Policy, Science and the Convening Power of Data - Neil Lawrence

Explore the intersection of policy, science, and data in this 42-minute conference talk by Professor Neil Lawrence, DeepMind Professor of Machine Learning at The Alan Turing Institute and University of Cambridge. Delve into the lessons learned from the COVID-19 pandemic, focusing on the increased attention on data-driven policy decisions and the potential of artificial intelligence to address major scientific and social challenges. Examine the experiences of the DELVE Initiative and its efforts to apply data science to COVID-19 policy. Discover the importance of adopting open data science methods to effectively deploy data science and AI expertise in tackling real-world problems. Learn about the challenges in obtaining and utilizing data for policy questions, the role of international comparisons, and the balance between different analytical approaches. Gain insights into the principles of effective data science application, including the importance of usefulness over presenteeism and finding a middle ground between overly specific and overly cautious modeling approaches.

Intro
Data at the Heart • Use data to answer policy questions. • Make international comparisons for input. • Challenges: around getting data. Report out today!
Formal Background
Human Communication
What is Machine Learning? data + model prediction
We do not reinvent the wheel: we use existing analysis techniques and datasets wherever possible to answer questions 5. Being useful is more important than presentee-ism. We should be considerate of people who work different hours and have different commitments. 6. We steer a path between the Hedgehog (too attached to a particular model) and the Fox (always considering worst case outcome).