The Ethics Blog

A blog from the Centre for Research Ethics & Bioethics (CRB)

Month: October 2024

Psychological distress: an overlooked issue in immigrants

Map of Europe and Northen Africa on a globe. Photo: Cristina Lue from Unsplash.
29 October, 2024 / / 0 Comments

Psychological distress that ethnic minorities experience is an often overlooked problem. In France, the mental well-being of ethnic minorities, particularly those with North African immigrant backgrounds is an important issue to study. Both first- and second-generation immigrants face unique challenges that may make them more vulnerable to more general mental health issues, and psychological disorders. A fresh report from the European Fundamental Rights Association on being a Muslim in the EU (published on October 24, 2024) sheds some light on issues related to health and racial harassment and violence. The report did not study psychological issues specifically, but it is worth noting that race-related violence has psychological impact for 55 percent of the respondents (p. 21).

Vulnerability is frequently linked to ethnic minority status, leading to recurring experiences of discrimination and difficulties in reconciling cultural identity with a society that often prioritizes assimilation. In this context, assimilation tends to erase or disregard the original cultural heritage in favor of integration into the dominant culture. Such dynamics can lead to feelings of isolation, invalidation, and psychological distress among affected individuals.

Research on the mental health of French populations of North African descent remains largely neglected. In other regions, for example North America, mental health and immigration is much better studied. While the topic of discrimination has been explored in some areas, few studies have focused on the psychological effects of these experiences and the coping strategies adopted by these populations in France. Some research does indicate a rise in discrimination, but lack of comprehensive studies on this issue creates both a scientific and social void, keeping these topics largely invisible.

In other southern European countries such as Italy and Spain, the mental health problems of ethnic minorities are recognized, but do not yet receive the same attention as in North America. In Italy, studies on the mental health of minorities are mainly focused on recent migrants and refugees, not least because of the importance of migratory flows in the Mediterranean. Researchers are mainly interested in the traumas associated with exile and the precarious conditions of migrants, but issues of discrimination or systemic racism are less well explored.

In Spain, there is also research on the mental health of migrants, particularly from Latin America and North Africa. However, the framework remains focused on social integration and economic issues, and less on the dynamics of discrimination and ethnicity. Both countries are beginning to recognize the importance of these issues, but in-depth studies on the impact of racial discrimination on the mental health of ethnic minorities, as in all parts of Europe, are still limited.

One psychological phenomenon that is still underexplored in this context is “racial battle fatigue.” Introduced in the early 2000s by William A. Smith, this concept refers to the emotional and psychological stress accumulated by individuals who repeatedly face racism. It represents the emotional burden that ethnic minorities carry as a result of racial discrimination and societal expectations. This burden can drive individuals to minimize or suppress their own suffering to avoid being perceived as “weak” or “complaining.” These coping mechanisms can exacerbate psychological issues, creating a vicious cycle of untreated distress.

In academic and professional settings, there is often reluctance to openly discuss these challenges. Some individuals may regard these topics as taboo or controversial, limiting the opportunities for open dialogue and scientific advancement. This reflects a broader trend in the mental health field, where the specific needs of ethnic minorities, particularly in terms of tailored psychological care, are not adequately addressed.

If we are going to be able to provide concrete answers to these questions, we need to study this phenomenon and shed some light on the mechanisms underlying the psychological suffering of ethnic minorities. Research on the psychological distress experienced by ethnic minorities could also help develop therapeutic interventions better suited to these populations. A recent French pilot study can lead the way: in Rania Driouach’s sample of people with North African descent, 226 out of a total of 387 participants indicated heightened psychological distress on a transgenerational level. Her study is the first step towards a scientific framework that acknowledges the specific needs of these groups while promoting an inclusive and rigorous therapeutic approach. Perhaps such a framework can help pave the way for a better understanding of the effects of migration on psychological distress across generations, and provide better tools for the (mental) health care providers that provide both first and second line care.

This post is written by Rania Driouach (Nîmes University) and:

Sylvia Martin

Sylvia Martin, Clinical Psychologist and Senior Researcher at the Centre for Research Ethics & Bioethics (CRB)

We transcend disciplinary borders

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Digitization of healthcare requires a national strategy to increase individuals’ ability to handle information digitally

Older lady with light hair and a pinstriped shirt sitting in front of a laptop. Photo by Shvets from Pexels.
22 October, 2024 / / 1 Comment

There is consensus that the digitization of healthcare can make it easier to keep in touch with healthcare and get information that supports individual decision-making about one’s own health. However, the ability to understand and use health information digitally varies. The promising digitization therefore risks creating unequal care and health.

In this context, one usually speaks of digital health literacy. The term refers to the ability to retrieve, understand and use health information digitally to maintain or improve one’s health. This ability varies not only between individuals, but also within the same individual. Illness can, for example, reduce the ability to use a computer or a smartphone to maintain contact with healthcare and to understand and manage health information digitally. Your digital health literacy is dependent on your health.

How do Swedish policy makers think about the need for strategies to increase digital health literacy in Sweden? An article with Karin Schölin Bywall as the main author examines the question. Material was collected during three recorded focus group discussions (or workshops) with a total of 10 participants. The study is part of a European project to increase digital health literacy in Europe. What did Swedish decision-makers think of the need for a national strategy?

The participants in the study said that the issue of digital health literacy was not as much on the agenda in Sweden as in many other countries in Europe and that governmental agencies have limited knowledge of the problem. Digital services in healthcare also usually require that you identify yourself digitally, but a large group of adults in Sweden lack e-identification. The need for a national strategy is therefore great.

Participants further discussed how digital health literacy manifests itself in individuals’ ability to find the right website and reliable information on the internet. People with lower digital health literacy may not be able to identify appropriate keywords or may have difficulty assessing the credibility of the information source. The problem is not lessened by the fact that algorithms control where we end up when we search for information. Often the algorithms make companies more visible than government organizations.

The policy makers in the study also identified specific groups that are at risk of digital exclusion (digital divide) and that need different types of support. Among others, they mentioned people with intellectual disabilities and young people who do not sufficiently master source criticism (even though they are skilled users of the internet and various apps). Specific measures to counteract the digital divide in healthcare were discussed, such as regular mailings with information about good websites, adaptation of website content for people with special needs, and teaching in source criticism. It was also emphasized that individuals may have different combinations of conditions that affect the ability to manage health information digitally in different ways, and that a strategy to increase digital health literacy must therefore be nuanced.

In summary, the study emphasizes that the need for a national strategy for increased digital health literacy is great. While digital technologies have huge potential to improve public health, they also risk reinforcing already existing inequalities, the authors conclude. Read the study here: Calling for allied efforts to strengthen digital health literacy in Sweden: perspectives of policy makers.

Something that struck me was that the policy makers in the study, as far as I could see, did not emphasize the growing group of elderly people in the population. Elderly people may have a particularly broad combination of conditions that affect digital health literacy in many different ways. In addition, the elderly’s ability to handle information digitally not only varies from day to day, but the ability can be expected to have an increasingly steady tendency to deteriorate. Probably at the same rate as the need to use the ability increases.

Pär Segerdahl

Written by…

Pär Segerdahl, Associate Professor at the Centre for Research Ethics & Bioethics and editor of the Ethics Blog.

Bywall, K.S., Norgren, T., Avagnina, B. et al. Calling for allied efforts to strengthen digital health literacy in Sweden: perspectives of policy makers. BMC Public Health 24, 2666 (2024). https://doi.org/10.1186/s12889-024-20174-9

This post in Swedish

Ethics needs empirical input

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Debate on responsibility and academic authorship

Dictionary "Collaborate", Image by Dianne Hope from Pixabay
8 October, 2024 / / 1 Comment

Who can be listed as an author of a research paper? There seems to be some confusion about the so-called Vancouver rules for academic authorship, which serve as publication ethical guidelines in primarily medicine and the natural sciences (but sometimes also in the humanities and social sciences). According to these rules, an academic author must have contributed intellectually to the study, participated in the writing process, and approved the final version of the paper. However, the deepest confusion seems to concern the fourth rule, which requires that an academic author must take responsibility for the accuracy and integrity of the published research. The confusion is not lessened by the fact that artificial intelligences such as ChatGPT have started to be used in the research and writing process. Researchers sometimes ask the AI ​​to generate objections to the researchers’ reasoning, which of course can make a significant contribution to the research process. The AI ​​can also generate text that contributes to the process of writing the article. Should such an AI count as a co-author?

No, says the Committee on Publication Ethics (COPE) with reference to the last requirement of the Vancouver rules: an AI cannot be an author of an academic publication, because it cannot take responsibility for the published research. The committee’s dismissal of AI authorship has sparked a small but instructive debate in the Journal of Medical Ethics. The first to write was Neil Levy who argued that responsibility (for entire studies) is not a reasonable requirement for academic authorship, and that an AI could already count as an author (if the requirement is dropped). This prompted a response from Gert Helgesson and William Bülow, who argued that responsibility (realistically interpreted) is a reasonable requirement, and that an AI cannot be counted as an author, as it cannot take responsibility.

What is this debate about? What does the rule that gave rise to it say? It states that, to be considered an author of a scientific article, you must agree to be accountable for all aspects of the work. You must ensure that questions about the accuracy and integrity of the published research are satisfactorily investigated and resolved. In short, an academic writer must be able to answer for the work. According to Neil Levy, this requirement is too strong. In medicine and the natural sciences, it is often the case that almost none of the researchers listed as co-authors can answer for the entire published study. The collaborations can be huge and the researchers are specialists in their own narrow fields. They lack the overview and competence to assess and answer for the study in its entirety. In many cases, not even the first author can do this, says Neil Levy. If we do not want to make it almost impossible to be listed as an author in many scientific disciplines, responsibility must be abolished as a requirement for authorship, he argues. Then we have to accept that AI can already today be counted as co-author of many scientific studies, if the AI made a significant intellectual contribution to the research.

However, Neil Levy opens up for a third possibility. The responsibility criterion could be reinterpreted so that it can be fulfilled by the researchers who today are usually listed as authors. What is the alternative interpretation? A researcher who has made a significant intellectual contribution to a research article must, in order to be listed as an author, accept responsibility for their “local” contribution to the study, not for the study as a whole. An AI cannot, according to this interpretation, count as an academic author, because it cannot answer or be held responsible even for its “local” contribution to the study.

According to Gert Helgesson and William Bülow, this third possibility is the obviously correct interpretation of the fourth Vancouver rule. The reasonable interpretation, they argue, is that anyone listed as an author of an academic publication has a responsibility to facilitate an investigation, if irregularities or mistakes can be suspected in the study. Not only after the study is published, but throughout the research process. However, no one can be held responsible for an entire study, sometimes not even the first author. You can only be held responsible for your own contribution, for the part of the study that you have insight into and competence to judge. However, if you suspect irregularities in other parts of the study, then as an academic author you still have a responsibility to call attention to this, and to act so that the suspicions are investigated if they cannot be immediately dismissed.

The confusion about the fourth criterion of academic authorship is natural, it is actually not that easy to understand, and should therefore be specified. The debate in the Journal of Medical Ethics provides an instructive picture of how differently the criterion can be interpreted, and it can thus motivate proposals on how the criterion should be specified. You can read Neil Levy’s article here: Responsibility is not required for authorship. The response from Gert Helgesson and William Bülow can be found here: Responsibility is an adequate requirement for authorship: a reply to Levy.

Personally, I want to ask whether an AI, which cannot take responsibility for research work, can be said to make significant intellectual contributions to scientific studies. In academia, we are expected to be open to criticism from others and not least from ourselves. We are expected to be able to critically assess our ideas, theories, and methods: judge whether objections are valid and then defend ourselves or change our minds. This is an important part of the doctoral education and the research seminar. We cannot therefore be said to contribute intellectually to research, I suppose, if we do not have the ability to self-critically assess the accuracy of our contributions. ChatGPT can therefore hardly be said to make significant intellectual contributions to research, I am inclined to say. Not even when it generates self-critical or self-defending text on the basis of statistical calculations in huge language databases. It is the researchers who judge whether generated text inspires good reasons to either change their mind or defend themselves. If so, it would be a misunderstanding to acknowledge the contribution of a ChatGPT in a research paper, as is usually done with research colleagues who contributed intellectually to the study without meeting the other requirements for academic authorship. Rather, the authors of the study should indicate how the ChatGPT was used as a tool in the study, similar to how they describe the use of other tools and methods. How should this be done? In the debate, it is argued that this also needs to be specified.

Pär Segerdahl

Written by…

Pär Segerdahl, Associate Professor at the Centre for Research Ethics & Bioethics and editor of the Ethics Blog.

Levy N. Responsibility is not required for authorship. Journal of Medical Ethics. Published Online First: 15 May 2024. doi: 10.1136/jme-2024-109912

Helgesson G, Bülow W. Responsibility is an adequate requirement for authorship: a reply to Levy. Journal of Medical Ethics. Published Online First: 04 July 2024. doi: 10.1136/jme-2024-110245

This post in Swedish

We participate in debates

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Why should we try to build conscious AI?

A visual interpretation of Artificial Intelligence (AI).
1 October, 2024 / / 1 Comment

In a recent post on this blog I summarized the main points of a pre-print where I analyzed the prospect of artificial consciousness from an evolutionary perspective. I took the brain and its architecture as a benchmark for addressing the technical feasibility and conceptual plausibility of engineering consciousness in artificial intelligence systems. The pre-print has been accepted and it is now available as a peer-reviewed article online.

In this post I want to focus on one particular point that I analyzed in the paper, and which I think is not always adequately accounted for in the debate about AI consciousness: what are the benefits of pursuing artificial consciousness in the first place, for science and for society at large? Why should we attempt to engineer subjective experience in AI systems? What can we realistically expect from such an endeavour?

There are several possible answers to these questions. At the epistemological level (with reference to what we can know) it is possible that developing artificial systems that replicate some features of our conscious experience could enable us to better understand biological consciousness, through similarities as well as through differences. At the technical level (with reference to what we can do) it is possible that the development of artificial consciousness would be a game-changer in AI, for instance giving AI the capacity for intentionality and theory of mind, and for anticipating the consequences not only of human decisions, but also of its own “actions.” At the societal and ethical level (with reference to our co-existence with others and to what is good and bad for us) especially the latter capabilities (intentionality, theory of mind, and anticipation) could arguably help AI to better inform humans about potential negative impacts of its functioning and use on society, and to help avoid them while favouring positive impacts. Of course, on the negative side, as showed by human history, both intentionality and theory of mind may be used by the AI for negative purposes, for instance for favouring the AI’s own interests or the interests of the limited groups that control it. Human intentionality has not always favoured out-group individuals or species, or indeed the planet as a whole. This point connects to one of the most debated issues in AI ethics, the so-called AI alignment problem: how can we be sure that AI systems conform to human values? How can we make AI aligned with our own interests? And whose values and interests should we take as reference? Cultural diversity is an important and challenging factor to take into account in these reflections.

I think there is also a question that precedes that of AI value alignment: can AI really have values? In other words, is the capacity for evaluation that possibly drives the elaboration of values in AI the same as in humans? And is AI capable of evaluating its own values, including its ethical values, a reflective process that drives the self-critical elaboration of values in humans, making us evaluative subjects? In fact, the capacity for evaluation (which may be defined as the sensitivity to reward signals and the ability to discriminate between good and bad things in the world on the basis of specific needs, motivations, and goals) is a defining feature of biological organisms, namely of the brain. AI may be programmed to discriminate between what humans consider to be good and bad things in the world, and it is also conceivable that AI will be less dependent on humans in applying this distinction. However, this does not entail that it “evaluates” in the sense that it autonomously performs an evaluation and subjectively experiences its evaluation.

It is possible that an AI system may approximate the diversity of cognitive processes that the brain has access to, for instance the processing of various sensory modalities, while AI remains unable to incorporate the values attributed to the processed information and to its representation, as the human brain can do. In other words, to date AI remains devoid of any experiential content, and for this reason, for the time being, AI is different from the human brain because of its inability to attribute experiential value to information. This is the fundamental reason why present AI systems lack subjective experience. If we want to refer to needs (which are a prerequisite for the capacity for evaluation), current AI appears limited to epistemic needs, without access to, for example, moral and aesthetic needs. Therefore, the values that AI has at least so far been able to develop or be sensible to are limited to the epistemic level, while morality and aesthetics are beyond our present technological capabilities. I do not deny that overcoming this limitation may be a matter of further technological progress, but for the time being we should carefully consider this limitation in our reflections about whether it is wise to strive for conscious AI systems. If the form of consciousness that we can realistically aspire to engineer today is limited to the cognitive dimension, without any sensibility to ethical deliberation and aesthetic appreciation, I am afraid that the risk of misusing or exploiting it for selfish purposes is quite high.

One could object that an AI system limited to epistemic values is not really conscious (at least not in a fully human sense). However, the fact remains that its capacity to interact with the world to achieve the goals it has been programmed to achieve would be greatly enhanced if it had this cognitive form of consciousness. This increases our responsibility to hypothetically consider whether conscious AI, even if limited and much more rudimentary than human consciousness, may be for the better or for the worse.

Written by…

Michele Farisco, Postdoc Researcher at Centre for Research Ethics & Bioethics, working in the EU Flagship Human Brain Project.

Michele Farisco, Kathinka Evers, Jean-Pierre Changeux. Is artificial consciousness achievable? Lessons from the human brain. Neural Networks, Volume 180, 2024. https://doi.org/10.1016/j.neunet.2024.106714

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