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11 February 2022 is International Day of Women and Girls in Science; a day dedicated to helping ensure women and girls are encouraged and able to contribute and benefit from the fields of science, technology, engineering and mathematics. Here DataLab policy lead, Jess Morley, discusses the challenges involved in closing the gap in representation and reward for women working in these fields, and what the DataLab are trying to do to help lower some of the associated barriers.

Jessica Morley photo
International Day of Women and Girls in Science, Jessica Morley

11 February 2022 is International Day of Women and Girls in Science; a day dedicated to helping ensure women and girls are encouraged and able to contribute and benefit from the fields of science, technology, engineering and mathematics. Here DataLab policy lead, Jess Morley, discusses the challenges involved in closing the gap in representation and reward for women working in these fields, and what the DataLab are trying to do to help lower some of the associated barriers.

The history of science, technology, engineering and mathematics (STEM) is littered with the achievements of high-profile women who, over the years, have made some of the most significant contributions to the field; Marie Curie, Virginia Apgar, Rosalind Franklin, Florence Nightingale, Gertrude Mary Cox, Enid Charles, Elizabeth Scott, Katherine Johnson, Dorothy Vaughn, Mary Jackson, Ada Lovelace, Margaret Hamilton, Marjorie Lee Browne, Maryam Mirzakhani, Dame Mary Lucy Cartwright, Stella Cunliffe, Grace Wahba, Marlyn Meltzer, Kathleen McNulty, Frances Allen, and many more. Yet, women remain woefully underrepresented and under-rewarded in the field today.

According to data from the Office for National Statistics, in the period October 2020 to September 2021, women accounted for: 21% of information technology and telecommunications professionals; 24% of science, research, engineering and technology professionals; and 30% of science, engineering and technology associate professionals. These individuals are paid on average 20% less if they work in universities; 19% less if they work in technical fields, and 15% less if they work in other research or experimental design jobs. More globally, estimates vary due to differences in definitions, but women consistently account for less than a third of those employed as data scientists, and over the last 120 years just 6% (58/943) of Nobel prize winners have been women. Perhaps even more concerningly, these data only represent cisgender and mostly white women. The attainment gaps for women of colour, and indiviudals who identify as transgender, non-binary, genderfluid, gender neutral, or agender, are likely to be even wider but such data is rarely captured.

Fortunately the world is, albeit slowly, waking up to the fact that the pervasiveness of this gender gap is a significant problem. Thanks, in large part, to the tireless work of individuals such as Caroline Criado-Perez, Timnit Gebru, and Safiya Noble, it is increasingly recognised that the striking lack of diversity in the STEM sector is not ‘just’ a parity issue, but a technical issue. A lack of diversity leads to poorer quality research, poorer quality analytics, poorer quality products and, ultimately, greater social harms. Data is very rarely (if ever) truly ‘raw’ and technology products are not neutral. Instead, data are collected, processed and analysed, whilst technology products are designed, developed, and deployed, in specific sociocultural contexts defined by the make-up of the teams responsible. Consequently, a lack of representation can result in issues including, data bias, misinterpretation, or, even more fundamentally a misunderstanding of user needs. In safety-critical sectors, like medicine, these - and other - issues can result in problems that potentially put end-users at risk, such as diagnostic algorithms that are less accurate for specific sub-groups of the population; wearables that underestimate activity levels for women, or heart rate sensors that underperform when used by individuals with darker skin.

As awareness of these issues has increased, so has the number of initiatives designed to help close the gender gap. Nationally, notable examples include Code First Girls in the UK or Kode with Klossy in the US. Internationally, last year saw UN Women launch the Action Coalition on Technology and Innovation with the aim of doubling the proportion of women working in technology and innovation by 2026 to “ensure that women and girls participate fully in finding solutions to the largest and most complex problems of our lives.” This is an ambitious target, and one that will be challenging to meet. The lack of diversity in STEM is a systemic issue, meaning its root causes are multifaceted and complex. It will only be achievable, therefore, if all individuals, organisations and institutions already engaged in STEM-related activities play their part - and that includes us in the DataLab.

There are already a number of excellent women in the DataLab team, including data scientists Millie Green, Lisa Hopcroft and Robin Park; researchers Helen Curtis, Orla Mcdonald, Anna Rowan, Rose Higgins, Christine Cunningham and Linda Nab; and software developers Becky Smith and Caroline Morton. They are all essential members of the team and lead on some of our most important work, for example, Helen and Lisa are responsible for our work on vaccine coverage; Anna led our initial investigations on hospital medicines data; Robin is working on methods for automated data validation; and Millie has been instrumental in the design and running of our co-piloting scheme. However, it is our aim to considerably increase the number of women and other gender identities in our team, and to do as much as we can to help encourage them to consider working in clinical research, data science, and software engineering. Data suggests that we might be particularly well-placed to do this. A survey by BCG in 2019, showed that 40% of STEM graduates in the UK view data science as being too abstract and low impact - a fact that was found to be a particular deterrent for women, 67% of whom expressed a clear preference for applied, impact-driven work, exactly the type of work that we do in the DataLab. With this in mind, we have been trying to be more proactive in our approach to recruiting more diverse candidates, for example:

  • We now publish the salary range of all jobs we have available
  • We offer preliminary, informal, and ‘off-the-record’ chats to anyone interested in applying for a role
  • We advertise our jobs on boards designed to reach a broader audience
  • We check all of our job adverts for gendered language
  • We actively support flexible and remote working
  • We are in the process of developing a paid internship programme
  • All our code and documentation is entirely open for others to learn from and re-use
  • We are planning to develop a range of teaching materials on a variety of technical topics
  • (COVID-permitting) we are planning a range of school outreach activities for this year.

This is, however, only the start and we would like to do more. So if you have any ideas about what we could do better, what you would like to see us doing more of in DataLab, or if you’d just like to chat to us to find out more about what it’s like working in applied data science then please - Get in touch!

 

 

Opinions expressed are those of the author/s and not of the University of Oxford. Readers' comments will be moderated - see our guidelines for further information.

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