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Workshop
Fri Dec 9th 12:00 -- 02:00 PM @ VIP Room
People and machines: Public views on machine learning, and what this means for machine learning researchers
Susannah Odell · Peter Donnelly · Jessica Montgomery · Sabine Hauert · Zoubin Ghahramani · Katherine Gorman





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The Royal Society is currently carrying out a major programme of work on machine learning, to assess its potential over the next 5-10 years, barriers to realising that potential, and the legal, ethical, social and scientific questions which arise as machine learning becomes more pervasive.
As part of this work, the Royal Society has carried out a public dialogue exercise to explore public awareness of, and attitudes towards, machine learning and its applications. The results of this work illustrate some of the key questions people have about machine learning; about why it is used, for what purpose, and with what pattern of benefits and disbenefits. It draws attention to the need to enable informed public debate that engages with specific applications.

In addition, machine learning is put to use in a range of different applications, it reframes existing social and ethical challenges, such as those relating to privacy and stereotyping, and also creates new challenges, such as interpretability, robustness and human-machine interaction. Many of these form the basis of active and stimulating areas of research, which can both move forward the field of machine learning and help address key governance issues.

The UK’s experience with other emerging technologies shows that it is possible to create arrangements that enable a robust public consensus on the safe and valuable use of even the most potentially contentious technologies. An effective dialogue process with the public can help to create these arrangements. From Twitter to Ted Talks, machine learning researchers have a range of ways in which they can engage with the public, and take an active role in public discussions about this technology. Yet, much of what the public hears about machine learning from the media focuses on accidents involving autonomous machines, or fears about labour market changes caused by direct substitution of people for machines.

This lunchtime session will present new research on the public’s view of machine learning, alongside a discussion of how research can help address some of the broader social challenges associated with machine learning.

Speakers: Dr Sabine Hauert speak about the Royal Society's recent public dialogues on machine learning and why it is important to engage with the public. Professor Zoubin Ghahramani will then explore the role of machine learning research in addressing areas of social concern, such as transparency and interpretability. Katherine Gorman will then discuss tools for communicating research to the public.

Lunch will be provided for attendees.

12:00 PM Introduction by Chair
Peter Donnelly
12:10 PM Understanding the Public's Views of Social Benefit and Social Risk: Lessons from the Royal Society's Public Dialogue and What This Means for Science Communication
Sabine Hauert
12:40 PM How Machine Learning Research Can Address Key Societal and Governance Issues
Zoubin Ghahramani
01:10 PM Crafting a Story to Communicate Your Research to the Public Using the 'Algorithm Toolkit'
Katherine Gorman
01:30 PM Extended Q&A, including Questions from Twitter: #RSmachinelearning