Gender bias in AI: what can we do about it?

From helping non-coders build apps and virtual assistants to automating a huge range of processes, AI has brought immense transformations to tech. Currently, we’re seeing the industry use ChatGPT to help build tools. From keyword research to web scraping, content writing, and even image production - you name it, someone’s likely using ChatGPT to help automate it.

The tool is now available to everyone, however, while AI and its emerging evolutions offer up huge potential, in a culture where sexism is thriving, malicious outcomes darken this otherwise exciting space.

Underrepresentation makes for bias tech

Currently, there’s a big gap between the proportion of male to female, non-binary, gender-queer and gender non-conforming programmers. 91.88% of the AI workforce is made up of men, while 5.17% is female, and 1.67% fall into the categories of non-binary, gender-queer and gender non-conforming. Consequently, AI is largely being defined under the hands of a certain group, leading to the underrepresentation of others.

Gender bias is manifesting in the space as a result of this. Innovations in tech like deep fakes have long been used predominantly against women. Meanwhile, gender stereotyping is emulated in much generative art, AI text outputs, and voice recognition systems.

Examples of gender bias in AI include generative tech being more likely to generate images of men when prompted with the word ‘doctor’, and images of women when prompted with ‘nurse’.

With the lack of women/nonbinary/queer AI programmers, underrepresentation is widening in a pivotal moment for AI, where the foundations of its ecosystem are being defined. AI can be quite binary – and if models are trained using data that doesn’t recognize certain groups, this could lead to these groups feeling underrepresented.

Discrimination is also present due to the lack of data representing these groups. The problem isn’t just the gender imbalance of the people training AI, but also the actual data that exists on gender, and its potential to reinforce inequality. Not only is this resulting in discriminatory outcomes, it’s also not accommodating to the needs of underrepresented groups.

The healthcare industry is a great example of how this tech can jeopardize the well-being of women and other minority groups. As Caroline Criado Perez nicely describes in her book ‘Invisible Women: Exposing Data Bias in a World Designed for Men’, training models to conduct medical diagnosis use past medical data that has been generated focussing on male health characteristics.

This is due to the lack of health data that is currently available for women or other groups. The result for these underrepresented groups? Inadequate healthcare advice, misdiagnosis, and poor treatment.

How do we tackle bias in AI?

The tech industry needs to encourage more women, queer, and non-binary-identifying people to enter the field. What’s more, they need to start regulating any malpractices that discriminate or target certain groups. Initiatives could include helping these groups get into tech-related higher education and increasing their awareness of jobs available across AI.

Historically, these groups have been divided and conditioned into a binary model, where the typical image of a tech employee is male. As a consequence, this has been feeding the misconception that men are more mathematical and tech-savvy than other groups.

Although queer coders might be more likely to fall out of the binary, there is still not enough accuracy in the data from a representation point of view. This is expected since there might be fear or discomfort of queer people vocalizing their identity at the workspace, and hence less likely to design ‘queer friendly’ code in a system directed by a larger group.

AI evolution can be great for our society. However, when used in a myopic way, and undermining these minorities, the dangers may far outweigh the benefits. Any new tech has to be treated with consciousness and used intending to have a positive impact on the world. Companies that use AI systems should be open about the data sets and algorithms used to train these systems and should be willing to make changes if bias is detected.

Governance has become – and will continue to be – an incredible challenge in the next few years. The tech industry has a strong voice in influencing the direction of tech. Because of this, we must ensure we participate in decision-making, promote the importance of regulation and apply best practices for representation. This will be key in tackling discrimination and avoiding gender bias spills.

By promoting diversity in the AI workforce, designing inclusive AI systems, and being transparent about the data and algorithms being used, we can help shape a healthy, safe and nonbiased environment.