We’re happy to share insights from Rónán Kennedy, Associate Professor in the School of Law at the University of Galway, on the discussions about AI’s role in education and research at the latest AI4People Summit.
The session, titled AI’s Impact on Science and the Humanities, brought together experts to explore key challenges in education and research, including Human/AI collaboration, data trustworthiness, and the implications for public interest.
The discussion featured speakers including Arisa Ema, Associate Professor and Sociologist at the University of Tokyo; Magnus Franklin, Managing Director at Teneo; and Iryna Gurevych, Professor in the Department of Computer Science at the Technical University of Darmstadt.
To watch this discussion please watch here
Cross-Disciplinary Approaches
As AI becomes more powerful, it is increasingly doing what humans used to do. Bias becomes a central question, which in turns requires a consideration of how humans can be involved in the development of technology. This is a cross-disciplinary issue, in which science and technology studies should be a central part of the response. Academia therefore needs to re-invent itself, so that it is a hub for independent thinking and as a locus for debate, which facilitates collaboration around important questions.
This may require new research methods. However, cross-disciplinary work is not easy. We must determine what our assumptions and unconscious biases are, how they vary by discipline, and how we can find common ground, essentially asking what a particular discipline finds unusual. AI can be of assistance here, particularly by simulating conversations across disciplinary boundaries so that different disciplines can understand each other better.
Collaboration in Scientific Research
Another important question is whether scientists and machines can co-operate? Developments such as the AI Scientist and the Agents4Science Conference indicate that serious work is being done to explore this. This therefore requires that we re-consider the role of a scientist, and whether research teams made up of humans and AI agents can co-construct solutions together.
Data Fidelity
Underlying all of this is a concern about data fidelity and the potential for drift from accuracy as synthetic data expands and begins to feed into the generative AI training pipeline. Self-generating AI is not very viable in the long-term. It is only a technology and a tool; human creativity is still one step ahead of it. We need to ensure that we are observing the real world, not a synthetic version. We also need to ask which version of history is used to train models. This means that humanities research is vital, particularly to help understand how AI is transforming the world. STEM research tends to be very standardised. The humanities are much more diverse. They will therefore require different solutions.
The Public Interest
We should also be considering the public interest in this process, particularly so that resources are not always allocated towards the most profitable avenues, ignoring (for example) cures for rare diseases. We should therefore consider where and how the public sector and government intervenes.
We should take a long-term perspective and remember that AI is an older and broader technology than only generative AI. We also need to consider the long-term cost of AI and conduct scientific research into whether it does produce cost benefits in the long-term, as the testing, maintenance and debugging of (for example) code that it produces may outweigh the short-term savings. The usefulness of these tools varies with the user and the task; the quality is often not good enough for an expert. The effort required to verify results can be very high.
Science is in a crisis. AI could raise the quality of peer review. It can adapt to human users. It can provide support that is appropriate to the level of expertise of the user and is available at any time. It can also review much more literature than humans can. This could be a training tool. The quality of the peer review is what matters. We can adjust to this as we adjusted to Internet search and Wikipedia. The role of education is important here.
The public interest is difficult to define and therefore difficult to optimise in an algorithmic way. The field is also changing very quickly and the universities are not well equipped to respond to it. There are claims that are not peer-reviewed and there is plenty of misleading information. Experts are needed to review this but they are expensive. These tools are also dual use and therefore there are important safety questions. This means that there is a need for regulation but social values differ between countries and cultures.
We may be able to learn from the history of Internet governance, which is multi-stakeholder. Trust in the technology is a key challenge. Can this be produced by technical means? Perhaps through privacy controls, source attribution, and value alignment. Critical AI literacy is also important and should be included in school curricula.
Actionable Roadmaps
“There is a need for actionable roadmaps for this.” (That was one of the more important things that was said, it might be something that AI4People could consider for a future report.)
Control over infrastructure is also important to the long-term development of AI in research. There is a risk of concentration in commercial entities. There may need to be public sector investment, as both have a role. The transparency of models is important, and the need to ensure that research is reproducible. China is leading on ‘open weight’ models. Europe could do more on this. We may need approaches similar to nutrition labels that make it clear what data were used to train the model, what biases might be present, and so on.
