How can scientific advances – across AI, genetic technology, and more – be harnessed to improve our quality of life? And how can we support these advances in a way that benefits all in society?
The fourteenth annual STS Forum, held in Kyoto, Japan from 1 to 3 October 2017, provided a platform for leading scientists, policymakers, and industrialists to explore the ‘lights and shadows’ of advances in science and technology. In sessions on the societal impact of innovation, genome engineering, and big data, the Royal Society contributed to this international dialogue on science and society, exploring the opportunities and challenges presented by emerging technologies. This blogpost gives a flavour of discussions.
Lights and shadows
In areas from renewable energy to advanced medicine and AI, conference attendees discussed how technological advances can help address global challenges.
In a session about big data, Professor Peter Donnelly highlighted the significant opportunities for advances in healthcare as the cost of genetic sequencing decreases, the number of people who have their genomes sequenced increases, and new analytical techniques such as machine learning allow this data to be analysed in new ways. In this context, machine learning offers the opportunity to gain new insights into human biology, improve drug development processes, and create new diagnostic tools or treatments for many illnesses.
Drawing from the findings of the Society’s machine learning working group, which he chaired, Professor Donnelly called for careful stewardship as machine learning develops. Such stewardship would create the conditions for machine learning to drive potentially transformative change in healthcare, education, finance, and more – through an amenable data environment, building skills at all levels, and support for business and advanced research – while also managing potential disruption, through broad societal dialogue about the possible opportunities and risks, and research in areas of societal importance.
A speech by Professor Sir Venki Ramakrishnan, the Royal Society’s President, considered the nature and type of disruption that could be brought by emerging technologies, including AI, in further detail. Previous major waves of technological change, including the industrial revolution, the use of electricity, and the development of electronics, have been characterised by productivity increases. In each case, there have been benefits arising from increased living standards and wellbeing, as well as substantial financial benefits to a small subset of individuals or corporations. There have also been changes in the work environment with some jobs or sectors being lost, or substantially changed. Sir Venki considered what we can learn from these previous periods of technological change, asking, if AI offers the promise of further productivity increases, how can we ensure that these benefits are shared across society? And how should we address similar questions about equality across areas of technological change?
Science and technology in society
The importance of effective dialogue across society was a theme across the Society’s sessions.
In a discussion about genome engineering, Professor Ottoline Leyser noted that key questions from the public about genetic technologies were often about how the technology was being put to use, who was using it, and who would benefit from its use. To contribute to this wider societal debate about genetic technologies, The Royal Society has commissioned a public dialogue in the UK to explore the range of views that individuals hold concerning which potential applications for genetic technologies should be developed, why, and under what conditions.
Similar questions were explored by Professor Donnelly, who introduced the results of the Society’s public dialogue on machine learning. This dialogue exercise showed that the public do not have a single view on machine learning. Attitudes towards this technology – whether positive or negative – depend on the circumstances in which it is being used. The nature or extent of public concerns, and the perception of potential opportunities, are linked to the application being considered: people’s views on particular applications of machine learning were often affected by their perception of who was developing the technology, and who would benefit. They were, for example, almost uniformly positive about the potential for machine learning to help doctors, and hence improve healthcare.
Together, these sessions asked not only what we can do as a result of technological advances, but also what we should do with these new capabilities, and how we can ensure that the benefits arising from them are spread as widely as possible.