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Showing posts with the label david cameron

Blog: Can robots help tackle loneliness?

Can robots help tackle loneliness? Dr Dave Cameron It was announced this week that Robots to be used in UK care homes to help reduce loneliness . Dr Dave Cameron gives his thoughts on robots tackling loneliness. Photo by  Alex Knight  from  Pexels What do you make of “companion robots”? Do you think they really could be the solution for curing loneliness in old age? Companion robots have a real potential as a research tool to better understand how people interact with each other and how social interactions progress. Their use in supporting people who are lonely is admirable but a lot of the ground work still needs to be done. I don’t think loneliness in old age could be solved just by using companionship robots. Companion robots may help though: they can provide entertainment, comfort, or distraction from loneliness, similar to other media, but not tackle the issue itself. They can offer a simulation of a relationship but that experience may be closer to the social connec...

New Paper: Integrating FATE/Critical Data Studies into Data Science Curricula: Where are we going and how do we get there?

New Paper: Integrating FATE/Critical Data Studies into Data Science Curricula: Where are we going and how do we get there? A number of Information School academics have published a new paper for the ACM Conference on Fairness, Accountability, and Transparency. The paper (published by Dr Jo Bates, Dr David Cameron, Dr Alessandro Checco, Professor Paul Clough, Dr Frank Hopfgartner, Dr Suvodeep Mazumdar, Dr Laura Sbaffi, Dr Peter Stordy, and Dr Antonio de la Vega de León) reflects on the ethical and practical aspects of teaching critical data science to inform future data practices & make them fair.  Abstract:  There have been multiple calls for integrating topics related to fairness, accountability, transparency, ethics (FATE) and social justice into Data Science curricula, but little exploration of how this might work in practice. This paper presents the findings of a collaborative autoethnography (CAE) engaged in by a MSc Data Science teaching team b...