Digitalisation and Gender: Can Digital Technologies Help to Overcome Gender Inequalities at the Workplace?

Friday, 20 July 2018: 08:30
Oral Presentation
Stefan LÜCKING, Hans-Böckler-Stiftung, Germany
Based on recent research funded by the Hans Boeckler Foundation and in preparation of a research programme on “Digitalization and workplace democracy” this paper develops a theoretical framework to analyse the impact of digital technologies on gender inequality at the workplace. Starting point is the concept of social construction of technologies. The development and the use of digital technologies are shaped by interests, social stereotypes and antagonisms. It’s part of a multitude of social conflicts,including he struggle for gender equality. How are digital technologies shaped and used to reproduce traditional gender roles and stereotypes? How can they be used to overcome them?

The paper will discuss these questions with regard to two topics: work life balance and talent analytics. The flexibility provided by mobile devices such as smart phones, tablets or laptops is praised for its potential to enhance work life balance by dissolving the spacial and temporal frontiers between work, home life and leisure.. However in many cases such a dissolution of frontiers only leads to increased work pressure and has a clear gender bias not only resulting from inequalities at the workplace but also from an unequal sexual division of housework. In Germany many company agreements already try to address these issues. A deeper theoretical reflection may help to improve such efforts. Talent Analytics is a growing market for enterprise software. It is aimed at improving decisions about hiring, management and promotion of “human resources” via bid data analysis. Its promise is provide “objective” data in order to overcome discrimination based on prejudices or affinities with regard to gender, race, age, etc. However bad algorithm may only reproduce existing discrimination. How to avoid such effects? How to enable employees and their representatives to critically assess the impact of algorithms for Talent Analytics?