Male devs

Men dominate women developers, 80/20 percent in digital development teams. 

This imbalance is problematic in terms of design. Male users are unconsciously designed for in favor of women, not deliberately as all developers want to deliver great digital products, but by default.

Gender bias is a significant and pervasive issue in digital product development. This bias often results in products and services that overlook, underrepresent, or even harm women and gender-diverse individuals, impacting everything from user experience to fundamental safety and access to opportunities. 

Key Areas and Examples of Gender Bias

Gender bias seeps into digital products through various stages of development, from the initial design assumptions to data collection and algorithmic training. 

Algorithmic and Data Bias: AI and machine learning models are trained on vast datasets, which often reflect historical and societal biases.

Hiring Tools: Amazon's experimental AI recruitment tool was abandoned after it was found to penalize female candidates because it was trained on historical data where male applicants were predominantly hired.

Natural Language Processing: Language models often associate professions like "doctor" or "engineer" with men and "nurse" or "homemaker" with women, reinforcing stereotypes in generated content and translations.

Product Design and User Experience (UX): The lack of diversity in development teams (women make up only about 30% of the tech workforce and a smaller percentage of leadership) means products are often designed with a "default male" user in mind.

Health Apps: Apple's initial Health app failed to include menstrual cycle tracking, despite its importance to a large portion of the population.

Voice Assistants: Virtual assistants like Siri and Alexa often default to female voices and were initially programmed with flirtatious responses to sexual harassment, reinforcing harmful stereotypes of women in subservient roles.

Physical Safety and Health: Digital technology embedded in physical products also exhibits bias, with severe consequences.

Car Safety: Crash-test dummies have historically been based on the average male body, making women 73% more likely to be seriously injured in a car crash.

Medical Diagnostics: AI systems in healthcare, trained on male-dominated datasets, have led to higher rates of misdiagnosis for women in conditions like heart disease. 

Impact and Consequences

This ongoing bias leads to:

Reduced Opportunities: Biased algorithms can limit women's access to job opportunities, loans, and career advancement.

Poor User Experience: Products not designed for diverse needs result in a higher cognitive load, frustration, and lower satisfaction for female users.

Perpetuation of Stereotypes: Biased technology reinforces existing societal inequalities and can influence public perception of gender roles and capabilities. 

Solutions and Future Outlook

Addressing gender bias requires systemic change and a multi-faceted approach involving researchers, developers, and policymakers. Key steps include: 

Diverse Teams: Ensuring diverse perspectives in development teams helps spot biases early and leads to more inclusive products.

Inclusive Data: Training AI models on diverse and representative datasets that account for gender-specific needs and experiences.

Ethical Frameworks: Adopting strong ethical guidelines and conducting gender impact assessments during the design and development process.

User Feedback and Testing: Actively seeking feedback from a wide range of users and conducting thorough testing across demographics to identify and fix biase

This imbalance is problematic in terms of design. male users are unconsciously designed for in favor of women, not deliberately as all developers want to deliver digital products, but by default.

It's important to consider this aspect of designing for virtual spaces as we embrace the notion of creating the kind of safe virtual spaces as we would like to occupy in the real world. 

Given the opportunity and ability to design experiences that are physically and psychologically safe for women (and by default.. everyone) why wouldn't we? 

Why perpetuate poorly considered, badly designed and downright abusive and controlling experiences for women simply because we didnt think deeply enough... or were under deadline etc. 

We could make virtual spaces example of safe spaces fir women... and maybe acquire knowledge that could transfer into everyday lives of women (and all of us) making real lives safer spaces to be in.

Formalizing a set of design principles that consider both physical and psychological safety for all, starts with specifically recognising women's different attitudes to what 'safety' means, in order to design for it. 

Next, we will consider womens 'Safety load'.