From Project Implicit. Here you will have the opportunity to assess your conscious and unconscious preferences for over 90 different topics ranging from pets to political issues, ethnic groups to sports teams, and entertainers to styles of music. At the same time, you will be assisting psychological research on thoughts and feelings.

Sessions require 10-15 minutes to complete. Each time you begin a session you will be randomly assigned to a topic. Try one or do them all! At the end of the session, you will get some information about the study and a summary of your results. We hope that you will find the experience interesting and informative.

By Caroline Criado-Perez. This book exposes the gender data gap - a gap in our knowledge that is at the root of perpetual, systemic discrimination against women, and that has created a pervasive but invisible bias with a profound effect on women's lives. Criado-Perez brings together an impressive range of case studies, stories and new research from across the world that illustrate the hidden ways in which women are forgotten, and the impact this has on their health and well-being. From government policy and medical research, to technology, workplaces, urban planning and the media, Invisible Women reveals the biased data that excludes women.

By Catherine D'Ignazio and Lauren F. Klein. In this book, D'Ignazio and Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, the authors show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, 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. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

From Criterion Institute. The success of the field of gender lens investing is dependent on the elevation of translators and translation: individuals, organizations, and processes that facilitate understanding between gender and finance expertise. The accumulation of a wider variety of voices in decision-making is needed to improve both the financial returns and the gender and social impact of the program designs in the field.

By Iris Bohnet, Siri Chilazi, Anisha Asundi and Lili Gil Valletta. Blind auditions, where musicians perform behind a curtain, helped increase the fraction of female musicians in the major US symphony orchestras from about 5 per cent in the 1970s to almost 40 per cent today. When orchestra directors couldn’t see who was playing, they based their selection decisions on the quality of the performance, rather than the personal qualities of the performer. An ingenious design intervention, the curtain and the accompanying research remind us that good people interested in maximising the quality of their product – such as orchestra directors seeking the best-sounding music – fall prey to bias.

In the world of early-stage investing, what venture capitalists (VCs) arguably care most about is the return on their investment. But they fall short of creating a level playing field for the most brilliant investing minds. Much like orchestras 50 years ago, US venture capital today is dominated by men – approximately 90 per cent of VC investors are men and roughly 88 per cent of venture dollars go to all-male founding teams. These venture capitalists have never had the benefit of the curtain to come face-to-face with how their biases affect their decision-making. They still believe in the power of meritocracy.