Save Alexa: Take an Intersectional Approach
Have you ever wondered why so many robots, virtual assistants, and AI are programmed with feminine traits? Have you ever pondered, if there were more women behind the algorithms, how AI would be different — not just vocally and aesthetically, but functionally?
With Alexa, Cortana, Siri, Google Home, and most GPS systems defaulting to female, there has been plenty of speculation over the ubiquity of demure, ladylike tones emitted from the speakers of modern machines.
One reason? Research has demonstrated a greater affinity for female voices by men and women alike
, prompting big tech companies like Amazon to opt for “Alexa” over “Alexander.” Still, it’s impossible to ignore the gendered implications at play, even if such decisions are driven by consumer demand. Why is the female voice pleasing in this context to begin with?
AI itself has been found to learn sexist behavior by amplifying biases found in existing data — for example, stock photos of women in kitchens and men on fields not just to be mirrored by AI, but given a boost
, resulting in the misgendering of men due primarily to their proximity to cookings supplies.
A key factor in this conversation is that AI are still mostly created by men, and white men in particular
. Machines must necessarily take on values and function as dictated by their creators, whether these values are intentional or not. Assemble a group of men and ask them to brainstorm AI applications, and you’ll get entirely different results than you would from a group of women. That’s not because their talents are different, but because their lived experiences, priorities, and worldviews are.
The solution is at once obvious and elusive: We need a more intersectional approach to AI programming, brought about by the recruitment of more female engineers. But one does not simply turn cultural norms on their heads in one swift motion; we need to play the long game, encouraging women to get involved in this increasingly-vital field before it’s too late.
Heather Roff, an AI and global security researcher at Arizona State University, explained how smart algorithms that double down on gender stereotypes work against society in an article for Foreign Policy. If bias is coded into an application, it is likely to further condition men and women to conform to traditional roles and buy into all the products that come with them. Already, targeted ads provide a stark contrast on assumed values, with certain algorithms perpetuating the pay gap by targeting higher-paying jobs toward men
. It doesn’t take much imagination to envision how much worse this could get.
Fortunately, Li and other women are putting in the work now to see the payoff sooner rather than later. Along with Melinda Gates, Li has founded AI4All
, a nonprofit that works to create pipelines for underrepresented talent through education, mentorship, and early exposure to AI’s potential for social good.
Other prominent women in AI, long viewed as token in their field, have organized regular conventions to realize their strength in numbers and work toward common goals. “Women in AI
” is a group of international AI and machine learning experts who also happen to be women. Their mission is to help improve diversity and close the gender gap in AI while helping companies and events source more female experts in the field.
Assuming these programs are successful, what would change? How would a world with more equity behind AI algorithms look different than the one we’re seeing unfold now?
According to Kriti Sharma
, Sage VP of AI and Bots, “The biggest hurdle standing in the way if making AI a transformative and productivity-enhancing revolution for all is the danger of building machines that don’t represent the entire human race.” Her company created a dedicated AI code of ethics
to guide businesses working with human-facing AI.
Machines that do represent all genders and races, then, would be infinitely more likely to service us all equally as we blaze forward into an uncharted future. This will be especially important as AI further penetrates fields like healthcare, government, and education, impactful and formative spaces that have gendered issues of their own to resolve.
Solutions to patriarchy-induced struggles for equal pay, rights, and opportunity may also be on the horizon, should AI be stopped from propping up capitalistic pursuits at the expense of equality.
All in all, the gender of a personal assistant’s voice may be the least of potential concerns, but the kind of thinking that perpetuates women as the product and men the builders is the kind that needs fixing. When women earn representation in this field, the emerging cycle of amplified bias in AI will not only be short-circuited, but reversed.
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