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Machine learning
Data management and use: Governance in the 21st century - a British Academy and Royal Society project
Dynamics of data science skills
Data and AI
Overview
Data and digital technologies are transforming the way we live, work and learn. The Royal Society’s Data Programme is developing policy and promoting debate that helps the UK safely and rapidly realise the growing benefits of data science and digital technologies.
Read moreReports and publications
Read or download all our reports, publications and consultation responses.
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Digital technology and the planet
This report sets out how digital technologies can help achieve net zero, both by enabling emissions reduction across sectors and by ensuring tech itself is low carbon.
Digital disruption
The digital disruption programme explores the ways individuals, communities, and societies can best negotiate the transformations brought about by data and digital technologies.
Artificial intelligence
The Royal Society’s AI programme explores the frontiers of AI technologies, and their implications for individuals, communities, and societies.
Our analysis and advice
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- Machine learning: what do the public think?
- Can machines really learn?
- Privacy Enhancing Technologies
- AI narratives
- Data ownership, rights and controls
- Data management and use: Governance in the 21st Century
- Flourishing in a data-enabled society
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Machine learning: what do the public think?
A summary of the findings from a public dialogue exercise on machine learning, carried out during 2016 in conjunction with Ipsos MORI, as a key part of the Royal Society machine learning programme of work.
Read our summary -
Machine learning: can machines really learn?
An interactive infographic about machine learning.
Explore the infographic -
Privacy Enhancing Technologies
How can technologies help organisations and individuals protect data and, at the same time, unlock opportunities for data access and use?
Read the report -
AI narratives
The AI narratives project has examined which narratives influence public debates about AI, and how these portrayals might shape public perceptions of the capabilities, risks, and benefits of AI technologies.
Read the report -
Data ownership, rights and controls
A report from discussions at a British Academy, Royal Society and techUK seminar on 3 October 2018.
Read the report -
Data management and use: Governance in the 21st Century
A review on the needs of a 21st century data governance system by the Royal Society and the British Academy.
Read our report
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Flourishing in a data-enabled society
A discussion paper calling for a coordinated approach across disciplines and sectors to ensure data and digital technologies support human flourishing.
Read the report
Also of interest
Fellows working in this area
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Sir Peter Donnelly FMedSci FRS
Elected 2006
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Sir Peter Donnelly FMedSci FRS
- Fellow
- Elected:2006
- Chief Executive Officer, Genomics plc
Professor Peter Donnelly
Peter Donnelly is Professor of Statistical Science and Director of the Wellcome Trust Centre for Human Genetics at the University of Oxford. A major focus of Peter’s research work is the development and appli¬cation of sophisticated statistical methods for the analysis of the vast datasets arising in modern genetic studies. Peter is the Chair of the Royal Society’s Machine Learning Working Group. He is CEO and founder of Genomics plc, a company developing and applying advanced analysis and algorithms to large datasets linking genetic variation with human traits and diseases to understand human biology, and improve human health and healthcare.
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Dame Wendy Hall DBE FREng FRS
Elected 2009
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Dame Wendy Hall DBE FREng FRS
- Fellow
- Elected:2009
Professor Dame Wendy Hall
Wendy Hall is a leading computer scientist who has made significant contributions to our understanding of the interactions between humans and large networks of computers, such as the internet. Consequently, Wendy is playing a prominent role in the development of web science as an emerging discipline.
Wendy Hall is a member of the Royal Society and British Academy Data Governance Working Group. She co-led a review on artificial intelligence for HM Government.
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