Technological disruptions in services

When our fridge broke down, at the end of last month, the job of finding a replacement was made a lot easier by the existence of websites, and significantly more interesting by the existence of augmented reality. With the former, we could gather lots of information about each fridge’s features and their availability, which helped … Continue reading Technological disruptions in services

May 2021 round-up

Lockdown eased further in May. We are now allowed to meet indoors (with some restrictions), indoor gym classes are back on, and I sat in a café for the first time in ages. And while there were many obvious signs that we are still living in a Covid world (masks, limited seating, distanced tables...), it … Continue reading May 2021 round-up

Critical science’s framework to classify the risks from AI

Artificial Intelligence has great potential, but also presents many risks, from taking over jobs, to making biased decisions. Rather than thinking about the risks of AI separately and reactively, it would be useful to have a framework to identify those risks holistically and proactively.  Shakir Mohamed, Marie-Therese Png and William Isaac suggest one such framework, … Continue reading Critical science’s framework to classify the risks from AI

We are more willing to trust tech companies with our sensitive data than the government

Contact tracing is a key mechanism for monitoring the evolution of communicable diseases. For instance, it is routinely used in the case of sexually transmitted diseases, to trace people who may have been infected, and to urge them to get tested and take precautions to avoid infecting others. Other applications include tuberculosis, measles, chicken pox, … Continue reading We are more willing to trust tech companies with our sensitive data than the government

“Gender and Money” project – Results released

This spring, I have been working on a very interesting project examining how men and women are represented with money in visual media. In this project, supported by Starling Bank, my colleague Shireen Kanji and I examined 600 images collected from the UK’s leading image banks: Getty, iStock and Shutterstock. We embarked on this project … Continue reading “Gender and Money” project – Results released

[Miscellany] Three interesting podcast episodes

I want to share with you three interesting podcasts that I came across recently. Daniel Kahneman on noisy decision making, and the need for algorithms Sandra Peter interviews psychologist / behavioural economist and Noble prize winner, Daniel Kahneman, for the Sydney Business Insights podcast. He discusses his famous book, Thinking Fast and Slow. But, most … Continue reading [Miscellany] Three interesting podcast episodes

Day in the Life of an Academic #12: Spanners in the work

I had a request for another day in the life blog post. I usually try to pick up days that illustrate different facets of the life of an academic, to help others considering this profession visualise the type of work and experiences involved; and to connect with those interacting with academics (students, administrators, research partners...). … Continue reading Day in the Life of an Academic #12: Spanners in the work

[Miscellany] Students these days

Differences between generations Now and then, when I am talking with colleagues who have been teaching in the University environment for... some time (almost 20 years, like me), one of us will end up reminiscing about the “good old days” when we would do X or Y, which worked well with students. We will wonder … Continue reading [Miscellany] Students these days

Using machine learning to identify learners at risk, and develop targeted interventions

Education is linked to higher salaries, increased job satisfaction, and better health outcomes. It prepares learners to tackle complex societal problems and can address regional skills’ gaps. Thus, being able to identify leaners at risk of not progressing on their studies, or even dropping out of their courses, is of critical importance for the learners … Continue reading Using machine learning to identify learners at risk, and develop targeted interventions