As part of our Data & AI Podcast, we recently interviewed three data professionals at Siemens Energy who are building Women+, a community to empower, connect and support women and minority genders working at the organisation:
The trio shared their insights on tackling gender bias in the data and AI space, what they have learned on their journey building this new initiative and what other data professionals should look out for.
Júlia shared a riddle that they use with audiences to highlight the depth of unconscious gender bias within us.
A father and son are in a car crash and the son is taken to hospital for an operation. A very prestigious surgeon is due to operate on him but on seeing him says, “I can’t operate on him, he’s my son!”. Who is the surgeon?
One response they received was that the son had two dads! People will sooner jump to the idea that the son has two dads than the possibility that the surgeon could be the mother.
Júlia went on to explain how this is due to the long-term lack of strong female role models in society as many just assume that certain roles are for men (e.g. engineers, surgeons etc.). Julia says she almost didn’t become an engineer because the stereotypes she heard sounded nothing like her (and, as she later found out, were not true at all).
In our conversation, they also highlighted how AI is trained on datasets that reflect these societal biases, including gender biases. This could potentially lead to AI-driven discrimination against women and other gender minorities.
Therefore, alongside the development of AI tools and technologies, it’s critical that we develop ways to manage the biases that are inherent to how these operate.
Just like in the technology sphere, social transformation is a team effort. It’s critical that you form alliances with other groups, movements and parts of the organisation.
Leadership support is critical. Within Siemens Energy, they are fortunate to have an equal gender representation across leadership roles in data, including Chief Data Officer Micheline Casey.
The team had massive support from Micheline who was the “fuel” for them to truly commit the project and build a community for representation. As Erika notes, this kind of top-down support is absolutely critical and “helps us to maintain our values and culture within the community”.
But broad alliances need to be made with all kinds of groups and communities both within and without. In the case of Women+, they connect with other grassroots communities within Siemens, such as Siemens Pride. Among the Women+ members exists a core of male allies while one of their founding members is also active in the wider women and data community.
No one group can do it alone, these diverse communities must combine their efforts and learnings to draw attention to inclusion, diversity, equality and sexism.
The Women+ team wanted to make sure the community wasn’t paying lip service, and was in fact helping the community as intended.
They used their big launch event—which was attended by a diverse crowd of over 300—to gather feedback from their community and inform initiatives moving forward. These responses have influenced how they will work, how they will communicate and the core themes they want to address with their content and events.
Exciting projects have emerged within the community including a global leadership event to connect women in data with other leaders within the company, especially around the impact of AI, and an internal podcast series that will interview internal leaders and female role models that have important stories to share.
Erika said projects like these “help them to raise their profile and connect people that wouldn’t otherwise have met”.
The consensus on the best approach to the work is to take small steps and not to try to change everything at once. If you can bring a smile and engage people in a way that matters, they will then start influencing others in turn.
It’s also critical to maintain momentum and energy over the long term. With community projects like these there can be a lot of positive energy at the beginning but many fail because of a lack of vulnerability in the leadership or the absence of a clear message to rally behind.
It’s all about resilience and keeping going, with regular cadence and achievable goals for your initiatives. At Siemens, they try to maintain the attention of their audience over time by producing engaging content and hearing from the community themselves on what it is that they need.
As Júlia noted in the podcast, the issue of gender bias is not just a fad or trend – the data is clear that it’s a critical issue. In the UK, for example, women make up only 20% of AI and data professionals and 18% of users across the largest online global data science platforms.
While some aspects of gender discrimination have been improved in society, there is still a huge amount of work to be done. Starting an initiative or community under these circumstances is not easy.
Sandra encourages people not to hesitate and just to step into truly being the change they want to see in the world. She shared an apt quote from Nelson Mandela: “Do not look the other way; do not hesitate. Recognise that the world is hungry for action, not words. Act with courage and vision”.
You just have to go for it. The starting point is always self-belief and carefully taking actions. In her words: “believe in yourself and believe that human beings are really good people.”
If you’re looking to explore this topic further, the Women+ team suggested some helpful resources:
Listen the episode on Addressing the Gender Bias in Data & AI from the Data & AI Podcast.
Listen to another episode of our podcast 'Women in Data: A Movement for Change'.
Join us at our Women in Data & AI Meetups in London by signing up to our Meetup page.
