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>Ditchley Foundation
EXECUTIVE SUMMARY
Context and why this is important
The intersection of machine learning and genetic engineering is speeding up development of new technologies and new applications creating transformational opportunities as well as difficult ethical challenges for society as a whole. The combination has opened doors to advances in medicine which change the way we understand health. But its potential extends beyond health in development of new materials and genetic engineering across biological organisms and in adapting biological processes. Parallel advances in robotics and automation are another force for acceleration. And, we don’t yet know what further dimension quantum computing could bring.
As the physical becomes digital, the opportunities for individuals will be life changing. Such technologies could have impact on people’s identities, personal morality and ethics. For societies, new kinds of inequalities could result, bringing fresh challenges for democratic processes. National economies will change and dual use means there are new threats to security. Autocratic states will follow different paths. And yet, questions of governance lag far behind. How social values and norms inform the ways technology develops is far from clear.
People
This conference brought together a mix of expertise including geneticists and biologists, biotechnologists, microbiologists; thinkers on philosophy, religion, law and machine learning, investors and a range of company founders.
Analysis
We felt like explorers of a whole new continent of opportunity and risk.
Presentations on genomic prediction and the significance of current applications in IVF and embryo selection quickly captured attention. Other features of the intersection of machine learning and genetic engineering, such as the understanding of human and computer interaction; uses in plants and agriculture; uses in warfare and the implications for security, were raised but issues of healthcare and the implications of embryo selection dominated because the dilemmas are already playing out and are urgent.
Society has yet to catch up. Establishing values, norms and regulation to facilitate the continued advance of the technology requires a deeper understanding of potential applications of machine learning and genetic engineering to map 2nd, 3rd and 4th order, as yet unknown, effects.
The interests of individuals wishing to select better outcomes for themselves and their offspring may well clash with the interests, longer-term, of populations as a whole. The question of the environmental impact on genetic expression would not go away. If we optimise ourselves for the environment we have now, and it then changes, what then? Are all bets off for genetic engineering if the environment changes? Do we have to learn to shape the environment as well as ourselves – which man has always sought to do?
Geography; research, applications and governance follow different tracks in different in parts of the world. What is acceptable and in fact happening already in some countries is not acceptable in others. What happens next?
Responses – not consensus, but ideas emerging
We need better maps of technological pathways and accompanying explanatory narratives. Calls were made for models, visualisations, scenarios and mapping with explorations for example of potential 2nd/3rd/4th order effects and unintended consequences. Alongside modelling, we need exploratory narratives to help develop our collective understanding of what is happening, what could happen and what the various publics in different parts of the world want to happen.
Don’t give up on multilateralism. Regulation at a multilateral, multinational level should be explored. Despite the doubt and concern over the effectiveness of multinational institutions – opportunities remain for collective frameworks for governance and regulation that reflect widely shared values. There are opportunities for the G20, WHO etc.
New approaches to data that allow use of anonymised data and support innovation, which could unlock the potential of this new field of technology and science should be a focus for research.
Focus innovation on where publics want to see change – for example, in oncology and cancer treatment.
Realise early that this is a contemporary security issue as well as a health issue.