Technological advances change not only what we can learn as scientists, but also how science is conducted. Here we explore how automation and outsourcing are affecting the act of doing science.
Artificial intelligence tools expand scientists’ impact but contract science’s focus
Nature - Artificial intelligence boosts individual scientists’ output, citations and career progression, but collectively narrows research diversity and reduces collaboration, concentrating...
Tech companies are cutting jobs and betting on AI. The payoff is far from guaranteed
AI experts say we’re living in an experiment that may fundamentally change the model of work
What technology takes from us – and how to take it back | Rebecca Solnit
The long read: Decisions outsourced, chatbots for friends, the natural world an afterthought: Silicon Valley is giving us life void of connection. There is a way out – but it’s going to take collective effort
Science Must Decentralize
Knowledge production doesn’t happen in a vacuum. Every great scientific breakthrough is built on prior work, and an ongoing exchange with peers in the field. That’s why we need to address the threat
The Current Crisis: What's Happening to Science in America
Care at the Edge of Automation – Topos Institute
Technologies don’t just solve problems, they change us. We invent technologies, and they invent us in turn, shaping our lives and worlds. This is the phenomenon that Terry Winograd and Fernando Flores, in Understanding Computers and Cognition (1986), called “ontological design.” It matters now more than ever—along with a second lesson they saw clearly. Technological research is always guided (and sometimes misguided) by deep ontological assumptions about, e.g., the nature of cognition, agency, and communication. If we are to create technologies that truly serve human flourishing and care, we must bring these hidden assumptions into the open and question them at their roots.
Artificial intelligence and illusions of understanding in scientific research
Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.
How AI Agents are transforming scientific discovery
AI agents are starting to reshape science, from proposing novel hypotheses to writing code. Learn what comes next for scientists and policymakers.
The Whale and the Reactor: A Search for Limits in an Age of High Technology, Second Edition
“In an age in which the inexhaustible power of scientific technology makes all things possible, it remains to be seen where we will draw the line, where we will be able to say, here are possibilities that wisdom suggest we avoid.” First published to great acclaim in 1988, Langdon Winner’s groundbreaking exploration of the political, social, and philosophical implications of technology is timelier than ever. He demonstrates that choices about the kinds of technical systems we build and use are actually choices about who we want to be and what kind of world we want to create—technical decisions are political decisions, and they involve profound choices about power, liberty, order, and justice. A seminal text in the history and philosophy of science, this new edition includes a new chapter, preface, and postscript by the author.
AI, peer review and the human activity of science
When researchers cede their scientific judgement to machines, we lose something important.
The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale
Sir Isaac Newton famously wrote, “If I have seen further, it is by standing on the shoulders of giants”. Scientific contributions are rarely developed in isolation, but build upon prior contributions, such as problem framings, experimental methods, and empirical findings. Understanding these prerequisite relationships is important for studying scientific progress, and for automated scientific discovery systems that must reason about which existing capabilities can be used to develop new ones (e.g. Lu et al., 2024; Jansen et al., 2025b; Baek et al., 2025).
From Genius Science to Scenius Science - Cosmik Labs
Why personal AI tools might not be all we need to revolutionize science
Science Works
Science Works is an independent research and policy studio tackling the biggest problems facing UK science and technology.
Science in the Digital Era
How does scientific research adapt to digital media and automated information processing? What are the opportunities, what are the risks?
Redressing the Balance: A Yin-Yang Perspective on Information Technology
Improving Science That Uses Code
Computational reproducibility (by Konrad Hinsen) — Semble
AI mediated science (by Ronen Tamari) — Semble
Establishing trust in automated reasoning - MetaROR
Why do we do astrophysics?
