SSP Seminar Series: Researching Inequalities Network Workshop One: Methodological Innovations and Challenges in Researching Inequalities
- Date: Wednesday 7 November 2018, 12:00 – 13:30
- Location: 12.21/25 Social Sciences Building
- Cost: Free
Les Monaghan and Dr Arpita Bhattacharjee deliver talks as part of the SSP seminar series on methodological innovation in researching inequalities. Les will discuss photography and Arpita tackles data.
“Relative Poverty” and “Aspirations”: Photography as a Medium through which to Research and Debate inequality.
Les Monaghan, Photographer
Abstract: Les Monaghan will reflect on two of his most recent photography projects to explore how visual representations of people and places can offer insights into how we make sense of and justify inequalities in public and political life.
Inequalities: What Data Can and Cannot Tell Us
Dr Arpita Bhattacharjee, Leeds University Business School
Abstract: We stand in the day and age of big data. Academics, governments, and industry practitioners are increasingly relying on data to inform research, policy, strategies – essentially decision-making – and to predict the outcomes of these decisions. Innovations in machine learning and artificial intelligence have made possible delegation of many tasks and decisions to data-driven algorithms. The lack of human bias or error is championed by some as the stepping stone to a more fair and equal society. A potential caveat is – if machines learn from data and the data represents the world as it is, what are the chances of replicating and possibly exacerbating the inequalities that are true of our society and hence ubiquitous in the data? AI can eliminate human bias in processing the data but unless programmed otherwise, it will not undo the existing inequalities reflected in data. This project attempts to combine empirical investigation into social inequalities along with an inquiry of individual behaviour and underlying preferences that can often further strengthen oppressive structures, to put forth an understanding of how the increasing prevalence of machine learning and artificial intelligence has to be carefully navigated so as to not amplify social divisions.