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.
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.
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.
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
Scientific discovery is based on the novelty of the questions you ask. This means that if you want to discover something new, you probably have to ask a different question. And since different people have different preconceptions and experiences than you, they are likely to formulate their questions differently. This makes a case for diversity in research, If we want to make new discoveries that concern diverse groups, diversity in research becomes even more important.
The Human Brain Project participated in the FENS 2020 Virtual Forum this summer, an international virtual neuroscience conference that explores all domains in modern brain research. For the Human Brain Project (HBP), committed to responsible research and innovation, this includes diversity. Which is why Karin Grasenick, Coordinator for Gender and Diversity in the HBP, explored the relationship between diversity and new discovery in the session “Of mice, men and machines” at the FENS 2020.
So why is diversity in research crucial to make new discoveries? Research depends on the questions asked, the models used, and the details considered. For this reason, it is important to reflect on why certain variables are analysed, or which aspects might play a role. An example is Parkinson’s disease, where patients are affected differently depending on both age and gender. Being a (biological) man or woman, old or young is important for both diagnosis and treatment. If we know that diversity matters in research on Parkinson’s disease, it probably should do so in most neuroscience. Apart from gender and age, we also need to consider other aspects of diversity, like race, ethnicity, education or social background. Because depending on who you are, biologically, culturally and socially, you are likely to need different things.
A quite recent example for this is Covid-19, which does not only display gender differences (as it affects more men than women), but also racial differences: Black and Latino people in the US have been disproportionately affected, regardless of their living area (rural or urban) or their age (old or young). Again, the reasons for this are not simply biologically essentialist (e.g. hormones or chromosomes), but also linked to social aspects such as gendered lifestyles (men are more often smokers than women), inequities in the health system or certain jobs which cannot be done remotely (see for example this BBC Future text on why Covid-19 is different for men and women or this one on the racial inequity of coronavirus in The New York Times).
Another example is Machine Learning. If we train AI on data that is not representative of the population, we introduce bias in the algorithm. For example, applications to diagnose skin cancer in medicine more often fail to recognize tumours in darker skin correctly because they are trained using pictures of fair skin. There are several reasons for not training AI properly, it could be a cost issue, lack of material to train the AI on, but it is not unlikely that people with dark skin are discriminated because scientists and engineers simply did not think about diversity when picking material for the AI to train on. In the case of skin cancer, it is clear that diversity could indeed save lives.
But where to start? When you do research, there are two questions that must be asked: First, what is the focus of your research? And second, who are the beneficiaries of your research?
Whenever your research focus includes tissues, cells, animals or humans, you should consider diversity factors like gender, age, race, ethnicity, and environmental influences. Moreover, any responsible scientist should consider who has access to their research and profits from it, as well as the consequences their research might have for end users or the broader public.
However, as a researcher you need to consider not only the research subjects and the people your results benefit. The diversity of the research team also matters, because different people perceive problems in different ways and use different methods and processes to solve them. Which is why a diverse team is more innovative.
This is a guest blog post from the Human Brain Project (HBP). The HBP as received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).
During the last phase of the Human Brain Project, the activities on this blog received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539). The views and opinions expressed on this blog are the sole responsibility of the author(s) and do not necessarily reflect the views of the European Commission.
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