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

Tag: Artificial Intelligence (Page 1 of 3)

Objects that behave humanly

Many forms of artificial intelligence could be considered objects that behave humanly. However, it does not take much for us humans to personify non-living objects. We get angry at the car that does not start or the weather that does not let us have a picnic, as if they were against us. Children spontaneously personify simple toys and can describe the relationship between geometric shapes as, “the small circle is trying to escape from the big triangle.”

We are increasingly encountering artificial intelligence designed to give a human impression, for example in the form of chatbots for customer service when shopping online. Such AI can even be equipped with personal traits, a persona that becomes an important part of the customer experience. The chatbot can suggest even more products for you and effectively generate additional sales based on the data collected about you. No wonder the interest in developing human-like AI is huge. Part of it has to do with user-friendliness, of course, but at the same time, an AI that you find personally attractive will grab your attention. You might even like the chatbot or feel it would be impolite to turn it off. During the time that the chatbot has your attention, you are exposed to increasingly customized advertising and receive more and more package offers.

You can read about this and much more in an article about human relationships with AI designed to give a human impression: Human/AI relationships: challenges, downsides, and impacts on human/human relationships. The authors discuss a large number of examples of such AI, ranging from the chatbots above to care robots and AI that offers psychotherapy, or AI that people chat with to combat loneliness. The opportunities are great, but so are the challenges and possible drawbacks, which the article highlights.

Perhaps particularly interesting is the insight into how effectively AI can create confusion by exposing us to objects equipped with human response patterns. Our natural tendency to anthropomorphize non-human things meets high-tech efforts to produce objects that are engineered to behave humanly. Here it is no longer about imaginatively projecting social relations onto non-human objects, as in the geometric example above. In interaction with AI objects, we react to subtle social cues that the objects are equipped with. We may even feel a moral responsibility for such AI and grieve when companies terminate or modify it.

The authors urge caution so that we do not overinterpret AI objects as persons. At the same time, they warn of the risk that, by avoiding empathic responses, we become less sensitive to real people in need. Truly confusing!

Pär Segerdahl

Written by…

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

Zimmerman, A., Janhonen, J. & Beer, E. Human/AI relationships: challenges, downsides, and impacts on human/human relationships. AI Ethics (2023). https://doi.org/10.1007/s43681-023-00348-8

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A strategy for a balanced discussion of conscious AI

Science and technology advance so rapidly that it is hard to keep up with them. This is true not only for the general public, but also for the scientists themselves and for scholars from fields like ethics and regulation, who find it increasingly difficult to predict what will come next. Today AI is among the most advanced scientific endeavors, raising both significant expectations and more or less exaggerated worries. This is mainly due to the fact that AI is a concept so emotionally, socially, and politically charged as to make a balanced evaluation very difficult. It is even more so when capacities and features that are considered almost uniquely human, or at least shared with a limited number of other animals, are attributed to AI. This is the case with consciousness.

Recently, there has been a lively debate about the possibility of developing conscious AI. What are the reasons for this great interest? I think it has to do with the mentioned rapid advances in science and technology, as well as new intersections between different disciplines. Specifically, I think that three factors play an important role: the significant advancement in understanding the cerebral bases of conscious perception, the impressive achievements of AI technologies, and the increasing interaction between neuroscience and AI. The latter factor, in particular, resulted in so-called brain-inspired AI, a form of AI that is explicitly modeled on our brains.

This growing interest in conscious AI cannot ignore certain risks of varying relevance, including theoretical, practical, and ethical relevance. Theoretically, there is not a shared, overarching theory or definition of consciousness. Discussions about what consciousness is, what the criteria for a good scientific theory should be, and how to compare the various proposed theories of consciousness are still open and difficult to resolve.

Practically, the challenge is how to identify conscious systems. In other words, what are the indicators that reliably indicate whether a system, either biological or artificial, is conscious?

Finally, at the ethical level several issues arise. Here the discussion is very lively, with some calling for an international moratorium on all attempts to build artificial consciousness. This extreme position is motivated by the need for avoiding any form of suffering, including possibly undetectable artificial forms of suffering. Others question the very reason for working towards conscious AI: why should we open another, likely riskier box, when society cannot really handle the impact of AI, as illustrated by Large Language Models? For instance, chatbots like ChatGPT show an impressive capacity to interact with humans through natural language, which creates a strong feeling that these AI systems have features like consciousness, intentionality, and agency, among others. This attribution of human qualities to AI eventually impacts the way we think about it, including how much weight and value we give to the answers that these chatbots provide.

The two arguments above illustrate possible ethical concerns that can be raised against the development of conscious artificial systems. Yet are the concerns justified? In a recent chapter, I propose a change in the underlying approach to the issue of artificial consciousness. This is to avoid the risk of vague and not sufficiently multidimensional analyses. My point is that consciousness is not a unified, abstract entity, but rather like a prism, which includes different dimensions that could possibly have different levels. Based on a multidimensional view of consciousness, in a previous paper I contributed a list of indicators that are relevant also for identifying consciousness in artificial systems. In principle, it is possible that AI can manifest some dimensions of consciousness (for instance, those related to sophisticated cognitive tasks) while lacking others (for instance, those related to emotional or social tasks). In this way, the indicators provide not only a practical tool for identifying conscious systems, but also an ethical tool to make the discussion on possible conscious AI more balanced and realistic. The question whether some AI is conscious or not cannot be considered a yes/no question: there are several nuances that make the answer more complex.

Indeed, the indicators mentioned above are affected by a number of limitations, including the fact that they are developed for humans and animals, not specifically for AI. For this reason, research is still ongoing on how to adapt these indicators or possibly develop new indicators specific for AI. If you want to read more, you can find my chapter here: The ethical implications of indicators of consciousness in artificial systems.

Written by…

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

Michele Farisco. The ethical implications of indicators of consciousness in artificial systems. Developments in Neuroethics and Bioethics. Available online 1 March 2024. https://doi.org/10.1016/bs.dnb.2024.02.009

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Women on AI-assisted mammography

The use of AI tools in healthcare has become a recurring theme on this blog. So far, the posts have mainly been about mobile and online apps for use by patients and the general public. Today, the theme is more advanced AI tools which are used professionally by healthcare staff.

Within the Swedish program for breast cancer screening, radiologists interpret large amounts of X-ray images to detect breast cancer at an early stage. The workload is great and most of the time the images show no signs of cancer or pre-cancers. Today, AI tools are being tested that could improve mammography in several ways. AI could be used as an assisting resource for the radiologists to detect additional tumors. It could also be used as an independent reader of images to relieve radiologists, as well as to support assessments of which patients should receive care more immediately.

For AI-assisted mammography to work, not only the technology needs to be developed. Researchers also need to investigate how women think about AI-assisted mammography. How do they perceive AI-assisted breast cancer screening? Four researchers, including Jennifer Viberg Johansson and Åsa Grauman at CRB, interviewed sixteen women who underwent mammography at a Swedish hospital where an AI tool was tested as a third reviewer of the X-ray images, along with the two radiologists.

Several of the interviewees emphasized that AI is only a tool: AI cannot replace the doctor because humans have abilities beyond image recognition, such as intuition, empathy and holistic thinking. Another finding was that some of the interviewees had a greater tolerance for human error than if the AI tool failed, which was considered unacceptable. Some argued that if the AI tool makes a mistake, the mistake will be repeated systematically, while human errors are occasional. Some believed that the responsibility when the technology fails lies with the humans and not with the technology.

Personally, I cannot help but speculate that the sharp distinction between human error, which is easier to reconcile with, and unacceptably failing technology, is connected to the fact that we can say of humans who fail: “After all, the radiologists surely did their best.” On the other hand, we hardly say about failing AI: “After all, the technology surely did its best.” Technology does not become subject to certain forms of conciliatory considerations.

The authors themselves emphasize that the participants in the study saw AI as a valuable tool in mammography, but held that the tool cannot replace humans in the process. The authors also emphasize that the interviewees preferred that the AI tool identify possible tumors with high sensitivity, even if this leads to many false positive results and thus to unnecessary worry and fear. In order for patients to understand AI-assisted healthcare, effective communication efforts are required, the authors conclude.

It is difficult to summarize the rich material from interview studies. For more results, read the study here: Women’s perceptions and attitudes towards the use of AI in mammography in Sweden: a qualitative interview study.

Pär Segerdahl

Written by…

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

Viberg Johansson J, Dembrower K, Strand F, et al. Women’s perceptions and attitudes towards the use of AI in mammography in Sweden: a qualitative interview study. BMJ Open 2024;14:e084014. doi: 10.1136/bmjopen-2024-084014

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Approaching future issues

Using artificial intelligence with academic integrity

AI tools can both transform and produce content such as texts, images and music. The tools are also increasingly available as online services. One example is the ChatGPT tool, which you can ask questions and get well-informed, logically reasoned answers from. Answers that the tool can correct if you point out errors and ambiguities. You can interact with the tool almost as if you were conversing with a human.

Such a tool can of course be very useful. It can help you solve problems and find relevant information. I venture to guess that the response from the tool can also stimulate creativity and open the mind to unexpected possibilities, just as conversations with people tend to do. However, like all technology, these tools can also be abused and students have already used ChatGPT to complete their assignments.

The challenge in education and research is thus to learn to use these AI tools with academic integrity. Using AI tools is not automatically cheating. Seven participants in a European network for academic integrity (ENAI), including Sonja Bjelobaba at CRB, write about the challenge in an editorial in International Journal for Educational Integrity. Above all, the authors summarize tentative recommendations from ENAI on the ethical use of AI in academia.

An overarching aim in the recommendations is to integrate recommendations on AI with other related recommendations on academic integrity. Thus, all persons, sources and tools that influenced ideas or generated content must be clearly acknowledged – including the use of AI tools. Appropriate use of tools that affect the form of the text (such as proofreading tools, spelling checkers and thesaurus) are generally acceptable. Furthermore, an AI tool cannot be listed as a co-author in a publication, as the tool cannot take responsibility for the content.

The recommendations also emphasize the importance of educational efforts on the ethical use of AI tools. Read the recommendations in their entirety here: ENAI Recommendations on the ethical use of Artificial Intelligence in Education.

Pär Segerdahl

Written by…

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

Foltynek, T., Bjelobaba, S., Glendinning, I. et al. ENAI Recommendations on the ethical use of Artificial Intelligence in Education. International Journal for Educational Integrity 19, 12 (2023). https://doi.org/10.1007/s40979-023-00133-4

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A new project will explore the prospect of artificial awareness

The neuroethics group at CRB has just started its work as part of a new European research project about artificial awareness. The project is called “Counterfactual Assessment and Valuation for Awareness Architecture” (CAVAA), and is funded for a duration of four years. The consortium is composed of 10 institutions, coordinated by the Radboud University in the Netherlands.

The goal of CAVAA is “to realize a theory of awareness instantiated as an integrated computational architecture…, to explain awareness in biological systems and engineer it in technological ones.” Different specific objectives derive from this general goal. First, CAVAA has a robust theoretical component: it relies on a strong theoretical framework. Conceptual reflection on awareness, including its definition and the identification of features that allow its attribution to either biological organisms or artificial systems, is an explicit task of the project. Second, CAVAA is interested in exploring the connection between awareness in biological organisms and its possible replication in artificial systems. The project thus gives much attention to the connection between neuroscience and AI. Third, against this background, CAVAA aims at replicating awareness in artificial settings. Importantly, the project also has a clear ethical responsibility, more specifically about anticipating the potential societal and ethical impact of aware artificial systems.

There are several reasons why a scientific project with a strong engineering and computer science component also has philosophers on board. We are asked to contribute to developing a strong and consistent theoretical account of awareness, including the conceptual conceivability and the technical feasibility of its artificial replication. This is not straightforward, not only because there are many content-related challenges, but also because there are logical traps to avoid. For instance, we should avoid the temptation to validate an empirical statement on the basis of our own theory: this would possibly be tautological or circular.

In addition to this theoretical contribution, we will also collaborate in identifying indicators of awareness and benchmarks for validating the cognitive architecture that will be developed. Finally, we will collaborate in the ethical analysis concerning potential future scenarios related to artificial awareness, such as the possibility of developing artificial moral agents or the need to extend moral rights also to artificial systems.

In the end, there are several potential contributions that philosophy can provide to the scientific attempt to replicate biological awareness in artificial systems. Part of this possible collaboration is the fundamental and provoking question: why should we try to develop artificial awareness at all? What is the expected benefit, should we succeed? This is definitely an open question, with possible arguments for and against attempting such a grand accomplishment.

There is also another question of equal importance, which may justify the effort to identify the necessary and sufficient conditions for artificial systems to become aware, and how to recognize them as such. What if we will inadvertently create (or worse: have already created) forms of artificial awareness, but do not recognize this and treat them as if they were unaware? Such scenarios also confront us with serious ethical issues. So, regardless of our background beliefs about artificial awareness, it is worth investing in thinking about it.

Stay tuned to hear more from CAVAA!

Written by…

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

Part of international collaborations

AI narratives from the Global North

The way we develop, adopt, regulate and accept artificial intelligence is embedded in our societies and cultures. Our narratives about intelligent machines take on a flavour of the art, literature and imaginations of the people who live today, and of those that came before us. But some of us are missing from the stories that are told about thinking machines. A recent paper about forgotten African AI narratives and the future of AI in Africa shines a light on some of the missing narratives.

In the paper, Damian Eke and George Ogoh point to the fact that how artificial intelligence is developed, adopted, regulated and accepted is hugely influenced by socio-cultural, ethical, political, media and historical narratives. But most of the stories we tell about intelligent machines are imagined and conceptualised in the Global North. The paper begs the question whether it is a problem? And if so, in what way? When machine narratives put the emphasis on technology neutrality, that becomes a problem that goes beyond AI.

What happens when Global North narratives set the agenda for research and innovation also in the Global South, and what happens more specifically to the agenda for artificial intelligence? The impact is difficult to quantify. But when historical, philosophical, socio-cultural and political narratives from Africa are missing, we need to understand why and what it might imply. Damian Eke & George Ogoh provide a list of reasons for why this is important. One is concerns about the state of STEM education (science, technology, engineering and mathematics) in many African countries. Another reason is the well-documented issue of epistemic injustice: unfair discrimination against people because of prejudices about their knowledge. The dominance of Global North narratives could lead to devaluing the expertise of Africans in the tech community. This brings us to the point of the argument, which is that African socio-cultural, ethical and political contexts and narratives are absent from the global debate about responsible AI.

The paper makes the case for including African AI narratives not only into the research and development of artificial intelligence, but also into the ethics and governance of technology more broadly. Such inclusion would help counter epistemic injustice. If we fail to include narratives from the South into the AI discourse, the development can never be truly global. Moreover, excluding African AI narratives will limit our understanding of how different cultures in Africa conceptualise AI, and we miss an important perspective on how people across the world perceive the risks and benefits of machine learning and AI powered technology. Nor will we understand the many ways in which stories, art, literature and imaginations globally shape those perceptions.

If we want to develop an “AI for good”, it needs to be good for Africa and other parts of the Global South. According to Damian Eke and George Ogoh, it is possible to create a more meaningful and responsible narrative about AI. That requires that we identify and promote people-centred narratives. And anchor AI ethics for Africa in African ethical principles, like ubuntu. But the key for African countries to participate in the AI landscape is a greater focus on STEM education and research. The authors end their paper with a call to improve the diversity of voices in the global discourse about AI. Culturally sensitive and inclusive AI applications would benefit us all, for epistemic injustice is not just a geographical problem. Our view of whose knowledge has value is powered by a broad variety of forms of prejudice.

Damian Eke and George Ogoh are both actively contributing to the Human Brain Project’s work on responsible research and innovation. The Human Brain Project is a European Flagship project providing in-depth understanding of the complex structure and function of the human brain, using interdisciplinary approaches.

Do you want to learn more? Read the article here: Forgotten African AI Narratives and the future of AI in Africa.

Josepine Fernow

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Josepine Fernow, science communications project manager and coordinator at the Centre for Research Ethics & Bioethics, develops communications strategy for European research projects

Eke D, Ogoh G, Forgotten African AI Narratives and the future of AI in Africa, International Review of Information Ethics, 2022;31(08).

We want to be just

Artificial intelligence: augmenting intelligence in humans or creating human intelligence in machines?

Sometimes you read articles at the intersection of philosophy and science that contain really exciting visionary thoughts, which are at the same time difficult to really understand and assess. The technical elaboration of the thoughts grows as you read, and in the end you do not know if you are capable of thinking independently about the ideas or if they are about new scientific findings and trends that you lack the expertise to judge.

Today I dare to recommend the reading of such an article. The post must, of course, be short. But the fundamental ideas in the article are so interesting that I hope some readers of this post will also become readers of the article and make a serious attempt to understand it.

What is the article about? It is about an alternative approach to the highest aims and claims in artificial intelligence. Instead of trying to create machines that can do what humans can do, machines with higher-level capacities such as consciousness and morality, the article focuses on the possibility of creating machines that augment the intelligence of already conscious, morally thinking humans. However, this idea is not entirely new. It has existed for over half a century in, for example, cybernetics. So what is new in the article?

Something I myself was struck by was the compassionate voice in the article, which is otherwise not prominent in the AI ​​literature. The article focuses not on creating super-smart problem solvers, but on strengthening our connections with each other and with the world in which we live. The examples that are given in the article are about better moral considerations for people far away, better predictions of natural disasters in a complex climate, and about restoring social contacts in people suffering from depression or schizophrenia.

But perhaps the most original idea in the article is the suggestion that the development of these human self-augmenting machines would draw inspiration from how the brain already maintains contact with its environment. Here one should keep in mind that we are dealing with mathematical models of the brain and with innovative ways of thinking about how the brain interacts with the environment.

It is tempting to see the brain as an isolated organ. But the brain, via the senses and nerve-paths, is in constant dynamic exchange with the body and the world. You would not experience the world if the world did not constantly make new imprints in your brain and you constantly acted on those imprints. This intense interactivity on multiple levels and time scales aims to maintain a stable and comprehensible contact with a surrounding world. The way of thinking in the article reminds me of the concept of a “digital twin,” which I previously blogged about. But here it is the brain that appears to be a neural twin of the world. The brain resembles a continuously updated neural mirror image of the world, which it simultaneously continuously changes.

Here, however, I find it difficult to properly understand and assess the thoughts in the article, especially regarding the mathematical model that is supposed to describe the “adaptive dynamics” of the brain. But as I understand it, the article suggests the possibility of recreating a similar dynamic in intelligent machines, which could enhance our ability to see complex patterns in our environment and be in contact with each other. A little poetically, one could perhaps say that it is about strengthening our neural twinship with the world. A kind of neural-digital twinship with the environment? A digitally augmented neural twinship with the world?

I dare not say more here about the visionary article. Maybe I have already taken too many poetic liberties? I hope that I have at least managed to make you interested to read the article and to asses it for yourself: Augmenting Human Selves Through Artificial Agents – Lessons From the Brain.

Well, maybe one concluding remark. I mentioned the difficulty of sometimes understanding and assessing visionary ideas that are formulated at the intersection of philosophy and science. Is not that difficulty itself an example of how our contact with the world can sometimes weaken? However, I do not know if I would have been helped by digital intelligence augmentation that quickly took me through the philosophical difficulties that can arise during reading. Some questions seem to essentially require time, that you stop and think!

Giving yourself time to think is a natural way to deepen your contact with reality, known by philosophers for millennia.

Pär Segerdahl

Written by…

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

Northoff G, Fraser M, Griffiths J, Pinotsis DA, Panangaden P, Moran R and Friston K (2022) Augmenting Human Selves Through Artificial Agents – Lessons From the Brain. Front. Comput. Neurosci. 16:892354. doi: 10.3389/fncom.2022.892354

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How can neuroethics and AI ethics join their forces?

As I already wrote on this blog, there has been an explosion of AI in recent years. AI affects so many aspects of our lives that it is virtually impossible to avoid interacting with it. Since AI has such an impact, it must be examined from an ethical point of view, for the very basic reason that it can be developed and/or used for both good and evil.

In fact, AI ethics is becoming increasingly popular nowadays. As it is a fairly young discipline, even though it has roots in, for example, digital and computer ethics, the question is open about its status and methodology. To simplify the debate, the main trend is to conceive AI ethics in terms of practical ethics, for example, with a focus on the impact of AI on traditional practices in education, work, healthcare, entertainment, among others. In addition to this practically oriented analysis, there is also attention to the impact of AI on the way we understand our society and ourselves as part of it.

In this debate about the identity of AI ethics, the need for a closer collaboration with neuroethics has been briefly pointed out, but so far no systematic reflection has been made on this need. In a new article, I propose, together with Kathinka Evers and Arleen Salles, an argument to justify the need for closer collaboration between neuroethics and AI ethics. In a nutshell, even though they both have specific identities and their topics do not completely overlap, we argue that neuroethics can complement AI ethics for both content-related and methodological reasons.

Some of the issues raised by AI are related to fundamental questions that neuroethics has explored since its inception. Think, for example, of topics such as intelligence: what does it mean to be intelligent? In what sense can a machine be qualified as an intelligent agent? Could this be a misleading use of words? And what ethical implications can this linguistic habit have, for example, on how we attribute responsibility to machines and to humans? Another issue that is increasingly gaining ground in AI ethics literature, as I wrote on this blog, is the conceivability and the possibility of artificial consciousness. Neuroethics has worked extensively on both intelligence and consciousness, combining applied and fundamental analyses, which can serve as a source of relevant information for AI ethics.

In addition to the above content-related reasons, neuroethics can also provide AI ethics with a methodological model. To illustrate, the kind of conceptual clarification performed in fundamental neuroethics can enrich the identification and assessment of the practical ethical issues raised by AI. More specifically, neuroethics can provide a three-step model of analysis to AI ethics: 1. Conceptual relevance: can specific notions, such as autonomy, be attributed to AI? 2. Ethical relevance: are these specific notions ethically salient (i.e., do they require ethical evaluation)? 3. Ethical value: what is the ethical significance and the related normative implications of these specific notions?

This three-step approach is a promising methodology for ethical reflection about AI which avoids the trap anthropocentric self-projection, a risk that actually affects both the philosophical reflection on AI and its technical development.

In this way, neuroethics can contribute to avoiding both hypes and disproportionate worries about AI, which are among the biggest challenges facing AI ethics today.

Written by…

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

Farisco, M., Evers, K. & Salles, A. On the Contribution of Neuroethics to the Ethics and Regulation of Artificial Intelligence. Neuroethics 15, 4 (2022). https://doi.org/10.1007/s12152-022-09484-0

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Images of good and evil artificial intelligence

As Michele Farisco has pointed out on this blog, artificial intelligence (AI) often serves as a projection screen for our self-images as human beings. Sometimes also as a projection screen for our images of good and evil, as you will soon see.

In AI and robotics, autonomy is often sought in the sense that the artificial intelligence should be able to perform its tasks optimally without human guidance. Like a self-driving car, which safely takes you to your destination without you having to steer, accelerate or brake. Another form of autonomy that is often sought is that artificial intelligence should be self-learning and thus be able to improve itself and become more powerful without human guidance.

Philosophers have discussed whether AI can be autonomous even in another sense, which is associated with human reason. According to this picture, we can as autonomous human beings examine our final goals in life and revise them if we deem that new knowledge about the world motivates it. Some philosophers believe that AI cannot do this, because the final goal, or utility function, would make it irrational to change the goal. The goal is fixed. The idea of such stubbornly goal-oriented AI can evoke worrying images of evil AI running amok among us. But the idea can also evoke reassuring images of good AI that reliably supports us.

Worried philosophers have imagined an AI that has the ultimate goal of making ordinary paper clips. This AI is assumed to be self-improving. It is therefore becoming increasingly intelligent and powerful when it comes to its goal of manufacturing paper clips. When the raw materials run out, it learns new ways to turn the earth’s resources into paper clips, and when humans try to prevent it from destroying the planet, it learns to destroy humanity. When the planet is wiped out, it travels into space and turns the universe into paper clips.

Philosophers who issue warnings about “evil” super-intelligent AI also express hopes for “good” super-intelligent AI. Suppose we could give self-improving AI the goal of serving humanity. Without getting tired, it would develop increasingly intelligent and powerful ways of serving us, until the end of time. Unlike the god of religion, this artificial superintelligence would hear our prayers and take ever-smarter action to help us. It would probably sooner or later learn to prevent earthquakes and our climate problems would soon be gone. No theodicy in the world could undermine our faith in this artificial god, whose power to protect us from evil is ever-increasing. Of course, it is unclear how the goal of serving humanity can be defined. But given the opportunity to finally secure the future of humanity, some hopeful philosophers believe that the development of human-friendly self-improving AI should be one of the most essential tasks of our time.

I read all this in a well-written article by Wolfhart Totschnig, who questions the rigid goal orientation associated with autonomous AI in the scenarios above. His most important point is that rigidly goal-oriented AI, which runs amok in the universe or saves humanity from every predicament, is not even conceivable. Outside its domain, the goal loses its meaning. The goal of a self-driving car to safely take the user to the destination has no meaning outside the domain of road traffic. Domain-specific AI can therefore not be generalized to the world as a whole, because the utility function loses its meaning outside the domain, long before the universe is turned into paper clips or the future of humanity is secured by an artificially good god.

This is, of course, an important philosophical point about goals and meaning, about specific domains and the world as a whole. The critique helps us to more realistically assess the risks and opportunities of future AI, without being bewitched by our images. At the same time, I get the impression that Totschnig continues to use AI as a projection screen for human self-images. He argues that future AI may well revise its ultimate goals as it develops a general understanding of the world. The weakness of the above scenarios was that they projected today’s domain-specific AI, not the general intelligence of humans. We then do not see the possibility of a genuinely human-like AI that self-critically reconsiders its final goals when new knowledge about the world makes it necessary. Truly human-equivalent AI would have full autonomy.

Projecting human self-images on future AI is not just a tendency, as far as I can judge, but a norm that governs the discussion. According to this norm, the wrong image is projected in the scenarios above. An image of today’s machines, not of our general human intelligence. Projecting the right self-image on future AI thus appears as an overall goal. Is the goal meaningful or should it be reconsidered self-critically?

These are difficult issues and my impression of the philosophical discussion may be wrong. If you want to judge for yourself, read the article: Fully autonomous AI.

Pär Segerdahl

Written by…

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

Totschnig, W. Fully Autonomous AI. Sci Eng Ethics 26, 2473–2485 (2020). https://doi.org/10.1007/s11948-020-00243-z

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Digital twins, virtual brains and the dangers of language

A new computer simulation technology has begun to be introduced, for example, in the manufacturing industry. The computer simulation is called a digital twin, which challenges me to bring to life for the reader what something that sounds so imaginative can be in reality.

The most realistic explanation I can find actually comes from Harry Potter’s world. Do you remember the map of Hogwarts, which not only shows all the rooms and corridors, but also the steps in real time of those who sneak around the school? A similar map can be easily created in a computer environment by connecting the map in the computer to sensors in the floor of the building that the map depicts. Immediately you have an interactive digital map of the building that is automatically updated and shows people’s movements in it. Imagine further that the computer simulation can make calculations that predict crowds that exceed the authorities’ recommendations, and that it automatically sends out warning messages via a speaker system. As far as I understand, such an interactive digital map can be called a digital twin for an intelligent house.

Of course, this is a revolutionary technology. The architect’s drawing in a computer program gets extended life in both the production and maintenance of the building. The digital simulation is connected to sensors that update the simulation with current data on relevant factors in the construction process and thereafter in the finished building. The building gets a digital twin that during the entire life cycle of the building automatically contacts maintenance technicians when the sensors show that the washing machines are starting to wear out or that the air is not circulating properly.

The scope of use for digital twins is huge. The point of them, as I understand it, is not that they are “exact virtual copies of reality,” whatever that might mean. The point is that the computer simulation is linked to the simulated object in a practically relevant way. Sensors automatically update the simulation with relevant data, while the simulation automatically updates the simulated object in relevant ways. At the same time, users, manufacturers, maintenance technicians and other actors are updated, who easily can monitor the object’s current status, opportunities and risks, wherever they are in the world.

The European flagship project Human Brain Project plans to develop digital twins of human brains by building virtual brains in a computer environment. In a new article, the philosophers Kathinka Evers and Arleen Salles, who are both working in the project, examine the enormous challenges involved in developing digital twins of living human brains. Is it even conceivable?

The authors compare types of objects that can have digital twins. It can be artefacts such as buildings and cars, or natural inanimate phenomena such as the bedrock at a mine. But it could also be living things such as the heart or the brain. The comparisons in the article show that the brain stands out in several ways, all of which make it unclear whether it is reasonable to talk about digital twins of human brains. Would it be more appropriate to talk about digital cousins?

The brain is astronomically complex and despite new knowledge about it, it is highly opaque to our search for knowledge. How can we talk about a digital twin of something that is as complex as a galaxy and as unknown as a black hole? In addition, the brain is fundamentally dynamically interactive. It is connected not only with the body but also with culture, society and the world around it, with which it develops in uninterrupted interaction. The brain almost merges with its environment. Does that imply that a digital twin would have to be a twin of the brain-body-culture-society-world, that is, a digital twin of everything?

No, of course not. The aim of the project is to find specific medical applications of the new computer simulation technology. By developing digital twins of certain aspects of certain parts of patients’ brains, it is hoped that one can improve and individualize, for example, surgical procedures for diseases such as epilepsy. Just as the map from Harry Potter’s world shows people’s steps in real time, the digital twin of the brain could follow the spread of certain nerve impulses in certain parts of the patient’s brain. This can open up new opportunities to monitor, diagnose, predict and treat diseases such as epilepsy.

Should we avoid the term digital twin when talking about the brain? Yes, it would probably be wiser to talk about digital siblings or digital cousins, argue Kathinka Evers and Arleen Salles. Although experts in the field understand its technical use, the term “digital twin” is linguistically risky when we talk about human brains. It easily leads the mind astray. We imagine that the digital twin must be an exact copy of a human’s whole brain. This risks creating unrealistic expectations and unfounded fears about the development. History shows that language also contains other dangers. Words come with normative expectations that can have ethical and social consequences that may not have been intended. Talking about a digital twin of a mining drill is probably no major linguistic danger. But when it comes to the brains of individual people, the talk of digital twins can become a new linguistic arena where we reinforce prejudices and spread fears.

After reading some popular scientific explanations of digital twins, I would like to add that caution may be needed also in connection with industrial applications. After all, the digital twin of a mining drill is not an “exact virtual copy of the real drill” in some absolute sense, right down to the movements of individual atoms. The digital twin is a copy in the practical sense that the application makes relevant. Sometimes it is enough to copy where people put their feet down, as in Harry Potter’s world, whose magic unexpectedly helps us understand the concept of a digital twin more realistically than many verbal explanations do. Explaining words with the help of other words is not always clarifying, if all the words steer thought in the same direction. The words “copy” and “replica” lead our thinking just as right and just as wrong as the word “twin” does.

If you want to better understand the challenges of creating digital twins of human brains and the importance of conceptual clarity concerning the development, read the philosophically elucidatory article: Epistemic Challenges of Digital Twins & Virtual Brains: Perspectives from Fundamental Neuroethics.

Pär Segerdahl

Written by…

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

Evers, Kathinka & Salles, Arleen. (2021). Epistemic Challenges of Digital Twins & Virtual Brains: Perspectives from Fundamental Neuroethics. SCIO: Revista de Filosofía. 27-53. 10.46583 / scio_2021.21.846

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