide.

* « Informed Consent—Dynamic, Broad, Tiered & Meta Consent for Biobanking », 11 mars 2020. https://cloudlims.com/informed-consent-dynamic-broad-tiered-and-meta-consent-for-biobanking/.
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Manchester Cancer Research Centre. « Manchester Cancer Research Centre, „MCRC Biobank Access Policy”, ». Accessed August 16, 2023. https://www.mcrc.manchester.ac.uk/.

« Medical University of Graz, Biobank Graz: Innovative biomedical research hub, https://www.medunigraz.at frontend/user upload/themen-pr/imagebroschueren/biobank.pdf ». Accessed August 16, 2023.

« OECD, Guidelines on Human Biobanks and Genetic Research Databases, 2009. https://www.oecd.org/sti/emerging-tech/44054609.pdf ». Accessed August 16, 2023.

Proposal for a Regulation of the European Parliament and of the Council on the European Health Data Space (2022). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52022PC0197.

PANELFIT Project: Report on the governance of data protection ELI in ICT research and innovation », Accessed August 16, 2023. https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5ef2551ff& appId=PPGMS,

« UK biobank, FAQs on applying and accessing UK Biobank data, https://www.ukbiobank.ac.uk/media/zfzoefdy/access_017-faqs-v4-1.pdf ». Accessed August 16, 2023.

« UK biobank, Information leaflet https://www.ukbiobank.ac.uk/media/ei3bagfb/participant_information_leaflet-baseline.pdf ». Accessed August 16, 2023.

« UMC Utrecht, “Biobank Regulations”, https://assets-eu-01.kc-usercontent.com/546dd520-97db-01b7-154d-79bb6d950a2d/ae4726d2-b6eb-407b-a576-db1a5c63a139/Biobank Regulations_UMCUtrecht.pdf ». Accessed August 16, 2023

« VIII-1679 Republic of Lithuania Law on Ethics of Biomedical Research ». Accessed August 16, 2023. https://e-seimas.lrs.lt/portal/legalAct/lt/TAD/83d798e0c21111ec9f0095b4d96fd400?jfwid=spnhjcj0n.

« WMA - The World Medical Association-WMA Declaration of Taipei on Ethical Considerations Regarding Health Databases and Biobanks ». Accessed August 16, 2023. https://www.wma.net/policies-post/wma-declaration-of-taipei-on-ethical-considerations-regarding-health-databases-and-biobanks/.

Genome editing

Abouna, George M. « Ethical Issues in Organ Transplantation ». *Medical Principles and Practice: International Journal of the Kuwait University, Health Science Centre* 12, n° 1 (2003): 54-69. https://doi.org/10.1159/000068158.

Adashi, Eli Y., et I. Glenn Cohen. « Who Will Oversee the Ethical Limits of Human Embryo Research? » *Nature Biotechnology* 40, n° 4 (avril 2022): 463-64. https://doi.org/10.1038/s41587-022-01274-6.

Albrecht, Steffen, Harald König, et Arnold Sauter. « Human Genome Editing. TAB-Fokus », 2021. https://doi.org/10.5445/IR/1000141085.

Asmamaw, Misganaw, et Belay Zawdie. « Mechanism and Applications of CRISPR/Cas-9-Mediated Genome Editing ». *Biologics: Targets & Therapy* 15 (2021): 353-61. https://doi.org/10.2147/BTT.S326422.

Bier, Ethan. « Gene Drives Gaining Speed ». *Nature Reviews Genetics* 23, n° 1 (janvier 2022): 5-22. https://doi.org/10.1038/s41576-021-00386-0.

Birney, Ewan. « Human Genome Editing: Regulations, Risks, and Ethical Considerations ». *EMBL (blog)*, 11 avril 2023. https://www.embl.org/news/lab-matters/human-genome-editing-regulations-risks-and-ethical-considerations/.

Bittlinger, Merlin, Dirk Hoffmann, Anna Sierawska, Marcel Mertz, Axel Schambach, et Daniel Strech. « Risk Assessment in Gene Therapy and Somatic Genome-Editing: An Expert Interview Study ». *Gene and Genome Editing* 3-4 (1 août 2022): 100011. https://doi.org/10.1016/j.ggedit.2022.100011.
Bostrom, Nick, et Rebecca Roache. « Ethical issues in human enhancement ». In *New Waves in Applied Ethics*, édité par J. Ryberg, T. Petersen, et C. Wolf, 120--152. Palgrave-Macmillan, 2007.

——. « Ethical issues in human enhancement ». *New Waves in Applied Ethics*, 1 janvier 2007.

——. « Ethical Issues in Human Enhancement », s. d.

Chan, Sarah. « Regulating Human Genome Editing: Negotiating Ethical Concerns Through Science and Policy ». In *Genome Editing and Engineering: From TALENs, ZFNs and CRISPRs to Molecular Surgery*, édité par Krishnarao Appasani, 454-63. Cambridge: Cambridge University Press, 2018. https://doi.org/10.1017/9781316756300.033.

European Parliament, Directorate-General for Parliamentary Research Services, A Nordberg, et L Antunes. *Genome editing in humans – A survey of law, regulation and governance principles*. European Parliament, 2022. https://doi.org/10.2861/07058.

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