DNA.I.: Early findings and emerging questions on the use of AI in genomics (Ada Lovelace Institute, 2023) by Henry Farmer comments
In recent years, the scientific fields of artificial intelligence (AI) and genomics have experienced increased public attention and investment by public and private institutions. The UK Government, for example, has made explicit plans to become ‘the most advanced genomic healthcare system in the world’, and lists AI as one of five ‘critical technologies’ that can make the country a scientific superpower.
Both AI and genomics have already been used to address major scientific challenges, including genomic sequencing to identify novel COVID-19 variants and the use of AI and machine learning to predict the structure of proteins. But both fields have also resulted in controversies over their ethical and societal implications, and raised a host of difficult issues for those looking to regulate, direct and govern their development and use.
In genomics, recent debates about acceptable uses of CRISPR-Cas9 have raised concerns around the ethics of genetic engineering. Similarly, the field of AI has recently experienced an increasingly intense public conversation about the ethical and societal implications of foundation models, powerful AI systems capable of a wide range of general tasks.
AI and genomics are also becoming progressively more intertwined. Many recent advances in genomics have been made possible by the use of AI, and AI research and product teams have increasingly sought to use genomic data to create AI-powered genomics research and products. Economic forecasts have suggested the market for AI and genomics technologies could reach more than £19.5 billion by 2030, up from half a billion in 2021.
The increasing convergence of AI and genomics is set to present policymakers with a new set of practical and theoretical challenges. Considered separately, developments in AI and in genomics already pose deep questions concerning agency, privacy, quality, bias and power. Considered in relation to one another, the issues posed by the two technologies become harder to predict, more complex and more numerous.
While there has been much research considering the ethical impacts of AI and genomics as separate technologies, comparatively little attention has been paid to exploring the broader implications of the two technologies when used together, and from a structural perspective. For policymakers seeking to navigate and regulate AI and genomics, this is a critical evidence gap.
AI and genomics futures is a joint project between the Ada Lovelace Institute and the Nuffield Council on Bioethics that investigates the ethical and political economy issues arising from the application of AI to genomics – which we refer to throughout this report as AI-powered genomics. This report of our early findings sets out the results of our research, its significance for policymakers, and the specific topics and questions we will focus on.
Our research shows that:
• AI-powered genomics has seen significant growth in the past decade, driven principally by advances in machine learning and deep learning, and has developed into a distinctive, specialised field.
• Private-sector investment in companies working on AI-powered genomics has been substantial – and has mainly gone to companies working on data collection, drug discovery and precision medicine.
• The most prominent current and emerging themes in research on AI- powered genomics relate to proteins and drug development, and the prediction of phenotypic traits from genomic data.
Moreover: implications of AI-powered genomic health prediction
• The specific combination of emerging themes and capabilities identified in AI-powered genomics points to the increasing viability of two broad techniques within healthcare over the next five to ten years:
— AI-powered genomic health personalisation: the ability to understand how treatment for the same health condition might vary between different people on the basis of genomic variations, and to tailor and adapt treatments accordingly.
— AI-powered genomic health prediction: the use of genomic data to estimate the probability of different people developing particular health conditions, responding well or badly to particular medicines or treatments, or being affected by lifestyle factors.
• The potential emergence of these techniques raises profound, urgent ethical, legal and policy questions.
• While some of these issues are already discussed and accounted for in existing legal, ethical and policy discourse, there are many questions concerning the macro-level impacts of developments in AI-powered genomics that have yet to be adequately explored.
• In particular, there is an urgent, relatively unmet need for sustained thinking and research on the structural, political, and economic implications of AI-powered genomic health prediction, and how its development might be steered and governed in line with public values and priorities.
