Research has shown that women thrive in leadership roles, ranking higher in 13 out of 19 competencies identified as the most important to overall leadership effectiveness, including ‘taking initiative’, ‘inspiring and motivating others’ and ‘championing change’. It’s unsurprising therefore that if a group includes more women, its collective intelligence rises.
If fostering gender equality in the workplace benefits not only women, but businesses too, what is your organisation doing to improve gender inclusivity?
Sometimes even the most well-intentioned organisations are guilty of unconscious bias against their female workforce, due in part to the prevalence of “Male Default Thinking.”
So, what is it and how can it be avoided?
If you were asked to picture a human being, what does that figure look like? It’s likely your brain will construct a representation of what ‘human’ means to you, and theories suggest that representation is likely to be male.
This unconscious bias is coined “Male Default Thinking”.
This theory suggests that western society has unconsciously constructed a world around the average man. This depiction has since been exaggerated by biased data collection – both historical and modern – that focuses purely on the typical lifestyle and body of this hypothetical demographic.
Perhaps the most notable example of Male Default Thinking is one you encounter in your daily life: the ‘little green man’ presented on traffic lights. When we see this symbol, we understand that all pedestrians can cross the road, not just males. Here, the ‘little green man’ – and the male gender as a whole – is not used as a representation of just the male gender, but a representation of humanity.
However, the implications of Male Default Thinking go beyond this innocuous example.
Biased data impacts the way technology, products, services, and processes have been designed, and can adversely impact women even when the creation is well-intentioned. A recent study by the University of California Berkeley found that, historically, clinical drug dose trails were conducted solely on male participants. A lack of female participants in the trials can lead to overmedication of women, increasing the likelihood of problematic side effects for the medication.
The danger of Male Default Thinking is evident in the evolution of workplace processes that are designed and constructed for the “hypothetical man” and then, at best, adapted for women.
This is what Amazon found during the launch of their new Artificial Intelligence (AI) driven recruitment system. Amazon’s AI was programmed to vet applicants by observing data and patterns in applications submitted over the last decade, the majority of which historically came from men. Their AI was therefore learning and reinforcing existing biases, in this case, against female candidates. Though the AI programme was retired once this flaw was realised, there is potential for similar issues to arise again given more than half of U.S. HR Managers feel artificial intelligence will be a regular part of their work within the next five years.
Human-led people management can also show high levels of Male Default Thinking: one recent study of sales workers at 214 firms found that women are often perceived to have lower ‘potential’ for promotions compared to men, even if their performance reviews were more favourable on average.
The layering effect of Male Default Thinking has a direct influence on women in the workforce. Research found that many women lack confidence in their ability to compete in fields that are historically dominated by men such as mathematics, technology and science. In addition, women are less likely to negotiate for raises, costing them as much as $1.5 million over the course of their careers.
So, what steps can an organisation take to move away from Male Default Thinking and in turn, foster a more equitable and profitable working environment?
1) Human-centred Design: Consider which areas of your business are automated or haven’t been updated recently – it may be beneficial to get a female perspective to account for possible gaps. Use personas to test out thinking and outcomes to evaluate whether unintended Male Default Thinking is embedded in your systems.
2) Unbiased data samples: Just as Amazon’s rogue AI was turning away strong potential candidates because of their gender, so too could your data sampling be accounting only for Default Male Thinking. As part of user testing, make sure you are accounting for data biases, and that these are explicitly written into test scripts for automated processes and apps.
3) Diverse live user groups: To test new processes and systems pre-launch, include team members from a cross section of levels, roles and functions in your live user groups, ensuring the entire workforce demographic is represented. This user group can then be repurposed for additional operational system testing which may have unintentionally embedded Male Default Thinking.