The Hidden Gender Bias in Smartphones Development

Gender bias in smartphones is something that most of us might fail to recognize. Even though we carry them every day and rely on them for everything from work to wellness. However, smartphones are often built with male-centric data and design assumptions.

It may be the screen size or AI voice recognition, but everything is baked into biased datasets and beliefs. Before diving into this article, I want to share a famous quote by Melvin Kranzbery. He said that:

“Technology is neither good nor bad—nor is it neutral.”

Now, in this article, you will learn the hidden gender disparities in smartphone development in an age of equality-driven innovation. 

Understanding Gender Bias in Tech Design

1. Define Gender Bias in Product and Tech Design

Gender bias in tech design refers to a digital product or system that ends up giving preference to one gender, usually men, over other genders. These preferences can occur due to the data that is male-dominated or due to social and cultural patterns that, in one or another, prefer men. 

It is worth mentioning here that biasness that prefer one gender over the other aren’t always conscious. Most of the time, developers and programmers are unaware that the datasets they are using are biased. 

Such datasets revolve around male bodies, habits, and behaviors, and developing products on such datasets can lead to gender discrimination in the long run.

Read more: How Gender Bias in Product Design Shapes Artificial Intelligence Development

Gender Bias in Smartphones: The Anatomy

1. Gender Bias in Smartphone Design

In this digital era, everyone uses smartphones, and modern smartphones usually fit in men’s hands. Most of the flagship phones today are designed with larger screens and bulkier frames, accommodating male hand sizes. 

Girls and women reading this post will agree that they struggle to use such phones with one hand. This is because the average female hand is around an inch shorter and narrower. This makes it difficult to reach all corners of the screen without stretching your fingers. And most of the stretching is useless, and you have to use both hands.

Digital watches also have screens almost the size of a woman’s wrist. There are smaller and more ergonomic models, but they’re often branded as “budget” or “lite,” which feels like an afterthought.

Similarly, fingerprint scanners on smartphones are placed in a position that is ideal for larger hands. Users with smaller hands often find it harder to use those smartphones comfortably or reliably.

2. Biases in Smartphone Interface Design

Gender bias in smartphone software designs also leans towards men’s choices. UI color palettes are in darker tones and portray minimalism. These are the styles that have been historically tested better with men in tech.

Iconography is designed with aggressive angular shapes or features, prioritizing performance modes over health settings. Such preferences signal who the product is ”really“ for. 

Things don’t stop here; those deep-in-the-menu accessibility options often hide the very features that marginalized users need. Such as older or non-binary people might actually need them upfront.

Moreover, some UX designs are also inclined towards certain genders. For instance, a wallet is assumed to be used by male users. Whereas a shopping app or feature is designed around female choices. These differences perpetuate gender stereotypes and alienate users who don’t relate to those roles.

3. Health App Features Often Optimized Male Physiology

It is generally assumed that health and fitness apps are only used by men. Hence, such apps talk about heart rate and muscle gain. They rarely focus on cycle tracking tools, hormonal health insights, or data tailored to female metabolism.

Many default health tracking apps use algorithms that are male-dominant. These algorithms use baselines for things like calories burned or heart rate variability. With such examples, women and gender-diverse users often get skewed results. 

Such biases in health apps can really mess with an individual’s health goals or even the overall medical data.

4. Biases in Facial Recognition Features

Facial recognition technology has historically struggled with darker skin tones and more feminine facial features. Especially if a woman is wearing makeup or a headscarf. Moreover, this recognition has also failed if gender-nonconforming traits are involved.

This gender bias in smartphones is because many training datasets are light-skinned and male-dominated. The algorithms of facial recognition suffer from biases that can’t be overlooked. These biases disproportionately affect certain genders and demographics. 

This results in slower unlock times, failed logins, and exclusion from facial ID from systems that claim to be secure and inclusive.

Data Collection and Testing Bias in Smartphones

1. Predominance of Male Data 

The data available to most of the tech companies is predominantly male. While tech companies are still stuck in the habit of collecting data that reflects just one kind of user. In biometric and usability testing, that “user” is overwhelmingly male.

For instance, the fingerprint scanners are calibrated to thicker male fingerprints. Women with longer nails often struggle with fingerprint recognition. Similarly, the heart rate sensors are optimized for male skin. All the data is skewed towards a single gender. 

2. Voice Assistants Trained on Male-Coded Language

Even though voice assistants have a female voice but they are programmed in a way that men like to talk. This is because these AI assistants are programmed by men coders. 

Many early voice assistants were trained on male speech patterns and sentence structures. Therefore, higher-pitched tones and softer commands, mostly used by women and some non-binary people, either received no response or the wrong response. 

For example, commands as simple as “Could you please set an alarm?” didn’t get registered.

3. Limited User Personas Excluding Non-binary or Disabled Users

Gender bias in smartphones hides in the approach for whom the technology is intended to be designed in the first place. In the designing phase, user personas target men around 30 years of age, who’s a software engineer and who loves gaming.

These assumed users are dominantly white males with able bodies, leaving out everyone else. Women, non-binary people, and users with disabilities aren’t part of the imagined user base. Consequently, their needs don’t even make it onto the roadmap.

When a group of users is not considered, their needs will never make it onto the roadmap. This is a blind spot that only increases discrimination and inequalities.

4. Gender Gaps in A/B Testing and Beta Programs

Another area that reinforces gender bias in smartphones is the A/B testing. In this phase, usually, the group doing the testing lacks diversity. A layout, a color scheme, or even a feature is tested with groups that represent one group, i.e., males. 

Unfortunately, most A/B testing pools and beta programs are still heavily male-dominated because they are often pulled from existing user bases. Therefore, the results are automatically skewed. 

So, the tests depicting users’ preferences do not represent all users but only a slice of the population that doesn’t reflect the whole picture. This creates a feedback loop with data that favours only one group. As a result, features that could have helped marginalized users are cut, simply because they didn’t “test well.”

Concluding Remarks

Smartphones have become extensions of ourselves—yet they’re still designed with one kind of user in mind. Gender bias in smartphones isn’t something we’d be discussing when gender equality is being pushed in every sphere of life.

As we talk about more ethical and inclusive tech, gender-neutral smartphones are not just a trend—they’re a necessity. By addressing bias in design and data, we pave the way for a future where tech truly belongs to everyone. If you are ready to demand better, start by choosing better and advocating for brands that build inclusively.