Gender Representation in AI – Part 2: Automating the Generation of Gender-Neutral Versions of Face Images

Experimenting with image classification through the gender lens

In the first part of our series we discussed a simple question: How would our looks change if we were to move images of us across the gender spectrum? Those experiments lead us the idea of creating gender neutral face images from existing photos. Is there a “mid-point” where we perceive ourselves as gender-neutral? And – importantly – at what point would an AI perceive a face as such?

Becoming more aware of technology we use daily

Image classification is an important topic. Technology advances daily and is employed in a myriad of applications – often without the user being aware of how the technology works. A current example is the Bold Glamour filter on TikTok. When applied on female-looking faces, facial features and amount of makeup change drastically. In contrast to this, male-looking faces change much less. This difference suggests that the data used to develop the AI behind the filters was unbalanced. The technology behind it is most likely based on GANs, like the one we explore in this article.

As a society of conscious citizens, all of us should have a grasp of the technology that makes this possible. To help establish more awareness we explore face image generation and classification through a gender lens. Rather than explore several steps along the spectrum, this time our aim is to generate gender-neutral versions of faces.

How to generate gender-neutral faces using StyleGAN

Utilizing a deep learning-based classifier for gender identification

To determine a point at which a face’s gender is considered neutral is anything but trivial. After relying on our own (of course not bias-free) interpretation of gender in faces, we quickly realized that we needed a more consistent and less subjective solution. As AI-specialists, we immediately thought of data-driven approaches. One such approach can be implemented using a deep learning-based image classifier.

These classifiers are usually trained on large datasets of labelled images to distinguish between given categories. In the case of face classification, categories like gender (usually only female and male) and ethnicity are commonly found in classifier implementations. In practice, such classifiers are often criticized for their potential for misuse and biases. Before discussing examples of those problems, we will first focus on our less critical application scenario. For our use-case, face classifiers allow us to fully automate the creation of gender-neutral face images. To achieve this, we can implement a solution in the following way:
We use a GAN-based approach to generate face images that look like a given input image and then use the latent directions of the GAN to move the image towards a more female or male appearance. You can find all a detailed exploration of this process in the first part of our series. Building on top of this approach, we want to focus on the usage of a binary gender classifier to fully automate the search of a gender-neutral appearance.

For that we use the classifier developed by Furkan Gulsen to guess the gender of the GAN-generated version of our input image. The classifier outputs a value between zero and one to represent the likelihood of the image depicting a female or male face respectively. This value tells us in which direction (more male or more female) to move to approach a more gender-neutral version of the image. After taking a small step in the identified direction we repeat the process until we get to a point at which the classifier can no longer confidently identify the face’s gender but deems both male and female genders equally likely.
Below you will find a set of image pairs that represent our results. On the left, the original image is shown. On the right, we see the gender-neutral version of our input image, that the classifier interpreted as equally likely to be male as female. We tried to repeat the experiment for members of different ethnicities and age groups.

Results: original input and AI-generated gender-neutral output

Are you curious how the code works or what you would look like? You can try out the code we used to generate these image pairs by going to this link. Just press on each play button one by one and wait until you see the green checkmark.

Image processing note: Image processing note: We used an existing GAN, image encoder, and face classifier to generate gender-neutral output. A detailed exploration of this process can be found here

Perceived gender-neutrality seems to be a result of mixed facial features

Above, we see the original portraits of people on the left and their gender-neutral counterpart – created by us – on the right. Subjectively, some feel more “neutral” than others. In several of the pictures, particularly stereotypical gender markers remain, such as makeup for the women and a square jawline for the men. Outputs we feel turned out rather convincing are images 2 and 4. Not only do these images feel more difficult to “trace back” to the original person, but it is also much harder to decide whether it looks more male or female. One could argue that the gender-neutral faces are a balanced toned-down mix of male and female facial features. For example, with image 2 when singling out and focusing on the gender-neutral version the eye and mouth area seems more female, while the jawline and face shape seem more male. In the gender-neutral version of image 3, the face alone may look quite neutral, but the short hair distracts from this, rendering the whole impression in the direction of male.

Training sets for image generation have been heavily criticized for not being representative of the existing population, especially regarding the underrepresentation of examples for different ethnicities and genders. Despite “cherry-picking” and a limited range of examples, we feel that our approach did not bring worse examples for women or non-white people in the results above.

Societal implications of such models

When talking about the topic of gender perception, we should not forget that people may feel they belong to a gender different from their biological sex. In this article, we use gender classification models and interpret the results. However, our judgements will likely differ from other peoples’ perception. This is an essential consideration in the implementation of such image classification models and one we must discuss as a society.

How can technology treat everybody equal?

A study by the Guardian found that images of females portrayed in the same situations as males are more likely to be considered racy by AI classification services offered by Microsoft, Google, and AWS. While the results of the investigation are shocking, they come as no surprise. For a classification algorithm to learn what constitutes sexually explicit content, a training set of image-label pairs must be created. Human labellers perform this task. They are influenced by their own societal bias, for example more quickly associating depictions women with sexuality. Moreover, criteria such as “raciness” are hard to quantify let alone define.

While these models may not explicitly be trained to discriminate between genders there is little doubt that they propagate undesirable biases against women originating from their training data. Similarly, societal biases that affect men can be passed on to AI models, too, resulting in discrimination against males. When applied to millions of online images of people, the issue of gender disparity is amplified.

Use in criminal law enforcement poses issues

Another scenario of misuse of image classification technology exists in the realm of law enforcement. Misclassification is problematic and proven prevalent in an article by The Independent. When Amazon’s Recognition software was used at the default 80% confidence level in a 2018 study, the software falsely matched 105 out of 1959 participants with mugshots of criminals. Seeing the issues with treatment of images depicting males and females above, one could imagine a disheartening scenario when judging actions of females in the public space. If men and women are judged differently for performing the same actions or being in the same positions, it would impact everybody’s right to equal treatment before the law. Bayerischer Rundfunk, a German media outlet, published an interactive page (only in German) where AI classification services’ differing classifications can be compared to one’s own assessment .

Using gender-neutral images to circumvent human bias

Besides the positive societal potentials of image classification, we also want to address some possible practical applications arising from being able to cover more than just two genders. An application that came to our minds is the use of “genderless” images to prevent human bias. Such a filter would imply losing individuality, so they would only be applicable in contexts where the benefit of reducing bias outweighs the cost of that loss.

Imagining a browser extension for the hiring process

HR screening could be an area where gender-neutral images may lead to less gender-based discrimination. Gone are the times of faceless job applications: if your LinkedIn profile has a profile picture it is 14 times more likely to get viewed. When examining candidate profiles, recruiters should ideally be free of subconscious, unintentional gender bias. Human nature prevents this. One could thus imagine a browser extension that generates a gender-neutral version of profile photos on professional social networking sites like LinkedIn or Xing. This could lead to more parity and neutrality in the hiring process, where only skills and character should count, and not one’s gender – or one’s looks for that matter (pretty privilege).

Conclusion

We set out to automatically generate gender-neutral versions from any input face image.

Our implementation indeed automates the creation of gender-neutral faces. We used an existing GAN, image encoder and face image classifier. Our experiments with real peoples’ portraits show that the approach works well in many cases and produces realistically looking face images that clearly resemble the input image while remaining gender neutral.

In some cases, we still found that the supposedly neutral images contain artifacts from technical glitches or still have their recognizable gender. Those limitations likely arise from the nature of the GANs latent space or the lack of artificially generated images in the classifiers training data. We are confident that further work can resolve most of those issues for real-world applications.

Society’s ability to have an informed discussion on advances in AI is crucial

Image classification has far-reaching consequences should be evaluated and discussed by society, not just a few experts. Any image classification service that is used to sort people into categories should be examined closely. What must be avoided is that members of society come to harm. Establishing responsible use of such systems, governance and constant evaluation are essential. An additional solution could be creating structures for the reasoning behind decisions using Explainable AI best practices to lay out why certain decisions were made. As a company in the field of AI, we at statworx look to our AI-principles as a guide.

Image Sources:

AdobeStock 210526825 – Wayhome Studio
AdobeStock 243124072 – Damir Khabirov
AdobeStock 387860637 – insta_photos
AdobeStock 395297652 – Nattakorn
AdobeStock 480057743 – Chris
AdobeStock 573362719 – Xavier Lorenzo

AdobeStock 546222209 – Rrose Selavy