Data-led research, journalism, and report writing

We and AI is a Non-Governmental Organisation (NGO) set up and staffed by volunteers.Our primary mission is to increase public awareness and understanding of the risks and rewards of Artificial Intelligence in the UK. The UK is falling behind the EU which has public education programmes being rolled out. Awareness and education are vital, in order to empower people to make their voices heard about how AI should be used and controlled. We also need to encourage more people from different backgrounds to get involved with or work in AI. For example currently, only 11% of those currently building AI are female.AI can be used for great good, as we can see with the multiple ways it is being used to speed up the diagnosis of Covid-19, deliver aid to those who need it, and speed up the search for a vaccine.But it can also increase inequality and make mistakes. It reflects the human biases of the select group who build AI, and can increase the inequalities in our society. Technology has evolved so quickly that there aren’t adequate laws or systems to protect us and our rights.That is why we aim to build engagement, feedback and activism amongst the public, who are vital to accelerate and better inform the process of making AI fair for everyone. We also aim to inspire more people from different backgrounds to work in developing and making decisions on AI, crucial to helping reduce the unconscious biases which AI models can amplify.We are developing easy to understand awareness, education and inclusion programmes about the risks and rewards of AI. We aim to bridge the gap between those making decisions on how AI is used and controlled, and the wider public whose lives are being shaped by it. In highlighting how and why AI is currently being used, we aim to make the invisible visible, and therefore more accountable.

DATA FOR SCIENCE AND HEALTH: contributed to the strategic development of a new major priority area at Wellcome, which has now been approved with a budget of £75 million over 5 years. I led the workstream related with data science capacity, focussing on diversity and multidisciplinarity issues; and got involved in work related with the ethics of data and AI, public engagement and education, quality and availability of health data, open-source tool development, health data capacity in high- and limited-resources countries.

Computational studies of intrinsically disordered proteins and small-molecule drugs related with ageing and neurodegenerative diseases.

Computational modelling of the adsorption of peptides and enzymes on oxide nanoparticles to assess their biocompatibility/toxicity.

Computational studies of metal/solution interfaces in the presence of an electric field.

Department of Physics, PhD thesis: "Modelling the behaviour and self-assembly processes of small biological and aromatic molecules on metallic surfaces."