Launching RSS: Data Science and Artificial Intelligence

Neil D. Lawrence

RSS Conference, Brighton

Introduction

Damned Lies

There are three types of lies: lies, damned lies and …

Damned Lies

There are three types of lies: lies, damned lies and generative AI

Why a New Journal?

The RSS has a long history of producing world-class publications, with our first journal dating back to 1838. However, we recognized a need in the rapidly evolving landscape of data science and AI.

Our Vision

Our vision for this journal is threefold:

  1. To help unify various “AI and data science” fields
  2. To combine technical details with important questions and applications
  3. To facilitate critical questions about emerging technologies

Unifying Data Science Fields

We aim to bring together high-quality papers with broad interest across:

  • Machine Learning
  • Statistics
  • Computer Vision
  • Natural Language Processing
  • Bioinformatics
  • Econometrics
  • And more

Technical Details and Important Questions

The journal will focus on papers that:

  • Present robust technical content
  • Address significant questions or concepts of broad interest
  • Demonstrate important applications

Critical Perspectives on Emerging Technologies

We encourage papers that explore:

  • Responsible Algorithms: Robustness, Fairness and Privacy of AI/ML systems
  • Reliability of data-driven solutions
  • Epistemological questions in data science

Editorial Board

We’ve assembled a distinguished editorial board led by:

  • Silvia Chiappa (Google DeepMind and UCL)
  • Sach Mukherjee (DZNE, University of Bonn, and University of Cambridge)
  • Myself, Neil Lawrence (University of Cambridge)

Editorial Board Members

Our board includes experts from various institutions:

  • Kyle Cranmer (University of Wisconsin-Madison)
  • Borja De Balle Pigem (Google DeepMind)
  • Arnaud Doucet (Google DeepMind)
  • Sandrine Dudoit (University of California, Berkeley)
  • Arnoldo Frigessi (University of Oslo)
  • Anthony Lee (University of Bristol)
  • Maria Liakata (Queen Mary University of London)

  • Nicolai Meinshausen (ETH Zürich)
  • Kevin Murphy (Google DeepMind)
  • Tom Nichols (University of Oxford)
  • Uri Shalit (Technion)
  • Isabel Valera (Saarland University)
  • Andrew Gordon Wilson (New York University)

Expertise Coverage

Our editorial board covers a wide range of expertise:

  • Machine Learning and AI: Chiappa, Lawrence, Mukherjee, Cranmer, De Balle Pigem, Murphy, Wilson
  • Statistical Methodology: Doucet, Dudoit, Lee, Meinshausen, Nichols, Frigessi
  • Biostatistics and Computational Biology: Dudoit, Frigessi, Mukherjee, Nichols
  • Natural Language Processing: Liakata
  • Neuroimaging: Nichols
  • Causal Inference: Shalit, Meinshausen, Valera, Mukherjee

Open Access and Submission Policy

RSS: Data Science and Artificial Intelligence is:

  • Fully open access
  • Following a submission policy similar to JMLR (accept submissions published at workshops or conferences, permit pre-print publication (such as arXiv))
  • Committed to rigorous peer review

Call for Papers

We invite submissions in areas including but not limited to:

  • Novel methodologies in AI and ML
  • Statistical approaches to big data
  • Ethical considerations in data science
  • Interdisciplinary applications of AI/ML

Conclusion

Thanks!

References