About the event
As data are increasingly mobilized in the service of governments and corporations, their unequal conditions of production, asymmetrical methods of application, and unequal effects on both individuals and groups have become increasingly difficult for data scientists––and others who rely on data in their work––to ignore. But it is precisely this power that makes it worth asking: “Data science by whom? Data science for whom? Data science, with whose interests in mind?”
These are some questions that emerge from what D’Ignazio and Klein call data feminism: a way of thinking about data science and its communication that is informed by the past several decades of intersectional feminist activism and critical thought. This talk will draw on insights from their collaboratively crafted book about how challenges to the male/female binary can challenge other hierarchical (and empirically wrong) classification systems; how an understanding of emotion can expand our ideas about effective data visualization; and how the concept of “invisible labor” can expose the significant human efforts required by our automated systems. Together, they show how feminist thinking be operationalized into more ethical and equitable data practices.
After the webinar, we will host Catherine and Lauren for virtual "after talk drinks". This session will be 45 minutes and try to capture the conversations that we would have over a glass of wine or ginger beer to explore the themes of their presentation. This session will be participatory, and we hope that attendees will share work they are undertaking or initiatives they know about that might be of interest to the Data Feminism community within the Turing and beyond.
If you would like to attend the after talk drinks zoom meeting, please add 1-2 sentences to your registration form.
If you would like to find out about the related ‘Women in Data Science and AI’ project at The Alan Turing Institute, a research initiative informed by intersectional feminist thought, please go to our community Hub.