In France, the relative proportion of women among scientific and technical students is increasing — but not in the most prestigious programs in mathematics and computer science. In 2016, fewer than 10% of computer science engineering students in France were women. Villani's report on artificial intelligence reminds us that 12% of professionals of the digital industry holding technical jobs are women. The situation isn't much better in the rest of the world. Those numbers are worrying: first, we must wonder what it is about these studies and jobs that repel women; and second, we must worry about the biases the lack of diversity induces in artificial intelligence applications.

I am committed to promoting diversity and inclusion in science, technology, engineering and mathematics (STEM). In addition to social justice considerations, I strongly believe that diversity, be it of genders, sexual orientations, ethnic origins, socio-economic backgrounds, family status, religious beliefs, or physical abilities, opens us to valuable life experiences and increases our ability to ask important questions and solve problems. I also believe this is particularly important in both machine learning and the biomedical sciences, where the perspective of underrepresented people is key.

There is so much I would like to do to promote diversity in STEM, but one has to start somewhere. For me, this therefore primarily means two things:

  • Fighting unconscious biases when recruiting.
  • Promoting women in machine learning and data science, through my visibility, through outreach activities targeted towards high-school students, and through Paris Women in Machine Learning and Data Science. Paris WiMLDS is the Parisian chapter of WiMLDS. We host regular meetups where all invited speakers identify as female. People of every gender are welcome to attend! We also have a public Slack channel.

Women-in-<please fill in the blank> events

I am usually happy to be invited to talk about my research and my career at any women-in-<please fill in the blank> event. I'm also happy to talk specifically about women-in-<please fill in the blank> issues, but I'm going to get angry if all the women speaking at your event are on the women-in-<please fill in the blank> panel and none or very few of them are giving technical talks; although I'm knowledgeable about these issues and I believe my experience to be valuable, you may want to consider inviting someone who actually studies women-in-<please fill in the blank> instead, and I can tell you about machine learning, precision medicine, and other things I'm actually studying.

Are you trying to organize a gender-balanced machine learning event, but find yourself unable to find women speakers? WiML has a great list of women active in machine learning. You may also be interested in Request a Woman Scientist and, for French speakers, in Les Expertes.

You will find below a few references on diversity in STEM.

Unconscious biases

International reports and recommendations

Barriers against women in STEM

Here are many links to scientific studies on the topic, courtesy of Celeste Labedz on March 24, 2019, which I am archiving here in case Twitter loses them or something: