Our research

At the Centre for Responsible Innovation, we research and teach Responsible AI. We collaborate with businesses to develop robust practices while helping avoid ethics-washing and greenwashing. Our work has supported a wide range of organisations involved in acquiring or developing AI services—from public service providers to a B Corp retailer and an environmental sensor manufacturer. We are building a community of practice in this space and welcome businesses and organisations interested in joining us. Our upcoming work includes a paper on why Responsible AI often falls short in practice, alongside a new framework that moves beyond narrow ideas of AI literacy. We also embed our research into teaching, co-producing innovative and creative work with students, and actively researching these issues alongside them.

This project explores the social, ethical, and ecological dimensions of plant synthetic biology by engaging farmers, growers, plant biologists, and wider publics. The research runs from 2025 to 2028 and involves experimenting with governance - developing and testing new decision-making approaches, and translating learnings into resources that others can use. We’ll produce 2 documentary films and 2 other artistic commissions to engage publics in ways that enable them to shape technology trajectories. The team comprises Rob Smith (PI) and Joana Formosinho (PDRF) University of Edinburgh, Adrian Ely (CoI), University of Sussex, Sarah Hartley (CoI) and Cecilie Hilmer (PRDF) University of Exeter and partners Sophia McCully, Nuffield Council on Bioethics, Sandra Knapp, Natural History Museum and Alexandra Davet, Royal Botanic Garden Edinburgh.

Gene drive governance is about understanding how choices around this emerging technology are made—how it's funded, developed, regulated, and potentially tested or released. We’ve held several research projects that explore who makes governance decisions, whose interests they serve, and what values underpin them. Our research looks at the wider impacts of gene drive: who supports it, who challenges it, and why. Governance is deeply political. Even the language we use to describe gene drive shapes how we think about it. Who decides on these terms? What assumptions do they carry? We also ask: how are risks identified, and who gets to say which risks or benefits count? Our research digs into these power dynamics and political choices to open up space for broader reflection and more inclusive conversations about the future of gene drive technologies.

You can find a collection of outputs from the various projects on our website: Gene Drive Governance

Over recent years, digital technologies, data science and Artificial Intelligence applications have been increasingly used in environmental research to address such challenges as climate change and biodiversity loss. This trend has been called ‘AI for the Environment’ and it is part of a broader strategy of ‘AI for Science’ that aims to accelerate scientific progress by enabling the automation of tasks, improving efficiency and accuracy of research, and providing concrete solutions for people and nature. For example, digital technologies and AI could enable better modelling of catastrophic events, such as hurricanes, or support biodiversity by monitoring endangered species with the help of camera traps and sensors. These technologies are already being applied and having an impact in the real world, however, we still do not fully understand the implications of these technologies for the environment and the society, including  the affected communities and the scientists conducting this type of research.

Our project explores how the rise of data science, digital tools and AI is transforming environmental research in the UK and what social and ethical aspects might be involved. We want to understand the implications of using AI and digital tool in environmental research, especially in relation to the issues of justice, inclusion and stakeholder engagement.

Digital technologies, automation and data science are increasingly used in animal farming for a wide range of purposes that include farm management, monitoring the health and welfare of livestock animals, biosecurity governance, animal breeding and supply chain management, among others. These technologies are often presented as offering pathways to increased productivity and efficiency in farming, better welfare for animals, and solutions to grand challenges such as climate change adaptation. But their introduction into farming also holds implications for people, animals, the environment and food systems that are not yet well understood.

Our research investigates how data and digital technology are changing animal agriculture, and what this means for society and livestock animals themselves. We aim to find new ways of co-producing data and digital technologies in animal farming that are inclusive of diverse stakeholders (including the perspectives of animals themselves) and are attentive to power dynamics, the uneven distribution of risks and benefits, and the environmental effects of digital systems.

In 2023, we produced a short film showcasing our results. You can find it here.

Find out more about the project

Engineering biology has been identified as a priority technology by the UK Government, but despite the UK’s prior strength in social science of engineering biology and the important social, ethical and political questions raised by this technology, recent UK Research & Innovation (UKRI) investments appear to include little social science. Our research, commissioned by the Economic and Social Research Council (ESRC) and led by Eleanor Hadley Kershaw with Sarah Hartley, Susan Molyneux-Hodgson, and John Love, scopes the current state and future potential of social science in engineering biology in the UK; provides strategic advice to inform UKRI’s and ESRC’s future strategy and priorities for investment; helps mobilise the UK social science community towards a new phase of research in this area and strengthen interdisciplinary capacity; and contributes to building the evidence base for the value of social science.