Artificial Intelligence in Science

Challenges, Opportunities and the Future of Research

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The rapid advances of artificial intelligence (AI) in recent years have led to numerous creative applications in science. Accelerating the productivity of science could be the most economically and socially valuable of all the uses of AI. Utilising AI to accelerate scientific productivity will support the ability of OECD countries to grow, innovate and meet global challenges, from climate change to new contagions.

This publication is aimed at a broad readership, including policy makers, the public, and stakeholders in all areas of science. It is written in non-technical language and gathers the perspectives of prominent researchers and practitioners. The book examines various topics, including the current, emerging, and potential future uses of AI in science, where progress is needed to better serve scientific advancements, and changes in scientific productivity.

Additionally, it explores measures to expedite the integration of AI into research in developing countries.

A distinctive contribution is the book’s examination of policies for AI in science. Policy makers and actors across research systems can do much to deepen AI’s use in science, magnifying its positive effects, while adapting to the fast-changing implications of AI for research governance.


Interpretability: Should – and can – we understand the reasoning of machine-learning systems?

Few artificial intelligence (AI) applications can do more than explain to a non-expert what they have learnt or the reasoning behind their decisions. Explanations are fundamental to understanding, but not every explanation persuades. If a doctor describes a skin lesion as possibly cancerous, her patient is likely to accept the diagnosis without asking for the doctor’s medical certificates. The same patient, though, might view with suspicion a mechanic’s estimate of several thousand US dollars for simple car repairs. If the “explainer” is non-human, an acceptable explanation may be particularly hard to obtain. This essay touches upon some challenges in the development of “explainable AI”, focusing on applications in science and medicine.


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