The post is based on a lecture given to researchers in human and social sciences.

Science and Its Futures

In this post, I want to discuss about how we can think about potential futures for scientific research and consider our role in shaping those futures responsibly, with a focus on the human and social sciences. The goal is to encourage critical reflection on a) the nature of science, b) the drivers of change in science, and c) how to navigate the directions of research in a changing world.

Science, whether we talk about natural, human, or social sciences, can be generally understood as constituted by three levels. These levels provide a comprehensive perspective on how we build and understand scientific knowledge:

The level of Content: This is about the subject matter of science – what science says about the phenomena it studies. It is about the theories, hypotheses, data, and factual information we gather and interpret about our world.

The level of Methodology: This is about how we build scientific knowledge. It includes the methods, procedures, and techniques we use to gather and analyze data. It is about the experimental designs, the statistical analyses, the interpretative methods, and the process of peer review and criticism that ensure the reliability and validity of our findings.

The Institutional Setting: This level considers how science is organized and understood socially and culturally. It concerns the politics and economics of science, the educational and professional institutions that shape scientific practices, the publication and funding systems that support scientific work, and the cultural values and societal attitudes that influence the direction and reception of scientific research.

Science is a dynamic process of discovery, revisions, rethinking, and even steps backwards. It is constantly being transformed through new theories, technologies, methodologies, and expanded understanding of the world. This inherent dynamism makes predicting the precise future course of any scientific field extremely difficult. How can we predict, on the basis of our current knowledge, how that very knowledge will change? However, we can develop scenarios (descriptions of future situations and paths to them) to explore plausible alternative futures. Scenario planning allows us to imagine diverse contexts and contingencies without making definitive predictions or limiting our thinking. It is a valuable tool for expanding our perspectives on what is possible going forward.

At the same time, scientific knowledge varies with respect to its robustness. The more robust some piece of knowledge is, the less likely it is to change. Moreover, science consists of layers of theories, some of which are more rigorously studied and accepted and some at the level of pursuit. Novelties won’t come out of blue but rather they probably exist already somewhere. There are limits to how much change we may expect in science. Moreover, there are diverse philosophical perspectives on how readily the fundamental theoretical frameworks, methodological assumptions, and core questions of a field can radically change over time. The degree and pace of changeability have been debated by scholars for centuries. Yet history provides many examples where shifts in societal values, control structures, and the perceived role of science within culture do profoundly transform scientific practices over decades or generations. So while the core of science has resilience, we must acknowledge forces that can dramatically reshape the trajectory of fields.

As researchers thinking about the future, we have an ethical responsibility to carefully consider how our visions and scenarios may influence present-day actions, funding priorities, and public perceptions. We want to stimulate critical, creative thought about possibilities, not inadvertently bias research directions or mislead audiences about what we can reasonably predict. Articulating possible futures in a responsible, nuanced manner is crucial. Some vision of the future inevitably guides all human choices and actions, whether we acknowledge it or not. Our task is to bring rigor, honesty, inclusiveness, and care to that visioning process.

But what if computers do the dirty work and predict the future of science? The advances in AI and machine learning technologies are profoundly influencing multiple facets of our society, including scientific research. Some researchers believe AI could be utilized to predict the future of science, either by identifying emerging trends or predicting potential breakthroughs based on analysis of existing scientific literature and data.

There are already examples of AI being used in this way. For instance, AI has been deployed to identify research trends by analyzing the frequency and co-occurrence of key terms in scientific papers over time. Similarly, AI has been used to predict successful scientific ideas, such as potential drug targets in biomedical research, based on patterns in existing data.

However, the use of AI in predicting the future of science also raises several issues. One is that AI predictions are fundamentally based on existing data and thus may be limited in their ability to anticipate truly novel ideas or paradigm shifts. Additionally, the algorithms used by AI are not transparent and can often produce results without a clear explanation, making it difficult to understand and critically assess their predictions.

What Changes Can Change Science?

The values, ideologies, and belief systems held by practitioners and people around science profoundly impact science. Carefully unpacking the sometimes implicit assumptions researchers hold about the nature of knowledge, the possibility of objectivity, and the appropriate role of science in society helps reveal forces that shape how fields evolve. When ideologies shift across a discipline, the practice of science itself is transformed. And surrounding societal ideologies also affect science, whether acknowledged or not.

Changes in values and ideologies will change science. Understanding how values and ideologies may change helps us to understand how science can change. Moreover, values are a mechanism through which many trends change science. For example, AI may change science through its changing our values (for example, by decreasing the value and need for simple and easily trackable explanation).

Relatedly, control and decision-making structures in science have important influence. Policies and priorities at the macro scale of national governments, strategies of meso-level institutions like universities, and micro-level dynamics within research groups and teams create a complex landscape that researchers must understand and navigate strategically. Changes in who controls funding, evaluations, promotions, and releasing results filter down to eventually alter practice.

Looking inward, the future health of a scientific field depends heavily on the culture it fosters internally. How does a discipline respond to theoretical problems, flaws in established methods, or new ethical concerns? Do incentives and norms promote open sharing of ideas, constructive criticism, and daring innovation? Is collaboration across disciplinary boundaries truly encouraged? Continuous self-reflection and adaptation are key to avoiding stagnation. Science does change internally.

Moreover, there knowledge of trends can be used to map the possible futures. Consider trends like interdisciplinarity, open science, digitalization, and citizen science. Each of these trends represents a significant shift in the way research is conducted and shared. Yet, we need to be a bit skeptical about them.

Interdisciplinarity encourages collaboration between different academic disciplines to address complex problems, a shift away from the traditional siloed approach to research. However, it is unclear whether interdisciplinarity is really increasing and whether there are real incentives for interdisciplinary research.

Open science, meanwhile, promotes transparency and accessibility in research. It involves making research findings, data, and methodologies publicly available, allowing for increased scrutiny and replication of studies, and encouraging greater collaboration. However, it may lead to problems like surveillance and platform dominance.

Digitalization has transformed the ways data are collected, stored, analyzed, and shared, opening up new possibilities for research. It has led to the development of new tools and methods, such as AI and machine learning, and allowed for the analysis of large datasets (big data). However, the use of AI can make replication and transparency of research problematic if we are not able to understand how the data and algorithms work.

Citizen science involves public participation and collaboration in scientific research. It can help to collect large amounts of data, increase public understanding of science, and democratize the research process. However, it also raises issues around data quality, privacy, and the ethics of volunteer involvement.

To sum up, there are several things that can give us information about how science can change.

What to Think about All This?

The prediction of the future of science comes with inherent uncertainties. Despite the advanced tools and methodologies available, there are multiple variables and unknown factors that can alter the trajectory of science. Uncertainties arise from both internal factors, such as the development of new theories or methodologies, and external factors, such as changes in societal values, political shifts, or global crises.

Risk is a closely related concept, representing the potential negative outcomes that may arise from these uncertainties. For example, the risk of scientific research being misdirected by external influences or biases, the risk of research not being properly understood or accepted by the public due to inadequate communication, or the risk of misuse of scientific findings for harmful purposes.

In navigating these uncertainties and risks, the scientific community needs to employ multiple strategies.

First, transparency and openness should be maintained in all scientific activities. This not only applies to the publishing of research results, but also to the process of research, including the planning and decision-making stages. Transparency allows for collective scrutiny, debate, and course-correction, which can mitigate risks and help navigate uncertainties.

Second, the scientific community needs to foster a culture of critical reflection and humility. Scientists should be open to questioning their own assumptions, methodologies, and conclusions, and to learning from mistakes. Humility recognizes the inherent limits of our knowledge and the possibility of change.

Third, scenario planning can be a useful tool. This approach involves envisioning different future scenarios based on various factors and exploring the potential implications of each. While it does not predict the future, it can provide insights into possible futures and help the scientific community prepare for them.

Finally, stakeholder engagement is critical. This includes not just scientists, but also policymakers, funders, public audiences, and other stakeholders. By involving a broad range of perspectives, the scientific community can gain a more comprehensive understanding of the uncertainties and risks, and collaboratively develop strategies to address them.

Next, I give a guide to research to envision the future of science and research:

Recognize Potential Trajectories

As researchers, we must acknowledge that scientific fields can follow diverse paths, ranging from deterioration to flourishing. Internal factors like research methods and external factors like public engagement shape these trajectories. We must recognize this complexity and build public trust through ethical conduct and transparent communication. Engaging broader perspectives beyond our own disciplines will also be critical for envisioning positive futures.

Embrace Proactive Approaches

Researchers play an active role in shaping the future by promoting innovation, fostering collaboration, and aligning with societal needs. We can encourage creativity by exploring unconventional approaches and embracing intellectual risk-taking. Developing strategies to capitalize on emerging opportunities will enable growth. Most importantly, we must prioritize ethical scientific conduct, from ensuring rigorous methodology to avoiding misconduct.

Prepare for Uncertainties

While the future is unpredictable, researchers can develop agility to navigate uncertainty. By anticipating challenges and opportunities, we can adopt flexible mindsets and skillsets to pivot in response to shifting realities. It is critical that we hedge against extreme risks that could compromise progress, whether biosecurity threats, environmental catastrophes, or political instability. Accepting inherent unpredictability will allow us to make incremental progress despite ambiguities.

Foster Collaboration and Communication

Scientific advancement is a collective effort requiring collaboration across disciplines and communication with diverse audiences. Researchers should foster dialogues with policymakers, the public, and international colleagues to strengthen partnerships. We must ensure accurate and accessible communication about findings and their broader implications. Training future generations and engaging underrepresented perspectives will also be key for steering science toward positive ends.