Version 2.01 no time to cry
lecture: Data and discrimination: representing marginalised communities in data
The right to be counted, or the right to be left alone?
In the past, marginalised communities have often been left uncounted when it comes to institutional datasets. Some argue that the right to be counted is a crucial step to addressing the needs of marginalised communities; but for others, the exact opposite is true. Anonymity and not being reflected in data is exactly what some communities want, and need. In this talk, I'll discuss both sides of the argument, with concrete examples of how the different strategies have been used in various situations.
For some marginalised communities, not being represented in certain institutional datasets means their needs don't have a chance of being met. The first step for changing this, for many anti-discrimination campaigners, is to be counted and to appear in those datasets, and thus have a chance at being reflected in decision making that might stem from the data.
In the eyes of many privacy campaigners, though, the exact opposite is true. Being reflected in data might do more harm to those marginalised communities, for whom operating under the radar or anonymously might be crucial to their livelihoods.
What can we learn from the way in which different marginalised communities have been reflected in data in the past? When discriminatory decisions have been made as a result of unrepresentative datasets, at what point were the mistakes made, and how could they have been mitigated?
Start time: 11:15
Track: Ethics, Society & Politics
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