Karl Benz invented the first automobile in 1886 but in his mind Karl had invented a Horseless Wagon, not knowing he had actually invented the modern automobile.
I relate to the idea that innovation is the result of exploration. While attempting to create a smarter UI for Magento 2 a few years ago, I began to experiment with the idea of using machine learning to train an algorithm to possibly solve gender-bias in shopping experiences.
What is Gender-Bias in Shopping Experiences?
I find the topic very fascinating as it fits with the current conversation of gender equality in the tech industry. Research shows that just over 1 in every 10 developers is a woman, and this demands a question to be asked: Are we indirectly influencing the design of user experiences with some gender-bias?
Let’s Pick on the Hamburger Menu
This minimalistic three-line icon was invented by Norman Cox – an interaction designer for the Xerox Star Interface in 1981. The name Hamburger was later coined by the same designer who stated during an interview, “I have to chuckle at all the attention that little hamburger symbol is getting lately”.
Even though the name Hamburger is playful – it seems heavily influenced by the inventor’s taste. Similar to the Rorschach test – where the subject’s perceptions of inkblots vary based on the subject’s psychological interpretation.
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Had Norman Cox been a vegetarian, this menu graphic could have possibly been called Celery Sticks?
Had Norman Cox been a Norma Cox – a female interaction designer – would this graphical icon have been referred to as something else besides a Hamburger?
If the way we rationalize the design of web interfaces is unintentionally influenced by the bias of the creator – we are not creating experiences for everyone, and that goes against the core nature of the internet.
How we solve this today?
Often, online commerce experiences try to mimic physical retail experiences. When it comes to crafting a custom experience for a guest user (non-authenticated user), the gender of the user is not the starting point for computing the elements needed to render a web page designed for the web user.
Personalization and recommendation engines fall short when they are not able to create a custom experience as they lack actionable insights from the user, and in order to provide personalization – these inputs must be provided before the actual personalization can take place. With today’s security breaches – many web users are not likely to provide their gender due to privacy concerns.
AI and machine learning can enhance these shopping experiences by deriving personalization and recommendation decisions without requesting input from the user and can do so in a more effective and elegant way.
“A web interface should not have to wait for user input to personalize a web experience, and such application should derive its own actionable insights without requesting input from the user in order to act.”
As part of my experimentation, TA Digital Magento Commerce Practice created a proof of concept for Magento 2 that leverages Python and open-source AI/ML libraries that allows the frontend application to sense if the user is a male or female.
By giving the system an actionable insight into the user’s gender from the start, e-commerce websites can leverage an algorithm to make quick decisions to present content to the user that is in line with their gender.
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