Research
My research is on sociotechnical systems that seamlessly combine artificial, human, and collective intelligence. I’m interested in emergent and augmented intelligence as we often experience it on open, decentralised digital platforms, like on social networks, and in online communities and open-innovation ecosystems.
I work on mechanisms, algorithms, and tools to describe and anticipate qualities of technical artifacts within such systems, and the links to the social environment in which they were created. In the past 15 years, I’ve worked on these topics in the context of smart cities (building human-in-the-loop smart transport infrastructures), citizen science (understanding motivations, incentives, and behaviours of volunteers) and knowledge communities (developing methods to assess the quality of knowledge graphs as a function of their social fabric). The systems I build and study are primarily digital, so many of the research methods I work with use digital traces of such platforms as a means to understand behaviour, design interventions, and suggest areas of improvement.
I believe data has a tremendous innovation potential, and an open approach to sharing and using it can help people and organisations collaborate and make decisions more effectively. I work together with industry and government from the UK and Europe to develop open innovation programs, incubators, and accelerators that use public and private data to create value in an economic, social, and environmental sense. I am also doing research in human-data interaction to understand how people engage with data and design better tools to find and make sense of data online. This line of research has taken me into arts-inspired approaches to data and technology engagement, as in the European programme MediaFutures.
I am excited about the applications of large language models and other similar AI technologies if used in a thoughtful way. They could change all areas I’ve researched in, from writing code and working with data to accessing and sharing knowledge. In ERC Marie Curie ITN WDAqua (2014 – 2019) and Cleopatra (2019 – 2023) I explored how people with various levels of AI literacy could use machine learning effectively and designed algorithms to improve the quality of data in knowledge graphs using content and collaborative features. Using qualitative and computational methods I demonstrated the social fabric of quality in collaborative knowledge graphs and explored the impact of large-language models on editor engagement. This line of research continues today in the RAI UK Keystone project PHAWM (2024 – 2028), which will allow editors to shape the datasets and AI algorithms used in generating Wikipedia articles in under-resourced languages, as well as the Hans Fischer Senior Fellowship (2023 – 2026), where I focus on making knowledge graphs (and knowledge graph practices) use AI responsibly.