We live in a time of significant global challenges. The term polycrisis has emerged as we face wide-reaching and impactful issues such as the climate, war and conflict, and health.  To address these challenges, we will need political will, global collaboration, inter- multi- and transdisciplinary approaches, systems thinking and scientific breakthroughs at scale.  Science and scientists have an important role to play as societies tackle these challenges and lay foundations for actions that will direct the course of the future.   It is possible to argue that science that engages effectively with society will depend on the sharing, uptake and accessibility of scientific knowledge and insight, but also on the way that science is designed and co-designed in ways that reflect excellence and the aspirations and expectations of societies. 

In what may be termed a Post-Covid Ongoing Stress Era (POSE), the appeal to reason and scientific knowledge, as we’ve understood these, seems all but cancelled.   

Today's fast-changing society is faced with a variety of complex large-scale societal challenges. Simultaneously, people are losing trust in both science and those studying and researching these challenges. Still, we need those scientists from all fields and backgrounds more than ever to work in inter- and multidisciplinary ways, solving these challenges and having their insights assist politicians and decision-makers. It is apparent that the scientific community has to foster trust in science. But how do we ensure that efforts to build trust in science are sustainable and that trust in science remains integrated in the community for the next decades to come? More than ever we need to engage across the generations and particularly the younger generations. 

In the 21st Century, as humanity and the Earth face crises of survival and sustainability, we must fundamentally rethink the values that science and technology aspire to, their social roles and methods, and the norms of scientists. This rethinking will likely lead to significant changes in the 200-year history of modern science, which has been institutionalized since the early 19th Century. 

“Making sense” of a particular domain at the level of an individual or a group of individuals is an act that results from a dynamic interaction between related stakeholders, influenced by their socio-economic and political environment. As such, there is room for subjective judgement and differing interpretations, as well as the potential for change over time. 

Making sense of science today requires not only a grasp of fundamental principles but also the ability to critically navigate uncertainty and complexity. Today’s challenges introduces significant diversity across key dimensions of human existence, including history, culture, politics, societies and economics. Furthermore, there is considerable diversity among scientific sub-communities regarding how we know what we know and how to design actionable solutions. The social sciences, natural sciences, engineering, and medical fields are acculturated into science in widely varying ways, often complicating communication and collaboration. Yet, most challenges we face inherently require collaboration. 

In many developing countries, the role of science in policy-making and public life is often argued, undervalued, or sidelined in favour of more immediate, pragmatic approaches. Faced with urgent challenges such as poverty, health crises, food security, and geopolitical issues, governments and communities frequently rely on diverse knowledge systems and prioritise solutions that offer quick, tangible benefits. In these contexts, science is just one of many sources of knowledge, and it is often not the most trusted or influential in shaping policy decisions. As a result, the conventional approaches to promoting trust in science and science’s societal impact need to be rethought from a perspective that acknowledges local realities and values. 

In the hyperbole about artificial intelligence, there have been claims about how AI, in the guise of large language models, can plausibly, confidently and succinctly summarise complex science, accompanied by warnings about its propensity for bias, factual inaccuracies and, most notoriously, to ‘hallucinate’ fake references. As was the case with the internet, this fabulous tool has to be used wisely if we are to get the most benefit. 

But there is a deeper problem with gazing at the world through an AI lens since, if anything, it can obscure the process of science.  

Modern society is one of fragmented logics. Business, finance, government, academia, all apply different logics in their operations. All of them make sense within their limited bubbles, all together create the conditions for self-extinction.  

Science is perceived by many as the activity allowing us to make sense of the rest, by unveiling the mysteries of how nature works and enabling human inventions changing, supposedly for good, our relationship to other humans, to nature, and to time. But science itself is a bubble operating with a partial logic. I am talking here of what makes scientists do what they do, investigate certain topics (and not others), and take decisions for their professional careers. Hyper-specialisation has become a useful tool to keep the dynamic of publication-grant-publication running, and that is what defines a good scientific career. Are we sure this logic is enough to serve societal purposes in the 21st century?