Hi everyone, I’m Sara Conejo Cervantes. As well as being a full-time Maths and Computer Science student, I am a Fem Tech Entrepreneur and Spokeswoman for Gender and AI.
I am here to discuss how we can close the gender gap using technology. The reason I choose to discuss this issue, in particular, is because inequality between men and women is still a very real issue for women all over the world.
I will be unfolding examples of where gender bias has led us to move backwards in our fight for equality. I will be covering the further implications of these biases on our society and on women as well as what can be done about it and offering my personal solution on this issue.
So I want to encourage you all to take on board my solutions and go further than just acknowledging them.
Currently, only 20% of jobs in the tech industry are held by women of which 5% are in leadership roles. In AI 18% of researchers are women. At my university, 17% of the students in my year are female. But why are these numbers so low if women were at the forefront of software development in the 1940s?
To answer that question, let’s have a quick history lesson where I will cover just one reason. During the Second World War, hundreds of women were hired to solve calculations that helped the Allies win the war. Throughout the 1950s, computer software programming was seen as ‘women’s work’, the alternative to the male vocation of hardware development.
In the 1970s and early 1980s, the number of US women pursuing degrees in computer science grew to 37% which was nearly twice the number recorded in 2015. However, revolutionary software developments brought a gold rush to Silicon Valley and the focus for men shifted from hardware to software. The media also gave rise to the idea of the ‘male tech genius’ with its focus on Bill Gates and Steve Jobs, as well as Pop culture stereotyping what a “nerd” is with films such as Weird Science and Revenge of the Nerds. Women became severely objectified in these films which lead to the number of women working in technology to begin to drop.
Now it is our job to undo what has been done and encourage young girls to pursue careers in technology, in specific
Why is diversity so important in the tech industry? The answer is quite simple. Diverse teams build better products. Why is this the case? Well, as a company you want as many people as possible to use your product. These people are not all going to have the same needs. By diversifying teams with regards to gender, ethnicity, culture, sexual orientation, or age, it puts the team in a better position to understand what the users want. Each team member will be able to offer a different perspective and bring more to the table.
Here are 3 different examples where gender biases have occurred in tech products due to the lack of diversity.
The first two are related to human physique.
Voice recognition software is integrated into many of our day-to-day systems however it “hears” male voices more easily than female ones. Humans have different voice frequencies, women’s voices tend to be of a higher frequency than men’s. If this is not considered then it can lead to complications, such as when a car’s voice command system fails to understand what a female driver is saying, or when the notes that a female health care provider dictates about a patient’s health are riddled with errors. This is because AI assistants are trained on male-biased. (Invisible Women by Caroline Criado-Perez)
Another example is Apple’s “comprehensive” health app that you could use to track your copper intake before being able to track your menstrual cycle, which half the population would be inclined to use. In addition, the app also has a step counter which is most effective when the device is in the pocket or a smartwatch is worn. However, creators forgot that women usually don’t have pockets big enough to carry their phones on them at all times so the step tracker is less accurate on women. This issue occurs due to the lack of diversity in design thinking.
The final example is to do with the lack of diverse data. Google Translate assigns gender to certain professions when translating from languages which do not have grammatical gender to when it does. In this case, here is me translating this sentence from English to Spanish. The noun doctor has been decided to be male and the nurse to be female. Algorithms like this enforce gender stereotypes, playing a big part in gender inequality.
The issue with “Sophisticated” ChatBots
Why do most virtual assistants powered by AI, such as Siri and Alexa by default have female names, female voices and often have a submissive or even flirtatious style?
Saniya Gulser Corat, Unesco’s director for gender equality said in a statement “Obedient and obliging machines that pretend to be women are entering our homes, cars and offices. The world needs to pay much closer attention to how, when and whether A.I. technologies are gendered and, crucially, who is gendering them.” And I couldn’t agree more with this statement.
These devices are made with their humanized personalities to enforce generations of problematic perceptions of women. They are putting a stamp on society as they become commonly used in our homes across the world and can influence interactions with real women and men.
Changes are needed in education because bias is a symptom of systemic underrepresentation within a male-dominated field. This leads me to talk about…
How do we actually close the gender gap?
This is a process. Two main points I will make are:
- Diversifying data and making the data feminist. Its time to take action and take matters into our own hand. Analyse your data and question it before it even gets used.
- Work with organisations which are encouraging girls in STEM or hold your own event where you can inspire young girls to pursue careers in technology.
As a final remark continuously question yourself and the company you work for on whether you/they are doing your/their best to close the gender gap and educate yourself and the people around you on these issues, there are great articles, books and other sources which explain problems that men and women face due to gender inequality.