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

Tag: neuroethics (Page 5 of 9)

What is required of an ethics of artificial intelligence?

I recently highlighted criticism of the ethics that often figures in the field of artificial intelligence (AI). An ethics that can handle the challenges that AI presents us with requires more than just beautifully formulated ethical principles, values ​​and guidelines. What exactly is required of an ethics of artificial intelligence?

Michele Farisco, Kathinka Evers and Arleen Salles address the issue in the journal Science and Engineering Ethics. For them, ethics is not primarily principles and guidelines. Ethics is rather an ongoing process of thinking: it is continual ethical reflection on AI. Their question is thus not what is required of an ethical framework built around AI. Their question is what is required of in-depth ethical reflection on AI.

The authors emphasize conceptual analysis as essential in all ethical reflection on AI. One of the big difficulties is that we do not know exactly what we are discussing! What is intelligence? What is the difference between artificial and natural intelligence? How should we understand the relationship between intelligence and consciousness? Between intelligence and emotions? Between intelligence and insightfulness?

Ethical problems about AI can be both practical and theoretical, the authors point out. They describe two practical and two theoretical problems to consider. One practical problem is the use of AI in activities that require emotional abilities that AI lacks. Empathy gives humans insight into other humans’ needs. Therefore, AI’s lack of emotional involvement should be given special attention when we consider using AI in, for example, child or elderly care. The second practical problem is the use of AI in activities that require foresight. Intelligence is not just about reacting to input from the environment. A more active, foresighted approach is often needed, going beyond actual experience and seeing less obvious, counterintuitive possibilities. Crying can express pain, joy and much more, but AI cannot easily foresee less obvious possibilities.

Two theoretical problems are also mentioned in the article. The first is whether AI in the future may have morally relevant characteristics such as autonomy, interests and preferences. The second problem is whether AI can affect human self-understanding and create uncertainty and anxiety about human identity. These theoretical problems undoubtedly require careful analysis – do we even know what we are asking? In philosophy we often need to clarify our questions as we go along.

The article emphasizes one demand in particular on ethical analysis of AI. It should carefully consider morally relevant abilities that AI lacks, abilities needed to satisfy important human needs. Can we let a cute kindergarten robot “comfort” children when they scream with joy or when they injure themselves so badly that they need nursing?

Pär Segerdahl

Written by…

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

Farisco, M., Evers, K. & Salles, A. Towards establishing criteria for the ethical analysis of Artificial Intelligence. Science and Engineering Ethics (2020). https://doi.org/10.1007/s11948-020-00238-w

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We want solid foundations

Ethics as renewed clarity about new situations

An article in the journal Big Data & Society criticizes the form of ethics that has come to dominate research and innovation in artificial intelligence (AI). The authors question the same “framework interpretation” of ethics that you could read about on the Ethics Blog last week. However, with one disquieting difference. Rather than functioning as a fence that can set the necessary boundaries for development, the framework risks being used as ethics washing by AI companies that want to avoid legal regulation. By referring to ethical self-regulation – beautiful declarations of principles, values ​​and guidelines – one hopes to be able to avoid legal regulation, which could set important limits for AI.

The problem with AI ethics as “soft ethics legislation” is not just that it can be used to avoid necessary legal regulation of the area. The problem is above all, according to the SIENNA researchers who wrote the article, that a “law conception of ethics” does not help us to think clearly about new situations. What we need, they argue, is an ethics that constantly renews our ability to see the new. This is because AI is constantly confronting us with new situations: new uses of robots, new opportunities for governments and companies to monitor people, new forms of dependence on technology, new risks of discrimination, and many other challenges that we may not easily anticipate.

The authors emphasize that such eye-opening AI ethics requires close collaboration with the social sciences. That, of course, is true. Personally, I want to emphasize that an ethics that renews our ability to see the new must also be philosophical in the deepest sense of the word. To see the new and unexpected, you cannot rest comfortably in your professional competence, with its established methods, theories and concepts. You have to question your own disciplinary framework. You have to think for yourself.

Read the article, which has already attracted well-deserved attention.

Pär Segerdahl

Written by…

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

Anaïs Rességuier, Rowena Rodrigues. 2020. AI ethics should not remain toothless! A call to bring back the teeth of ethics. Big Data & Society

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We like critical thinking

Ethical frameworks for research

The word ethical framework evokes the idea of ​​something rigid and separating, like the fence around the garden. The research that emerges within the framework is dynamic and constantly new. However, to ensure safety, it is placed in an ethical framework that sets clear boundaries for what researchers are allowed to do in their work.

That this is an oversimplified picture is clear after reading an inventive discussion of ethical frameworks in neuroscientific research projects, such as the Human Brain Project. The article is written by Arleen Salles and Michele Farisco at CRB and is published in AJOB Neuroscience.

The article questions not only the image of ethical frameworks as static boundaries for dynamic research activities. Inspired by ideas within so-called responsible research and innovation (RRI), the image that research can be separated from ethics and society is also questioned.

Researchers tend to regard research as their own concern. However, there are tendencies towards increasing collaboration not only across disciplinary boundaries, but also with stakeholders such as patients, industry and various forms of extra-scientific expertise. These tendencies make research an increasingly dispersed, common concern. Not only in retrospect in the form of applications, which presupposes that the research effort can be separated, but already when research is initiated, planned and carried out.

This could sound threatening, as if foreign powers were influencing the free search for truth. Nevertheless, there may also be something hopeful in the development. To see the hopeful aspect, however, we need to free ourselves from the image of ethical frameworks as static boundaries, separate from dynamic research.

With examples from the Human Brain Project, Arleen Salles and Michele Farisco try to show how ethical challenges in neuroscience projects cannot always be controlled in advance, through declared principles, values ​​and guidelines. Even ethical work is dynamic and requires living intelligent attention. The authors also try to show how ethical attention reaches all he way into the neuroscientific issues, concepts and working conditions.

When research on the human brain is not aware of its own cultural and societal conditions, but takes them for granted, it may mean that relevant questions are not asked and that research results do not always have the validity that one assumes they have.

We thus have good reasons to see ethical and societal reflections as living parts of neuroscience, rather than as rigid frameworks around it.

Pär Segerdahl

Written by…

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

Arleen Salles & Michele Farisco (2020) Of Ethical Frameworks and Neuroethics in Big Neuroscience Projects: A View from the HBP, AJOB Neuroscience, 11:3, 167-175, DOI: 10.1080/21507740.2020.1778116

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We like real-life ethics

Working online during the pandemic: recommendations from the Human Brain Project

The covid-19 pandemic forced many of us to work online from home. The change contained surprises, both positive and negative. We learned that it is possible to have digital staff meetings, seminars and coffee breaks, and that working from home can sometimes mean less interference than working in the office. We also discovered how much better the office chair and desk are, how difficult it is to try to be professional online from an untidy home, and that working from home often means more interference than working in the office!

The European Human Brain Project (HBP) has extensive experience of collaborating digitally, with regular online meetings. This is how they worked long before the pandemic struck, since the project is a collaboration between more than 100 partner institutions in almost 20 countries, also outside Europe. As part of the project’s investment in responsible research and innovation, special efforts are now being made to digitally include everyone, when so much of the work has moved to the internet.

In the Journal of Responsible Technology, Karin Grasenick and Manuel Guerrero from HBP formulate recommendations based on experiences from the project. Their recommendations concern four areas: How do we facilitate social and family life? How do we reduce stress and anxiety? How do we handle career stages, roles and responsibilities? How do we support team spirit and virtual cooperation?

Read the concise article! You will recognize your work situation and be inspired by the suggestions. Even after the pandemic, online collaboration will occur.

Pär Segerdahl

Written by…

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

Karin Grasenick,  Manuel Guerrero, Responsible Research and Innovation& Digital Inclusiveness during Covid-19 Crisis in the Human Brain Project (HBP), Journal of Responsi-ble Technology(2020), doi: https://doi.org/10.1016/j.jrt.2020.06.001

We like ethics

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Ethical fitness apps for high performance morality

In an unusually rhetorical article for being in a scientific journal, the image is drawn of a humanity that frees itself from moral weakness by downloading ethical fitness apps.

The authors claim that the maxim “Know thyself!” from the temple of Apollo at Delphi is answered today more thoroughly than ever. Never has humanity known more about itself. Ethically, we are almost fully educated. We also know more than ever about the moral weaknesses that prevent us from acting in accordance with the ethical principles that we finally know so well. Research is discovering more and more mechanisms in the brain and in our psychology that affect humanity’s moral shortcomings.

Given this enormous and growing self-knowledge, why do we not develop artificial intelligence that supports a morally limping humanity? Why spend so much resources on developing even more intelligent artificial intelligence, which takes our jobs and might one day threaten humanity in the form of uncontrollable superintelligence? Why do we behave so unwisely when we could develop artificial intelligence to help us humans become superethical?

How can AI make morally weak humans super-ethical? The authors suggest a comparison with the fitness apps that help people to exercise more efficiently and regularly than they otherwise would. The authors’ suggestion is that our ethical knowledge of moral theories, combined with our growing scientific knowledge of moral weaknesses, can support the technological development of moral crutches: wise objects that support people precisely where we know that we are morally limping.

My personal assessment of this utopian proposal is that it might easily be realized in less utopian form. AI is already widely used as a support in decision-making. One could imagine mobile apps that support consumers to make ethical food choices in the grocery shop. Or computer games where consumers are trained to weigh different ethical considerations against each another, such as animal welfare, climate effects, ecological effects and much more. Nice looking presentations of the issues and encouraging music that make it fun to be moral.

The philosophical question I ask is whether such artificial decision support in shops and other situations really can be said to make humanity wiser and more ethical. Imagine a consumer who chooses among the vegetables, eagerly looking for decision support in the smartphone. What do you see? A human who, thanks to the mobile app, has become wiser than Socrates, who lived long before we knew as much about ourselves as we do today?

Ethical fitness apps are conceivable. However, the risk is that they spread a form of self-knowledge that flies above ourselves: self-knowledge suspiciously similar to the moral vice of self-satisfied presumptuousness.

Pär Segerdahl

Written by…

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

Pim Haselager & Giulio Mecacci (2020) Superethics Instead of Superintelligence: Know Thyself, and Apply Science Accordingly, AJOB Neuroscience, 11:2, 113-119, DOI: 10.1080/21507740.2020.1740353

The temptation of rhetoric

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Responsibly planned research communication

Academic research is driven by dissemination of results to peers at conferences and through publication in scientific journals. However, research results belong not only to the research community. They also belong to society. Therefore, results should reach not only your colleagues in the field or the specialists in adjacent fields. They should also reach outside the academy.

Who is out there? A homogeneous public? No, it is not that simple. Communicating research is not two activities: first communicating the science to peers and then telling the popular scientific story to the public. Outside the academy, we find engineers, entrepreneurs, politicians, government officials, teachers, students, research funders, taxpayers, healthcare professionals… We are all out there with our different experiences, functions and skills.

Research communication is therefore a strategically more complicated task than just “reaching the public.” Why do you want to communicate your results; why are they important? Who will find your results important? How do you want to communicate them? When is the best time to communicate? There is not just one task here. You have to think through what the task is in each particular case. For the task varies with the answers to these questions. Only when you can think strategically about the task can you communicate research responsibly.

Josepine Fernow is a skilled and experienced research communications officer at CRB. She works with communication in several research projects, including the Human Brain Project and STARBIOS2. In the latter project, about Responsible Research and Innovation (RRI), she contributes in a new book with arguments for responsibly planned research communication: Achieving impact: some arguments for designing a communications strategy.

Josepine Fernow’s contribution is, in my view, more than a convincing argument. It is an eye-opening text that helps researchers see more clearly their diverse relationships to society, and thereby their responsibilities. The academy is not a rock of knowledge in a sea of ​​ignorant lay people. Society consists of experienced people who, because of what they know, can benefit from your research. It is easier to think strategically about research communication when you survey your relations to a diversified society that is already knowledgeable. Josepine Fernow’s argumentation helps and motivates you to do that.

Josepine Fernow also warns against exaggerating the significance of your results. Bioscience has potential to give us effective treatments for serious diseases, new crops that meet specific demands, and much more. Since we are all potential beneficiaries of such research, as future patients and consumers, we may want to believe the excessively wishful stories that some excessively ambitious researchers want to tell. We participate in a dangerous game of increasingly unrealistic hopes.

The name of this dangerous game is hype. Research hype can make it difficult for you to continue your research in the future, because of eroded trust. It can also make you prone to take unethical shortcuts. The “huge potential benefit” obscures your judgment as a responsible researcher.

In some research fields, it is extra difficult to avoid research hype, as exaggerated hopes seem inscribed in the very language of the field. An example is artificial intelligence (AI), where the use of psychological and neuroscientific vocabulary about machines can create the impression that one has already fulfilled the hopes. Anthropomorphic language can make it sound as if some machines already thought like humans and functioned like brains.

Responsible research communication is as important as difficult. Therefore, these tasks deserve our greatest attention. Read Josepine Fernow’s argumentation for carefully planned communication strategies. It will help you see more clearly your responsibility.

Finally, a reminder for those interested: the STARBIOS2 project organizes its final event via Zoom on Friday, May 29, 2020.

Pär Segerdahl

Written by…

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

Fernow, J. (2019). Note #11: Achieving impact: Some arguments for designing a communications strategy, In A. Declich (Ed.), RRI implementation in bioscience organisations: Guidelines from the STARBIOS2 project, (pp. 177-180). Uppsala University. ISBN: 978-91-506-2811-1

We care about communication

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Inspiration for responsible research and innovation

Our attitude to science is changing. Can we talk solemnly about it anymore as a unified endeavor, or even about sciences? It seems more apt to talk about research activities that produce useful and applicable knowledge.

Science has been dethroned, it seems. In the past, we revered it as free and independent search for the truth. We esteemed it as our tribunal of truth, as the last arbiter of truth. Today, we demand that it brings benefits and adapts to society. The change is full of tension because we still want to use scientific expertise as a higher intellectual authority. Should we bow to the experts or correct them if they do not deliver the “right knowledge” or the “desirable facts”?

Responsible Research and Innovation (RRI) is an attempt to manage this risky change, adapting science to new social requirements. As you hear from the name, RRI is partly an expression of the same basic attitude change. One could perhaps view RRI as the responsible dethroning of science.

Some mourn the dethroning, others rejoice. Here I just want to link RRI to the changed attitude to science. RRI handles a change that is basically affirmed. The ambiguous attitude to scientific expertise, mentioned above, shows how important it is that we take responsibility for people’s trust in what is now called research and innovation. For why should we listen to representatives of a sector with such unholy designation?

RRI is introduced in European research within the Horizon 2020 programme. Several projects are specifically about implementing and studying RRI. Important aspects of RRI are gender equality, open access publishing, science education, research communication, public engagement and ethics. It is about adapting research and innovation to a society with new hopes and demands on what we proudly called science.

A new book describes experiences of implementing RRI in a number of bioscience organizations around the world. The book is written within the EU-project, STARBIOS2. In collaboration with partners in Europe, Africa and the Americas, this project planned and implemented several RRI initiatives and reflected on the work process. The purpose of STARBIOS2 has been to change organizations durably and structurally. The book aims to help readers formulate their own action plans and initiate structural changes in their organizations.

The cover describes the book as guidelines. However, you will not find formulated guidelines. What you will find, and which might be more helpful, is self-reflection on concrete examples of how to work with RRI action plans. You will find suggestions on how to emphasize responsibility in research and development. Thus, you can read about efforts to support gender equality, improve exchange with the public and with society, support open access publication, and improve ethics. Read and be inspired!

Finally, I would like to mention that the Ethics Blog, as well as our ethics activities here at CRB, could be regarded as examples of RRI. I plan to return later with a post on research communication.

The STARBIOS2 project is organising a virtual final event on 29 May! Have a look at the preliminary programme!

Pär Segerdahl

Written by…

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

Declich, Andrea. 2019. RRI implementation in bioscience organisations: Guidelines from the STARBIOS2 project.

We recommend readings

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Anthropomorphism in AI can limit scientific and technological development

Anthropomorphism almost seems inscribed in research on artificial intelligence (AI). Ever since the beginning of the field, machines have been portrayed in terms that normally describe human abilities, such as understanding and learning. The emphasis is on similarities between humans and machines, while differences are downplayed. Like when it is claimed that machines can perform the same psychological tasks that humans perform, such as making decisions and solving problems, with the supposedly insignificant difference that machines do it “automated.”

You can read more about this in an enlightening discussion of anthropomorphism in and around AI, written by Arleen Salles, Kathinka Evers and Michele Farisco, all at CRB and the Human Brain Project. The article is published in AJOB Neuroscience.

The article draws particular attention to so-called brain-inspired AI research, where technology development draws inspiration from what we know about the functioning of the brain. Here, close relationships are emphasized between AI and neuroscience: bonds that are considered to be decisive for developments in both fields of research. Neuroscience needs inspiration from AI research it is claimed, just as AI research needs inspiration from brain research.

The article warns that this idea of ​​a close relationship between the two fields presupposes an anthropomorphic interpretation of AI. In fact, brain-inspired AI multiplies the conceptual double exposures by projecting not only psychological but also neuroscientific concepts onto machines. AI researchers talk about artificial neurons, synapses and neural networks in computers, as if they incorporated artificial brain tissue into the machines.

An overlooked risk of anthropomorphism in AI, according to the authors, is that it can conceal essential characteristics of the technology that make it fundamentally different from human intelligence. In fact, anthropomorphism risks limiting scientific and technological development in AI, since it binds AI to the human brain as privileged source of inspiration. Anthropomorphism can also entice brain research to uncritically use AI as a model for how the brain works.

Of course, the authors do not deny that AI and neuroscience mutually support each other and should cooperate. However, in order for cooperation to work well, and not limit scientific and technological development, philosophical thinking is also needed. We need to clarify conceptual differences between humans and machines, brains and computers. We need to free ourselves from the tendency to exaggerate similarities, which can be more verbal than real. We also need to pay attention to deep-rooted differences between humans and machines, and learn from the differences.

Anthropomorphism in AI risks encouraging irresponsible research communication, the authors further write. This is because exaggerated hopes (hype) seem intrinsic to the anthropomorphic language. By talking about computers in psychological and neurological terms, it sounds as if these machines already essentially functioned as human brains. The authors speak of an anthropomorphic hype around neural network algorithms.

Philosophy can thus also contribute to responsible research communication about artificial intelligence. Such communication draws attention to exaggerated claims and hopes inscribed in the anthropomorphic language of the field. It counteracts the tendency to exaggerate similarities between humans and machines, which rarely go as deep as the projected words make it sound.

In short, differences can be as important and instructive as similarities. Not only in philosophy, but also in science, technology and responsible research communication.

Pär Segerdahl

Written by…

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

Arleen Salles, Kathinka Evers & Michele Farisco (2020) Anthropomorphism in AI, AJOB Neuroscience, 11:2, 88-95, DOI: 10.1080/21507740.2020.1740350

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What is a moral machine?

I recently read an article about so-called moral robots, which I found clarifying in many ways. The philosopher John-Stewart Gordon points out pitfalls that non-ethicists – robotics researchers and AI programmers – may fall into when they try to construct moral machines. Simply because they lack ethical expertise.

The first pitfall is the rookie mistakes. One might naively identify ethics with certain famous bioethical principles, as if ethics could not be anything but so-called “principlism.” Or, it is believed that computer systems, through automated analysis of individual cases, can “learn” ethical principles and “become moral,” as if morality could be discovered experientially or empirically.

The second challenge has to do with the fact that the ethics experts themselves disagree about the “right” moral theory. There are several competing ethical theories (utilitarianism, deontology, virtue ethics and more). What moral template should programmers use when getting computers to solve moral problems and dilemmas that arise in different activities? (Consider self-driving cars in difficult traffic situations.)

The first pitfall can be addressed with more knowledge of ethics. How do we handle the second challenge? Should we allow programmers to choose moral theory as it suits them? Should we allow both utilitarian and deontological robot cars on our streets?

John-Stewart Gordon’s suggestion is that so-called machine ethics should focus on the similarities between different moral theories regarding what one should not do. Robots should be provided with a binding list of things that must be avoided as immoral. With this restriction, the robots then have leeway to use and balance the plurality of moral theories to solve moral problems in a variety of ways.

In conclusion, researchers and engineers in robotics and AI should consult the ethics experts so that they can avoid the rookie mistakes and understand the methodological problems that arise when not even the experts in the field can agree about the right moral theory.

All this seems both wise and clarifying in many ways. At the same time, I feel genuinely confused about the very idea of ​​”moral machines” (although the article is not intended to discuss the idea, but focuses on ethical challenges for engineers). What does the idea mean? Not that I doubt that we can design artificial intelligence according to ethical requirements. We may not want robot cars to avoid collisions in city traffic by turning onto sidewalks where many people walk. In that sense, there may be ethical software, much like there are ethical funds. We could talk about moral and immoral robot cars as straightforwardly as we talk about ethical and unethical funds.

Still, as I mentioned, I feel uncertain. Why? I started by writing about “so-called” moral robots. I did so because I am not comfortable talking about moral machines, although I am open to suggestions about what it could mean. I think that what confuses me is that moral machines are largely mentioned without qualifying expressions, as if everyone ought to know what it should mean. Ethical experts disagree on the “right” moral theory. However, they seem to agree that moral theory determines what a moral decision is; much like grammar determines what a grammatical sentence is. With that faith in moral theory, one need not contemplate what a moral machine might be. It is simply a machine that makes decisions according to accepted moral theory. However, do machines make decisions in the same sense as humans do?

Maybe it is about emphasis. We talk about ethical funds without feeling dizzy because a stock fund is said to be ethical (“Can they be humorous too?”). There is no mythological emphasis in the talk of ethical funds. In the same way, we can talk about ethical robot cars without feeling dizzy as if we faced something supernatural. However, in the philosophical discussion of machine ethics, moral machines are sometimes mentioned in a mythological way, it seems to me. As if a centaur, a machine-human, will soon see the light of day. At the same time, we are not supposed to feel dizzy concerning these brave new centaurs, since the experts can spell out exactly what they are talking about. Having all the accepted templates in their hands, they do not need any qualifying expressions!

I suspect that also ethical expertise can be a philosophical pitfall when we intellectually approach so-called moral machines. The expert attitude can silence the confusing questions that we all need time to contemplate when honest doubts rebel against the claim to know.

Pär Segerdahl

Written by…

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

Gordon, J. Building Moral Robots: Ethical Pitfalls and Challenges. Sci Eng Ethics 26, 141–157 (2020).

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Herb Terrace about the chimpanzee Nim – do you see the contradiction?

Have you seen small children make repeated attempts to squeeze a square object through a round hole (plastic toy for the little ones)? You get puzzled: Do they not see that it is impossible? The object and the hole have different shapes!

Sometimes adults are just as puzzling. Our intellect does not always fit reality. Yet, we force our thoughts onto reality, even when they have different shapes. Maybe we are extra stubborn precisely when it is not possible. This post is about such a case.

Herb Terrace is known as the psychologist who proved that apes cannot learn language. He himself tried to teach sign language to the chimpanzee Nim, but failed according to his own judgement. When Terrace took a closer look at the videotapes, where Nim interacted with his human sign-language teachers, he saw how Nim merely imitated the teachers’ signs, to get his reward.

I recently read a blog post by Terrace where he not only repeats the claim that his research demonstrates that apes cannot learn language. The strange thing is that he also criticizes his own research severely. He writes that he used the wrong method with Nim, namely, that of giving him rewards when the teacher judged that he made the right signs. The reasoning becomes even more puzzling when Terrace writes that not even a human child could learn language with such a method.

To me, this is as puzzling as a child’s insistence on squeezing a square object through a round hole. If Terrace used the wrong method, which would not work even on a human child, then how can he conclude that Project Nim demonstrates that apes cannot learn language? Nevertheless, he insists on reasoning that way, without feeling that he contradicts himself. Nor does anyone who read him seem to experience any contradiction. Why?

Perhaps because most of us think that humans cannot teach animals anything at all, unless we train them with rewards. Therefore, since Nim did not learn language with this training method, apes cannot learn language. Better methods do not work on animals, we think. If Terrace failed, then everyone must fail, we think.

However, one researcher actually did try a better method in ape language research. She used an approach to young apes that works with human children. She stopped training the apes via a system of rewards. She lived with the apes, as a parent with her children. And it succeeded!

Terrace almost never mentions the name of the successful ape language researcher. After all, she used a method that is impossible with animals: she did not train them. Therefore, she cannot have succeeded, we think.

I can tell you that the name of the successful researcher is Sue Savage-Rumbaugh. To see a round reality beyond a square thinking, we need to rethink our thought pattern. If you want to read a book that tries to do such rethinking about apes, humans and language, I recommend a philosophical self-critique that I wrote with Savage-Rumbaugh and her colleague William Fields.

To philosophize is to learn to stop imposing our insane thoughts on reality. Then we finally see reality as it is.

Pär Segerdahl

Written by…

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

Segerdahl, P., Fields, W. & Savage-Rumbaugh, S. 2005. Kanzi’s Primal Language. The Cultural Initiation of Primates into Language. Palgrave Macmillan.

Understanding enculturated apes

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