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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

Fri Dec 09 03:00 AM -- 05:00 AM (PST) @ VIP Room
Event URL: www.royalsociety.org/machine-learning »

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.

Fri 3:00 a.m. - 3:10 a.m. [iCal]
Introduction by Chair (Talk)
Peter Donnelly
Fri 3:10 a.m. - 3:40 a.m. [iCal]
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 (Talk)
Sabine Hauert
Fri 3:40 a.m. - 4:10 a.m. [iCal]
How Machine Learning Research Can Address Key Societal and Governance Issues (Talk)
Zoubin Ghahramani
Fri 4:10 a.m. - 4:30 a.m. [iCal]
Crafting a Story to Communicate Your Research to the Public Using the 'Algorithm Toolkit' (Talk)
Katherine Gorman
Fri 4:30 a.m. - 5:00 a.m. [iCal]
Extended Q&A, including Questions from Twitter: #RSmachinelearning (Panel Discussion)

Author Information

Susannah Odell (The Royal Society)
Peter Donnelly (Oxford University)
Jessica Montgomery (Royal Society)
Sabine Hauert (University of Bristol)

Sabine is Lecturer in Robotics at the University of Bristol in the UK and President and Cofounder of Robohub.org, a non-profit dedicated to connecting the robotics community to the world. Robohub has helped thousands of experts in robotics communicate about their work through podcasts, articles, and videos, that reach over a million views each year. As an expert in science communication, Sabine is often invited to discuss the future of robotics, including in the journal Nature, at the European Parliament, and as a member of the Royal Society's Working Group on Machine Learning. Sabine’s research at the Bristol Robotics Laboratory focusses in designing swarms that work in large numbers (>1000), and at small scales (<1 cm). Swarm strategies are either inspired from nature or are automatically discovered using machine learning and crowdsourcing. Before joining the University of Bristol, Sabine engineered swarms of nanoparticles for cancer treatment at MIT as a Human Frontier Science Program Cross-Disciplinary Fellow, and deployed swarms of flying robots at EPFL. She is a Royal Society Machine Learning Working Group member.

Zoubin Ghahramani (Uber and University of Cambridge)

Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.

Katherine Gorman (Talking Machines)

Katherine Gorman is a podcast producer. After a decade in public radio she helped to found the podcast Talking Machines of which she is now the co-host and executive producer. She is the head of podcasting for Collective Next, where she develops shows and solves creates communication solutions.

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