Annie Flynn from the Royal Statistical Society introduces the launch of a new open access journal, RSS: Data Science and AI, and the relaunch of the digital platform Real World Data Science.
In today’s world, where data influences decisions in every area of life from public health to consumer tech, and AI technologies are advancing at a remarkable pace, clarity and trust are more essential than ever.
From targeted advertising algorithms to financial risk modelling, data science is embedded in everyday life—but it’s not always clear who is steering the conversation.
Amid the noise, the Royal Statistical Society is stepping forward with two initiatives that reflect both the urgency and opportunity of our times: the launch of a new open access journal, RSS: Data Science and AI, and the relaunch of our digital platform Real World Data Science.
Whether you’re developing new methods, reflecting on the societal impact of data-driven systems or responding to a headline-grabbing breakthrough, our publications offer a space to share your insights.
Champions of rigour
Founded in 1834, the RSS is a world-leading learned society for statistics, but our mission reaches beyond statistical theory.
For nearly two centuries, the society has championed sound evidence, rigorous methodology, and ethical data use, and our membership includes professional statisticians, data scientists, policy-makers, educators, and curious citizens who believe in the power of data for public good.
Since 1838, our portfolio of world class journals has upheld rigorous peer review standards to ensure the publication of innovative and robust statistical research.
As data science has grown into a distinct and dynamic field, the RSS has adapted and expanded to meet it, offering training, implementing professional standards, and convening communities of practice that span academia, industry, and government.
Crucially, it’s a society not just for data professionals, but one that helps shape the standards and structures that make data science trustworthy and impactful.
Real World Data Science
Real World Data Science (RWDS) is a collaboration between RSS and the American Statistical Association, created to meet the pace and complexity of today’s data-driven world with our trademark steadying presence.
As the field of data science evolves rapidly – with new tools, controversies, and breakthroughs emerging almost daily – Real World Data Science offers a space for timely, thoughtful responses grounded in expertise and reviewed by a distinguished editorial board.
It bridges the gap between rigorous analysis and real-time relevance, providing tutorials, case studies, and think-pieces that speak directly to current events and trends. Whether unpacking the implications of a high-profile algorithm failure or spotlighting innovative practice in the public sector, the platform is practical, credible and unafraid to ask the hard questions.
Having just relaunched a new editorial phase of the site, we are now calling for submissions. See our editor’s letter and call for submissions for full details of the content we are looking for.
RSS: Data Science and Artificial Intelligence
While RWDS provides an exciting opportunity for us to share emerging ideas and foster community engagement within the Data Science world, we feel that advancing foundational theory and validated methodology requires the depth and permanence offered by more traditional academic publishing.
For this reason, we believe in the urgent need for a journal of record – run by a learned society divorced from the influence of Big Tech – to house the body of work generated during this historic moment.
As a home for best-in-class papers unifying the rigour of statistics with the applications of machine learning and AI, our new journal operates as a source-of-truth for the field, enshrining our collective knowledge. The journal’s editorial board brings a range of expertise from machine learning and AI, statistical methodology and bio statistics to computational biology, natural language processing, neuroimaging and causal inference.
As with every journal in our portfolio, a meticulous peer-review process ensures that only the highest quality research makes its way to publication.
From the journal’s editorial statement:
“RSS: Data Science & AI aims to bring together the various data sciences and AI fields within a single, inclusive journal. Our ambition is to attract outstanding research in these fields, including work that would particularly benefit from reaching a broad audience as well as interdisciplinary research that may not fit easily within existing boundaries.
We believe this will catalyse effective diffusion of ideas between these fields and at the same time contribute to building a truly pan-data science community.”
You can see our call for submissions here.
Rising to the moment
At the annual RSS conference in September last year, the journal’s editor-in-chief Neil Lawrence spoke powerfully about the dangers of digitising decision-making without a proper understanding of the downstream effects:
“We’re in a moment where statistics – the science of state – is being pushed aside. A moment where various think tanks are telling us we should revolutionise the entire state on the back of AI, on the back of a poorly understood technology.
If you want to understand what the consequences of this would be without rigorous grounding and understanding, you only have to look at the Horizon Scandal …
If you think about the wholesale replacement of decision making by AI systems, removing the context that can only come from the human within our state decision-making mechanisms, you’re talking about sowing the seeds of 10,000 Horizon scandals.”
It’s clear we’re at a pivotal moment in the story of data science.
Together, Data Science and AI and Real World Data Science allow the RSS to meet the unique challenges of our time, providing both a lasting record of high-impact research, and a responsive space for expert insight into the fast-moving developments shaping the field.
The choices we make now will shape how data and AI serve society for years to come.
If you’re working on the front lines of these changes, whether through research, practice, or critical reflection, we invite you to share your insights and help us build a future for data science that is thoughtful, transparent and grounded in real world understanding.
Respond to our call for submissions.
About the author
Annie Flynn is Head of Content at the Royal Statistical Society where she helps the society’s vital work across policy, advocacy and public understanding reach the audiences who need it most.
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