Trends and challenges in science: A philosophy of science perspective

Challenges for the communication to come

• The fragility of the international research ecosystem, driven by the prioritisation of quantity over quality, the dominance of a small number of over-represented actors (mainly private and public actors in the Global North), and their poorly diversified research practices.
• The difficulty of maintaining diverse research ecosystems. Diversity is crucial for resilience in the face of unexpected change and plays a key role in science diplomacy. For policymakers, it is essential to “cultivate informed and stable agreements (nationally and internationally) about what scientific and social aims to pursue and prioritise.” This also requires actively cultivating international scientific cooperation – not only to advance science, but also to promote peaceful relations and reciprocal trust among nation-states and other international stakeholders.
• The implementation of Open Science principles, which seek to promote “universal access to research processes and findings” but remains constrained by practical barriers, including inequalities in access to material and human resources.
• The disruption of the geopolitical landscape has put pressure on the principles of universality and transboundary cooperation established by science diplomacy in the post-Second World War period. At a time of rapid scientific and technological change, the weakening of international scientific cooperation risks making policy responses reactive rather than proactive, thus limiting their ability to steer technological innovation towards the common good.
• The need to strengthen transdisciplinary and situated approaches to research, in order to better account for the diversity of social contexts in which science is embedded and for the varied social needs and values of the publics it seeks to serve.

The authors also examine how these crosscutting issues shape both challenges and opportunities in specific domains of innovation in science. One of the most complex and fast-evolving areas is Artificial Intelligence (AI), whose diffusion has increased exponentially in the past few years, raising concerns especially about its social and environmental impacts. Its growing role in research, in particular, raises critical questions: among these, how to ensure equitable access to powerful AI tools in a context of unequal connectivity (especially in some regions of the Global South), limited material resources, and uneven infrastructures; and how to guarantee the safety and transparency of AI systems, whose opacity can undermine the quality of scientific results and erode trust in science more broadly.

More importantly, one of the most significant anticipated disruptions of AI is its role as an indirect threat to democracy. AI systems provide rapid access to vast quantities of information without filtering for quality. As Leonelli and Williams note, this “significantly change[s] the knowledge environment in which citizens must operate” by reshaping “how questions are framed, how answers are provided and how evidence is used.” In the absence of transparency about how information is selected and synthesized, users – whether scientists or citizens – must be able to evaluate and choose the most relevant and reliable information available. This evaluative process thus becomes “a much more politically significant decision,” as it actively contributes to shaping public discourse against the quantity of conflicting information generated by AI systems.

In this unprecedented and challenging context, science communication and science journalism play an increasingly central social role by providing accurate fact-checking and helping to counter the spread of misinformation. A related strategy to counter misinformation spreading that journalists, citizens, and  policymakers can employ is ‘narrative-checking.’ Since misinformation often stems not from inaccurate or false information alone but from how information is framed and narrated, it is essential to pay attention to how stories are constructed and adapted to the social needs and values of different audiences. This means highlighting “the variety of ways in which meaning is attributed to data, given the specific scientific and social circumstances of various publics.”