Stop, collaborate listen: Gender equality in social data science. Watch the panel discussion now

This is an edited extract of Katie Metzler’s introduction at our gender equality in social data science event held on Ada Lovelace Day 2018.

Social science research is changing, just as everything in the world around us is changing as a result of technology. In response to these changes, SAGE launched the SAGE Ocean initiative, which was set up to support social scientists working with big data, computational social scientists and or social data scientists (depending on which term you prefer). At a time when there is more data generated by and about human behavior than ever before, and a growing number of new tech and tools to make sense of it all, we believe that social scientists have a critical role to play in helping us to use that data to ask and answer questions that will benefit society. 

And so SAGE Ocean, in our efforts to advocate for the social scientists and to support the future of social science research, hosts events (as seen in the video below) to bring people together to network, share ideas, form new research partnerships and discuss key issues and challenges facing social scientists of the future.

Gender equality in social data science means we need to talk about gender equality in academia. We know that pay gaps can still exist between men and women in academia - and some universities have taken strong measures to correct this. Essex University, for example, took measures to close the gap in female professorial salaries by giving women professors a one-off pay lift in 2016. 

And talking about gender equality in social data science means talking about the representation of women in tech and attitudes towards women in tech. It means confronting the stubborn prejudices and perceptions that women can’t code or can’t do stats. It means having a discussion about how as this new community of thought and practice is forming, we have a chance to make it look different than the communities that came before. And in particular, it seems vital to challenge ourselves to do so because of the questions social data scientists are asking and the methods they are using - because of the danger of biased algorithms, of reinforcing inequality through policies based on big but dirty data.