As a budding political scientist, I wrote my PhD about information technology in government in the 1990s. At the time this was a sad and lonely thing to do; neighbors in the London School of Economics dining room would cast their eyes around for a “normal” political scientist who could pass on some Westminster gossip. Then along came the internet, the first IT to be domesticated in our social, economic and political networks, followed by huge digital platforms based on processing massive quantities of data generated by human interactions, and the current AI revolution. I have a better time at dinner parties these days.

Data-intensive technologies — usually, and often inaccurately, labelled artificial intelligence — are having a profound effect on research and challenging the boundaries of the fields that feed the pipeline of technology specialists.

Those who are expert in manipulating huge data sets and complex networks — engineers, physicists and computer scientists — have traditionally trained on data relating to materials, cells, atoms, bits. When they enter the huge industry that has grown up around platforms of Facebook, Amazon, Google, most data they deal with relates to people. But the academic training ground for understanding human behavior is social science — including economics, anthropology, sociology and political science — the concepts and methods of which bore most engineers to tears.

These technologies provoke a whole raft of new ethical issues and dilemmas. They can reduce the transparency and accountability of business processes and decision-making, requiring frameworks to ensure trust. There are issues of privacy and rights connected with personal data. Machine-learning algorithms can introduce bias and discrimination. Resolving such issues requires an approach grounded in ethics and an understanding of what causes bias in the first place — traditionally the province of sociology.